1. UNIVERSITY OF SOUTHAMPTON
FACULTY OF ENGINEERING AND THE ENVIRONMENT
INSTITUTE OF SOUND AND VIBRATION RESEARCH
Removing Movement Artefact from ECG
Signals Recorded from Human Subjects
by
Shuokai Pan
Supervisor: Dr D. Simpson & Dr E. Rustighi
Thesis for MSc Sound and Vibration
December 2013
2.
3. i
Acknowledgements
They say that success is ten percent inspiration and ninety percent perspiration. Hence
I would like to express my outmost gratitude to my friends, colleagues and
supervisors, Dr David Simpson and Dr Emiliano Rustighi, for giving me both. Their
extraordinary ability to make complex problems perspicuous has helped me many
times during the MSc project. I also appreciate their patience with me, and that they
spent many hours of their valuable time for correcting my English mistakes.
I am also grateful to all my classmates at ISVR. Unfortunately, the list of names is too
long to be given here. Thank you for creating a pleasant and stimulating work
environment. I particularly enjoyed the cultural diversity and the feeling of an
international family atmosphere.
Finally, I would like to express my deep gratitude to my wife, Anna Deng, for your
encouragement, understanding, patience and joy you bring. I owe my parents,
Shehong Pan and Chunxi Ma, for their sacrifice and endless support during both bad
times and good times and for teaching me the intrinsic value of education.
ShuokaiPan
Southampton December, 2013
4. ii
Contents
Abstract........................................................................................................................- 1 -
Chapter 1 Introduction....................................................................................................- 2 -
1.1 Motivation ........................................................................................................- 2 -
1.2 Objectives of the Project.....................................................................................- 3 -
1.3 Thesis Organization ...........................................................................................- 4 -
1.4 Main Contributions............................................................................................- 4 -
Chapter 2......................................................................................................................- 5 -
AReview of ECG Basics and Artefact..............................................................................- 5 -
2.1 The heart..........................................................................................................- 5 -
2.1.1 Anatomy and Physiology of the Heart.........................................................- 5 -
2.1.2 Electrical Conduction System of the Heart ..................................................- 6 -
2.2 ECG Measurement and Morphology ....................................................................- 7 -
2.2.1 ECG Signal Acquisition ............................................................................- 7 -
2.2.2 ECG Morphology ....................................................................................- 9 -
2.3 Artefact in ECG............................................................................................... - 10 -
2.4 Origins of Motion Artefact................................................................................ - 11 -
2.4.1 Skin Anatomy and Skin Potential ............................................................. - 12 -
Chapter 3.................................................................................................................... - 15 -
AReview of Adaptive Filter Theories and its Potential for Motion Artefact Reduction.......... - 15 -
3.1 Introduction .................................................................................................... - 15 -
3.2 Adaptive Filter Theories ................................................................................... - 15 -
3.2.1 Linear Adaptive Filters ........................................................................... - 17 -
3.2.2 Nonlinear Adaptive Filters ...................................................................... - 20 -
3.3 Areview of Adaptive Motion Artefact Reduction in ECG signals........................... - 23 -
Chapter 4.................................................................................................................... - 27 -
ECG Signal Acquisition and Motion Artefact Generation .................................................. - 27 -
4.1 Introduction .................................................................................................... - 27 -
4.2 Methods......................................................................................................... - 28 -
4.2.1 Equipment Set Up.................................................................................. - 28 -
4.2.2 Method of Stimulus Signal Generation ..................................................... - 30 -
4.2.3 Equipment Calibration ............................................................................ - 32 -
4.2.4 Experimental Procedure.......................................................................... - 36 -
Chapter 5.................................................................................................................... - 39 -
Application of Adaptive Filters to ECG Motion Artefact Reduction.................................... - 39 -
5.1 Introduction .................................................................................................... - 39 -
5.2 Methods......................................................................................................... - 40 -
5.2.1 Signal Preprocessing .............................................................................. - 40 -
6. - 1 -
Abstract
Cardiovascular disease (CVD) is a major cause of death in many regions of the world.
The electrocardiogram (ECG), being the most widely used approach, provides an
effective way to monitor heart conditions and to diagnose heart dysfunctions.
Traditional equipment seriously limits the daily routine of the patients while wearable
systems offer convenient solutions to long term assessment of cardiac health and
therapy effects. However, one of the major problems of such ambulatory ECG
recordings is the presence of artefact and motion artefact are regarded as the most
disturbing source as they not only overlap in the frequency domain with the ECG
signal but also resemble important features of ECG waveform.
One promising direction of motion artefact reduction is the use of adaptive methods
which โcleanโ the noisy signals by subtracting an estimate of movement interference
from additional channels of reference recordings. Previous studies have employed
electrode deformation or acceleration, skin/electrode impedance, skin strain and
half-cell potential as reference inputs, but most of them referred to linear filter
structures and none was very successful in decreasing the motion artefact in a
reproducible and consistent manner. This could be attributed to the lack of correlation
between the reference signal and the induced noise.
The major contributions of this study are the incorporation of nonlinear cascade
models, the comparison of different evaluation standards and optimizations of filter
orders. The types of motions introduced were controlled vibration of the shaker and
voluntary motions of the subjects while the reference signals were either single axis
acceleration of the shaker or trial axial accelerations of the subjectโs right hand. The
performance of each structure was evaluated using NCC (Normalized Cross
Correlation Coefficient), PAR (Percentage Artefact Reduction) and AIC (Akaike
Information Criterion) criteria. From the results obtained in this study, it can be
concluded that the linear and nonlinear adaptive methods investigated in this study are
most effective in reducing artefact generated through controlled motions but less
effective to those induced voluntarily. Such poor effectiveness of adaptive methods
could result from other sources of disturbances or the insufficiency of the nonlinear
models investigated. Possible directions for future work are listed in the end.
7. - 2 -
Chapter 1 Introduction
1.1 Motivation
Being one of primary causes of death in many parts of the world and costing billions
of dollars every year according to American Heart Association [20], cardiovascular
diseases (CVD) necessitate the advance and research for their early, accurate and
cost-effective diagnosis and assessment. One of the techniques that is widely used to
monitor heart health is the electrocardiogram (ECG), which is the recording of the
electrical activity of the heart obtained from measuring the electrical potential
between specific sites on the skin using electrodes. Although current medical
equipment can provide precise information about the heart conditions, traditional
ECG acquisition approaches are limited in use when patientsโ movements are required.
This can be a burden to both the medical staff and patients when frequent, long
periods or exercise recordings are required for continuous CVD monitoring, treatment
or research.
Recent advances in electronics, computational power and signal processing
techniques have revolutionized the concept of ECG monitoring. A variety of
integrated wearable systems such as iRhythm [64] and Corventis [65] have been
introduced in an attempt to enable the continuous, reliable and long-term
cardiovascular recording without interfering with the patientsโ daily routines. Despite
these improvements in instrumentation, the signal quality obtained in ambulatory
conditions continues to be plagued by high levels of noise induced by wearersโ
movements, known as motion/movement artefact. For instance, in Holter monitoring
or stress tests, motion artefact often lead to difficult interpretation of ECG signals since
it can resemble the morphologies of the P, QRS and T waves. For event recorders or
detection devices, identifying and compensating motion artefact is crucial for the
accurate operation of the devices as they may lead to false alarms and wrong event
detections [1]. Reducing the motion artefact would considerably extend the
applicability of ambulatory monitors to situation of higher activity, as usually
encountered in non-controlled daily-life situations. [1]
What make the motion artefact particularly troublesome are the facts that their spectra
completely overlap that of the ECG and their characteristics are constantly changing
during different movements [2]. Therefore, digital filters with fixed specifications are
not appropriate for motion artefact reduction since no determined prior knowledge of
the operation environment is available for the systems parameters. Over the past
decades, numerous more advanced methods have been proposed and investigated in
literature for interference cancellation in ECG signals. From the perspective of
underlying principles, they can be divided into two major categories: non-signal
processing approaches and signal processing approaches. Non-signal processing
8. - 3 -
approaches usually involve modifying the electrodes configurations or placement and
careful preparation practice such as abrasion of the skin [25] and skin puncture [26].
These methods can be regarded as preventing the motion artefact from being
generated, but have a number of practical limitations or drawbacks under ambulatory
conditions. Signal processing methods, by contrast, aim at reducing the interference
through mathematical manipulations such as discrete time wavelet transform [36],
independent component analysis [37] and adaptive filtering [40], each of which has its
respective desirable properties. All of these approaches have been shown to produce
varying but promising results. Due to constraints in time, the current project will
focus on adaptive filtering techniques, building on previous contributions from former
ISVR students [3, 4, 62].
In addition to the motion artefact, the ECG signals may be corrupted by various other
sources of noise like power line interference, baseline wander, noise from other
bioelectric signals (especially electromyography - EMG) and noise arising from the
instrumentation itself (amplifiers, filters). These are covered in chapter 2 for
completeness but their removal is not included in this project since our concern is the
movement noise reduction. From this point onward, โartefactโ will be used to refer
exclusively to motion artefact.
1.2 Objectives of the Project
The objectives of this study can be summarized as,
1. To investigate the coupling between acceleration recorded at the electrodes and the
recorded ECG signal.
2. To refine previously proposed linear adaptive filtering algorithms and extend these
to nonlinear cases for movement artefact reduction and validate them on simulated
and recorded signals.
3. To investigate objective measures for evaluating the effectiveness of linear and
nonlinear adaptive filtering algorithms and compare their performances.
This project builds upon previous work by former ISVR students and its primary
extension will be the experiments with nonlinear adaptive filters. The key assumption
underlying the previously studied linear adaptive filters is the linear correlation
between motion acceleration and the artefact induced. Former results have shown that
this hypothesis can lead to very notable reduction in artifact in some cases, but the
remaining amount of noise may indicate a certain degree of nonlinearity. Including
nonlinear adaptive filtering algorithms allows for investigation of this nonlinear
possibility, comparison with linear models and better selection of algorithms for
practical applications. This will be the main contribution of this study.
9. - 4 -
1.3 Thesis Organization
The contents of this thesis are organized as follows. Chapter 1 introduces the
background information and major motivations for carrying out this study. It also
describes the original contribution that will be made after the completion. Chapter 2
provides a brief overview of the heart physiology, electrocardiogram and types of
artefact which might corrupt the ECG signals. Possible physical models for the
generation of motion artefact are specifically discussed. Basic theories of linear and
nonlinear adaptive filters are introduced in Chapter 3. The methodologies of
generating motion artefact and how the corrupted ECGs of each subject were recorded
experimentally are described in Chapter 4. Details of how each set of recordings
were processed and representative results were explained and demonstrated in
Chapter 5, which forms the most important part of this thesis. Finally, discussions,
conclusions and suggestions for future work are presented in Chapter 6.
1.4 Main Contributions
The major contributions from this thesis are:
1. Critically reviewed and discussed various quantitative measures for performance
evaluation. The filter orders were optimized before adaptive filtering using selected
criteria.
2. Validated the effectiveness of LMS and RLS adaptive filters for motion artifact
reduction through computer simulations.
3. Validated the performances of single input and tri-input RLS algorithms using
experimentally recorded ECGs.
4. Extended previous studies on linear adaptive methods using three nonlinear cascade
models and determined the LN model to be the most suitable one for most situations
investigated.
10. - 5 -
Chapter 2
A Review of ECG Basics and Artefact
The ECG is perhaps the most commonly known, recognized and used biomedical
signal in clinical practice whose origin dates back to more than100 years. It is the
manifestation of the electrical activity of the heart, and can be recorded fairly easily
with surface electrodes on the limbs or chest. Not only can the rhythm of the heart
be estimated by counting the readily identifiable waves, but, more importantly, the
fact that the ECG waveshape is altered by cardiovascular diseases and abnormalities
such as myocardial ischemia and infraction, ventricular hypertrophy and conduction
problems means that it is very important in the diagnosis and treatment evaluation of
these dysfunctions [5].
2.1 The heart
2.1.1 Anatomy and Physiology of the Heart
The human heart is shaped like a cone, weighs 250- 350 grams and is approximately
equal to the size of the fist [6]. It is located anterior to the vertebral column and
posterior to the sternum. It is composed of three layers of tissues: Myocardium,
Epicardium, Endocardium. The myocardium is the thick main layer of the heart
muscle, made up predominantly of cells called myocytes and responsible for pumping
blood. Its outside surface is covered by a thin, glossy membrane called the epicardium.
Another smooth glossy membrane, the endocardium, covers the inside surfaces of the
heartโs 4 chambers, the valves, and the muscles that attach to those valves [7].
The human heart is a four chamber dual pump with two atria for collection of blood
and two ventricles for pumping out of blood. Figure 2.1-A shows a cross section of
the four chambers, direction of blood flow and the major vessels connected to the
heart while Figure 2.1-B reveals its function in the blood circulatory system. The
resting or filling phase of a cardiac cycle is called diastole; the contracting or pumping
phase is called systole.
The right atrium (RA) receives blood from the superior and inferior vena cavae, blood
which has delivered oxygen and other nutrients and picked up waste products
(including carbon dioxide โ CO2) from all parts of the body. During atrial contraction,
blood is passed from the right atrium to the right ventricle (RV) through the tricuspid
valve. During ventricle systole, the impure blood in the right ventricle is pumped out
through the pulmonary valve to the lungs to pick up oxygen and release CO2. The left
atrium (LA) collects blood from the lungs, which is passed on during atrial
11. - 6 -
contraction to the left ventricle (LV) via the mitral valve. The left ventricle is the
largest and the most important cardiac chamber. It contracts the strongest as it has to
pump out the oxygenated blood through the aortic valve and the aorta against the
pressure of the rest of the vascular system of the body where gasses (oxygen, carbon
dioxide, etc.), nutrients and waste products are again exchanged [5].
(A) [9] (B) [10]
Figure (2.1) Anatomy of the heart and its function in humanโs circulatory system
2.1.2 Electrical Conduction System of the Heart
The rhythmic contractile activity of the heart is never possible without its unique and
specialized electrical excitation and conduction systems, as shown in Figure (2.2),
(A) [11] (B) [12]
Figure (2.2) Electrical Conduction System of the Heart.
Unlike skeletal muscle which is triggered directly from the nervous system, the
contractions of the heart muscle are initiated internally, by its own natural, intrinsic
pacemakerโthe Sinoatrial node (SA node) and are only speeded up or slowed down
by the delivery of neurotransmitters from the nervous system. The electrical impulses
generated by the SA node excite its own train of action potentials through the
processes of depolarization and repolarization. These action potentials then propagate
12. - 7 -
through the atrial musculature at comparatively slow rates, causing slow-moving
depolarization of the atria. When the electrical stimulus arrives at the Atrioventricular
node, the only conduction pathway between the atria and the ventricles, it passes
through the bundle of His, diverges into the right and left bundle branches, spreads
down to the apex of the heart and curve back up the sides through the Purkinje Fibers,
eventually causing rapid contraction of the ventricles. A small portion of this electrical
potential can be recorded at the body surface. By applying electrodes on the skin at the
selected points, the electrical potential generated by this current can be recorded as an
ECG signal [13].
2.2 ECG Measurementand Morphology
2.2.1 ECG Signal Acquisition
The worldโs first practical electrocardiography was pioneered by a Dutch doctor and
physiologist Willem Einthoven. His machine was sensitive enough to reliably
measure electrical potential differences between two limbs placed in two separate jars
filled with salt water. Plotting the values measured versus time gave a picture of the
electrical activity of the human heart. After examining a variety of combinations of
limbs and other regions of the body, Einthoven and his contemporaries found that the
ECG machine only captured one dimension of the three dimensional electricity
fluctuation of the heart. In other words, each of these combinations represented a
different projection of the time-varying vector generating the electrical field within
the heart. Hence one of such placements is called a lead (which does NOT refer to the
wires used!). Since some leads gave better views of the heart than other, based upon
the numerous experiments performed, Einthoven eventually established three standard
"limb leads" as listed in Table 2-1[14]. The value of each lead is calculated by
subtracting the electrical potential on negative electrode from that on positive
electrode.
Table 2-1 Three standard limb leads
Lead name Negative electrode Positive electrode
Lead I Right arm Left arm
Lead II Right arm Left leg
Lead III Left arm Left leg
These three common limb leads are often shown by a diagram called "Einthoven's
triangle", (Figure 2.3-A). Sometimes the electrodes are not placed on the limb but
instead on almost-equivalent parts of the torso for convenience [14] as shown in
Figure 2.3-B,
13. - 8 -
(A) Limb leads on the limbs [15] (B) Limb leads on the torso [16]
Figure (2.3) Einthoven's triangle
Figure 2.5 shows a complete representation for the modern standard 12-channel ECG
electrode placement. Except the above limb leads, this widely adopted configuration
incorporates three additional augmented limb leads known as aVR, aVL and aVF(aV
for augmented lead, R for the right arm, L for the left arm and F for the left foot) and
six chest leads, all referencing the Wilsonโs central terminal[5] (the center of the
triangle in Figure 2.5-A. The conducting jars are replaced with electrodes which are
typically paste filled metal, silver/silver chloride(Ag/Agcl) electrodes attached to the
skin using adhesive pads. A typical example of disposable ECG electrode is shown in
Figure 2.4,
Figure (2.4) Disposable ECG electrode [17]
The electrical potentials acquired from those electrodes are then transmitted to an
ECG amplifier, where the signals are amplified, filtered and usually analogue to
digital converted for heart monitoring, and/or automatic or manual clinical assessment,
as part of the diagnosis of any dysfunction. Note that even though modern
electrocardiography records "12 channels", there are only ten electrodes on the body.
These include 9 recording wires (3 on the limbs [right arm, left arm, and left leg] plus
6 on the chest over various regions surrounding the heart) and a 10th neutral or
ground wire attached to the right leg. The specific values of the three limb leads and
augmented limb leads can be derived from the calculation process expressed in Figure
14. - 9 -
2.5-A and the chest leads are obtained by subtracting the electrical potential at the
Wilsonโs central terminal [5] from those measured by the six chest electrodes.
(A)Limb leads [18] (B) Chest leads [19]
Figure (2.5) 12 leads ECG electrodes placement
2.2.2 ECG Morphology
A typical ECG waveform corresponding to two cardiac cycles is shown in Figure 2.6.
It consists of a series of separate waves (P wave, QRS complex and T wave)
corresponding to different periods of the electrical activity of the heart. The P wave is
produced by atrial depolarization caused by the electrical impulse traveling from the
SA node towards to the AV node. The QRS complex (which is a combination of Q, R
and S waves) represents the rapid depolarization of both ventricles and the largest
amplitude results from their largest muscle mass. The subsequent T wave is produced
by ventricular repolarization. The significant features of the ECG signal are the
individual waveforms and their durations such as the P-R, S-T, QRS and Q-T intervals
[20]. The amplitude of the ECG is normally 0.1 to 3 mV, with a frequency range of
0.05 to 100 Hz [21]. Although ECG signals have similar morphology, it should also
be noted that each patientโs ECG is unique. The patientโs physical size, skin type, age,
and pathology as well as where the electrodes are placed, influence the results of the
tracing.
Figure (2.6) Typical ECG waveform [8]
15. - 10 -
Figure 2.7 shows the typical outputs of a standard 12-lead ECG acquisition system. It
can be observed that the waveshape of each channel changes from one to another. A
well trained cardiologist will be able to deduce the 3D orientation of the cardiac
electrical vector by analyzing the waveshapes in the six limb leads. Cardiac defects, if
any, may also be localized by analyzing six chest leads [5].
Figure (2.7) Typical 12-lead ECG waveform [23]
2.3 Artefact in ECG
The ECG measurement environment is never perfect, the heartโs commonly small
electrical signals can be affected by a number of disturbances that are of similar
frequency and often larger amplitudes. These types of artefact can seriously degrade
the signal quality and frequency resolution or mask important features necessary for
accurate clinical monitoring and diagnosis. Major sources of artefact are:
A) Movement Artefact
During long term mobile or ambulatory ECG monitoring like the Holter Recording
where patientsโ daily routine is not interfered with, body movements or exercise can
introduce distortion to the recorded ECG, known as motion artefact. Up to now, there
is still no unanimous definitive physical model for this effect, only empirical models
have been proposed to illustrate possible mechanisms for its generation [31, 32].
Previous research on the likely origins of motion artefact are summarized in section
2.4. Motion artefact shares the same frequency spectrum as that of ECG signals and
its shape can be very similar to the ECG wave events. It is also of time-varying nature.
Therefore, it is particularly difficult to reliably detect and remove this artifact only
using the ECG signal or conventional fixed parameter filters. Novel signal processing
approaches such as adaptive filtering are thus studied extensively in this project.
B) Electromyographic Artefact (EMG artefact)
Changes of electrical potentials (EMG) caused by muscle activities (not heart muscles)
16. - 11 -
in the vicinity of electrodes might also be picked up during ECG acquisition period,
contaminating the ECG morphology [6]. The EMG signals appear on the monitor as
narrow, rapid spikes associated with muscle movement, being able to render the ECG
recording completely useless [24]. Despite the fact that these signals mostly resides in
a frequency range higher than that of the ECG signal and can be sufficiently
suppressed from the trace though fixed filters, modern ECG measurement practice
still carefully chooses electrodes positions where muscular interference is negligible.
C) Power Line Interference
Power line interference comes from the feeding lines of measurement systems and can
be either 50Hz or 60Hz depending on different countries. It occurs through two
mechanisms: capacitive and inductive coupling [6]. Capacitive coupling refers to the
transfer of energy between two circuits by means of a coupling capacitance present
between them, while inductive coupling is caused by mutual inductance. Typically,
capacitive coupling produces high frequency artefact while inductive coupling
introduces low frequency noise. For this reason inductive coupling is the dominant
mechanism for electrical interference in ECG monitoring. With proper screening, its
effect can be decreased significantly but not eliminated.
D) Electrode Contact Noise
Electrode contact noise is caused by variations in the position of the heart with respect
to the electrodes and changes in the propagation medium between the heart and the
electrodes [6]. Such variability is usually due to improper contact of the electrodes
which interrupts for a short period the connection between patient and measuring
system. The loss of contact can be permanent or intermittent, as in case when a loose
electrode is brought in and out of connection with the patientโs skin [24]. This
switching action at the input of measuring apparatus causes sudden changes in the
amplitude of the ECG signal, which can be as high as the maximal recorder output
with duration of a few seconds. In addition, poor conductivity between the electrodes
and the skin reduces the amplitude of the ECG signal and increases the probability of
disturbances [6].
E) Instrument Noise
The electrical equipment used in ECG measurements also contributes noise. This is
mainly from the electrode probes, cables, the amplifier and the analog-to-digital
converter [6]. Unfortunately instrumentation noise cannot be eliminated as it is inherent
in electronic components, but it can be reduced through more advanced equipment and
careful circuit design.
2.4 Origins of MotionArtefact
The origins of motion artefact have been studied by many researchers, the most
widely accepted explanation comes from Tam and Webster [25] concluding that the
changes in skin potential caused by skin deformation was the major source of motion
17. - 12 -
artifact. The electrode/paste interface did not introduce motion artefact of any
significance when recessed Ag-AgCl electrodes were used. One year later, Burbank
and Webster [26] studied the properties of skin stretch artefact in relation with skin
stretch history, stretch force, stretch time and electrolyte concentration. It was
quantified that skin stretch induced artifact in biopotential recording can reach up to
17 mV when the skin strain is 6%. They also further demonstrated that motion
artefact resulted largely from potential generator within the skin and changes in skin
impedance only played a minor role. Odman and Oberg [27] researched movement-
induced potentials in various electrode placements and found that potentials generated
by skin deformation beneath the electrodes dominate the disturbance pattern in ECG
recording. The streaming potential created by gel movements at the electrode disc was
negligible but higher potential was recorded from gel movement relative to the skin.
In addition, the potentials did not depend on impedance variations but were highly
related to specific mechanical design. Webster [28] reviewed various types of artifacts
in biopotential recording, the sources and the means for minimizing them. He
concluded that the major motion artifact in ECG recording arises from the skin and
not the electrode.
2.4.1 Skin Anatomy and Skin Potential
For its direct relationship with motion artefact, it is necessary to investigate the
structure of the skin and more importantly, how skin potential is generated and
changed during motion induced deformation. Figure 2.7 shows that the skin consists
of three layers: the epidermis, the dermis and the subcutaneous layer. The epidermis is
made up of another three layers: stratum corneum, stratum granulosum and the
stratum germinativum. The surface layer, stratum corneum, is a layer consists of dead
cells, with a thickness between 0.02 mm and 0.7 mm [25]. New cells form within the
germinating layer (stratum germinativum) and over about 30 days move outward
through the barrier layer (stratum granulosum) to the stratum corneum on the surface,
and then fall of [28].
Figure (2.7) Skin anatomy [29]
The potential difference that exists between the inside and the outside of the skin is
believed to be the sum of potentials generated by each layer. The greatest potential has
18. - 13 -
been shown to exist across the barrier membrane between the horny layer and the
granular layer [25]. Although the consensus is that the electrical behavior of the skin
can be represented by a system of resistors, capacitors and generators, there is no
unanimous definitive physical model but many empirical mechanisms have been
suggested.
Thakor and Webster [30] and Talhouet and Webster [31] approximated the skin layers
as an equivalent circuit model consisting of resistors and a current supply (Figure 2.8).
Thakor and Webster [30] hypothesized that the skin potential arises from a constant
current source called โinjury currentโ flowing through the extracellular resistance. This
injury current results from the difference of metabolic activity between the dead cells
of the stratum corneum and the viable cells of the inner layers of the skin. Talhouet
and Webster [31] validated this model through experiments, and quantified the change
in epidermal skin impedance and skin potential through stretched force which was
applied with hung weights. It was found that both the skin potential change and skin
impedance change increase with weight in a logarithmic manner. The decrease of
impedance of the transitional region shunted by the current was attributed to the
extracellular channels increase in diameter when stretching the skin.
Figure (2.8) Equivalent electrical model of skin [20].
Comert et al. [32] propose a complete electrode-skin model for both the gelled
electrodes and dry electrodes, as shown in Figure 2.9. For gelled electrodes, the
electrolyte has high conductivity and acts as a resistor, while for dry electrodes, the
contact area may change considerably with applied pressure and motion, affecting the
electrical properties of the electrode-skin interface. The half-cell potential, caused by
the ion concentration gradient between the electrode and electrolyte, is denoted as
๐ธโ๐. The skin potential, together with the epidermis is modeled as a voltage source in
series with a parallel circuit consisting of a resistor and capacitor. In the presence of
sweat, the sweat ducts start acting as a current pathway, and their contribution is
modeled as Esw in series with the parallel Rsw and Csw.
20. - 15 -
Chapter 3
A Review of Adaptive FilterTheories and its
Potentialfor Motion Artefact Reduction
3.1 Introduction
Given the potential benefits and significance of understanding the origin and
characteristics of the motion artefact, a wide variety of feasible methods for its
cancellation have been proposed and investigated in the literature. Basically, they can
be divided as non-signal processing approaches like skin abrasion or puncture [25,26]
and novel electrode design [32] while signal processing approaches include discrete
wavelet transform [36], principle component analysis [22], independent component
analysis [37] and adaptive filtering [39,40,41,42]. The method which was focused
upon in this project is adaptive filtering and this chapter provides a brief review of
important adaptive filter theories that will form the basis to understand and construct
practical algorithms for motion artefact cancellation.
The aim of this review is to provide an explanation of the adaptive methods at a level
which is suitable for both engineers and biomedical researchers. First, some
preliminary principles of adaptive filtering techniques are presented. Then, linear
adaptive filter theories are introduced as they are the most popular models studied
before and are easier to understand. The collection of previous achievements which
concludes this section is extended by incorporating the more complex nonlinear
adaptive models and algorithms, as linear ones have often been shown to leave some
of the motion artefact unaffected [3,4]. This may result from nonlinear effects in the
motion artefact generation process and the inclusion of nonlinear models can provide
a more comprehensive and rigorous evaluation of adaptive methods.
3.2 Adaptive Filter Theories
In signal processing and control applications where the signals and transfer functions
involved are time invariant and well specified at design stage, implementing fixed
filters and controllers to achieve the desired design goals is sufficient. However, in
situations where the specifications are not available or time varying, like the
movement artefact model in ECG corruption, it is necessary to construct adaptive
systems that are capable of self-adjustment in the changing environment. Two key
components of such systems are filter structure and adaptive algorithm. Their
self-learning capability is usually achieved by controlling the coefficients of a digital
filter so as to optimize predefined quantities or cost functions through adaptive
21. - 16 -
algorithms. The general configuration of an adaptive filtering system is illustrated in
Figure 3.1,
Figure (3.1) General adaptive filter configuration
where the index n denotes the number of iterations, x(n) is the primary input signal,
y(n) is the adaptive filter output signal and d(n) represents the desired signal. The
error signal e(n), calculated as d(n)-y(n), is fed back through the adaptive algorithms
to update the filter coefficients for the next iteration, aiming to decrease the cost
functions. The minimization of predetermined cost functions implies the arrival of
optimum state of the adaptive filter in some statistical sense. Depending on the
various definitions of objective functions and realizations of filter structures, different
adaptive algorithms have been developed. Additionally, distinct interpretations and
manipulations of the filter inputs and outputs give rise to four main types of
applications of adaptive filtering which are system identification, inverse modeling,
signal prediction and interference cancellation.
As the purpose of this project is to reduce as much as possible the motion artefact, the
configuration of adaptive noise cancellation, shown in Figure 3.2, was adopted. The
primary sensors, i.e. disposable electrodes, supply the original signal of interest buried
in artefact and the reference sensors presumably supply only the noise. It is assumed
that the output of the reference sensor is highly correlated with the motion artefact but
uncorrelated with the desired ECG signal. An estimate of the artefact can thus be
obtained from the reference signal through the adaptive filter, which is then subtracted
from the corrupted ECG. The overall output of the noise canceller i.e. the modified
ECG, is fed back to adjust the tap weights in the filter, trying to produce a best
estimate of the desired ECG signal in some statistical sense. One point that needs
more attention is the possible confusion between the naming preference in adaptive
filtering theories and the meanings of the signals processed in this project. What is
usually denoted as desired signal is actually the corrupted ECG and the error signal
represents the reconstructed ECG, as indicated in Figure 3.2.
Adaptive
Filter
Adaptive
Algorithm
x(n) y(n)
e(n)
d(n)
22. - 17 -
Figure (3.2) Schematic of adaptive noise cancellation
The adaptive algorithms are normally of recursive forms which start from some
predetermined set of initial conditions, representing any prior knowledge of the
environment. In a stationary environment, it has been shown that the filter coefficients
are able to converge to the optimum Wiener solution after a certain number of
iterations of the algorithms. In non-stationary environments, however, the tracking
capability varies depending upon specific algorithm employed. As a direct
consequence of the parameters being updated from one iteration to the next, the filter
coefficients become data dependent. This means that an adaptive filter is, strictly
speaking, a nonlinear system in the sense that it does not obey the principle of
superposition. Notwithstanding this property, adaptive filters are commonly classified
as linear if its input-output relationship obeys the principle of superposition whenever
its parameters are fixed. Otherwise, the adaptive filters are said to be nonlinear [34].
3.2.1 Linear Adaptive Filters
The filtering process can be implemented in a number of different structures.
Basically, linear adaptive filters can be divided into two major classes: finite duration
impulse response (FIR) filter and infinite duration impulse response (IIR) filter.
Popular FIR filter structures include transversal filter realized through a tapped delay
line, lattice predictor consisting of a number of individual lattice stage and systolic
array which represents a parallel computing networks ideally suited for mapping a
number of important liner algebra computations. Canonic direct form IIR filters have
also attracted much interest. The choice of different structure can have profound
influences on filter performances such as computational complexity, stability and
speed of convergence. In this project, we only focus on casual FIR adaptive filters
implemented through a transversal structure, as they are the simplest model to begin
with and have been proved effective in numerous applications. The block diagram of
a typical transversal adaptive filter is displayed in Figure 3.3,
Adaptive
Filter
Adaptive
Algorithm
r(n) y(n)
e(n)
Primary Signal: d(n)=s(n)+a(n)
Reference Signal
Desired
Signal
Motion
Artefact
23. - 18 -
Figure (3.3) Transversal adaptive filter.
The number of delay elements, M, is referred to as the filter order. The output of such
a transversal filter is given by,
๐ฆ( ๐) = โ ๐ค ๐ ๐ฅ(๐ โ ๐)๐=๐โ1
๐=0 (3.1)
where ๐ค ๐is the tap weights, x(n) is the reference input, y(n) represents the estimated
motion artefact and e(n)is the filtered ECG signal.
Adaptive algorithms are the mechanisms by which the filter coefficients are updated.
There is no unique solution to this problem and a huge number of algorithms have
been developed to a wide variety of filter structures. Each of them offers desirable
features of its own such as rate of convergence, misadjustments, tracking capability,
robustness or computational complexity. From this immensity, two basic underlying
methodologies can be identified for deriving linear adaptive filter algorithms. They
are stochastic gradient approach and least-square approach, giving rise to the Least
-Mean-Square (LMS) based algorithms and the Recursive Least-Squares (RLS) based
algorithms respectively. Performances and properties of these algorithms have
investigated and discussed in numerous textbooks and papers, thus a detail description
is not repeated and only a quick overview is provide. Interested readers can refer to
[34].
The cost function of the stochastic gradient approach is defined as the mean-square
error (the mean square value of the difference between the desired response and the
transversal filter output). This is a quadratic function of the tap weights and its bottom
point is approached through a recursive algorithm based on the method of steepest
descent. Practical implementation of this method results in the widely known least-
mean-square (LMS) algorithm, whose conventional version can be expressed by the
following set of equations,
๐งโ1
๐งโ1
Adaptive Algorithm
๐ค0
๐ค1 ๐ค2
๐งโ1
๐ค ๐โ1
โฌ โฌ โฌ โฌ
x(n) x(n-1) x(n-M+1)
y(n)
d(n)
e(n)
24. - 19 -
๐ฆ( ๐) = ๐พ ๐( ๐) ๐ฟ( ๐) (3.2)
๐( ๐) = ๐( ๐) โ ๐ฆ(๐) (3.3)
๐พ( ๐ + 1) = ๐พ( ๐) + ๐๐ฟ(๐)๐(๐) (3.4)
where n is the number of iteration, ๐ฟ(๐) is the reference input column vector, ๐พ(๐)
is the filter weights column vector at the nth
iteration, d(n) is the primary input (Noisy
ECG), y(n) is the filter output (Estimated Artefact), e(n) is the error signal
(Reconstructed ECG) and ๐ is the step size parameter.
The value of the step size parameter controls the rate of convergence and filter
stability. The necessary condition for the mean square stability of LMS filters with
moderate or large length is given by [34],
0 < ๐ <
2
๐๐ ๐๐ ๐ฅ
(3.5)
where ๐ ๐๐๐ฅ is the maximum value of the power spectral density of the input signal
and M is the filter order.
The LMS algorithm estimates the real gradient vector from the instantaneous
correlation matrix and cross correlation vector, introducing gradient noise. The filter
coefficients computed this way execute a random motion around the minimum point
of the error performance surface, instead of terminating on the optimal Wiener
solution. But the LMS algorithm is still the most widely adopted algorithm for its
computational simplicity and proof of convergence in the stationary environment. In a
non-stationary environment, the LMS algorithm is able to continually track the bottom
of the error performance surface, provided that the input data vary slowly compared
with the learning rate of the LMS algorithm [34].
Another approach to the problem of optimal filtering is least square estimation whose
cost function is defined as the sum of weighted error squares. The recursive realization
of the method of least squares, based upon matrix inversion lemma, results in the RLS
algorithm. It belongs to the quasi-Newton adaptive algorithms and is more difficult to
implement in real time than the LMS algorithm since it requires more calculations in
order to provide the recovered signal. The standard RLS algorithm can be described
by the following set of equations,
๐( ๐) = ๐ท(๐ โ 1)๐ฟ(๐) (3.6)
๐( ๐) =
๐(๐)
๐ฟ (๐)๐(๐)
(3.7)
๐( ๐) = ๐( ๐) โ ๐พ (๐ โ 1)๐ฟ(๐) (3.8)
๐พ(n) = ๐พ( ๐ โ 1) + ๐(๐)๐(๐) (3.9)
๐ท( ๐) = โ1
๐ท( ๐ โ 1) โ โ1
๐( ๐) ๐ฟ (๐)๐ท(๐ โ 1) (3.10)
where n is the number of iteration, ๐ฟ(๐) is the reference input column vector, ๐( ๐)
is the gain vector, ๐พ(๐) is the filter weights column vector at the nth
iteration, d(n) is
25. - 20 -
the primary input (Noisy ECG) and e(n) is the error signal (Reconstructed ECG).
The computation of w is based on a priori estimation error e and the gain vector k. The
matrix P is the recursive estimation of the inverse of the correlation matrix of the
input reference. The ฮป, the forgetting factor (0 < ฮป โค 1), controls the rate of adaptation
of the algorithm. The fundamental difference between RLS and LMS algorithm is that
the step-size parameter is replaced by the inverse of the correlation matrix of the input
vector. Because of this modification, the rate of convergence of the RLS algorithm is
typically one order of magnitude faster than that of the LMS algorithm and also
invariant to the eigenvalue spread of the ensemble average correlation matrix of the
input vector. Although the RLS algorithm is more computationally demanding, the
faster convergence speed is highly desirable in the current study as the motion artefact
could be rapidly changing in some situations. Besides, since practical implementation
is not a major concern of this project, LMS adaptive filters were only validated in
computer simulations while the RLS adaptive filters were applied to both emulated
ECGs and experimentally measured ECGs.
3.2.2 Nonlinear Adaptive Filters
A nonlinear filter is involves a structure that produces an output which is not a linear
function of the inputs. It offers a solution to model system nonlinearities when linear
assumption is not sufficient or fails completely. Evidence is available showing that the
mechanical stress applied and the resultant skin potential variations follow a nonlinear
relationship [31]. This serves the main motivation for extending previous linear
adaptive motion artefact reduction scheme to nonlinear ones. A major drawback of
nonlinear structures is the increased model complexity and lack of mathematical tools,
originating from the high degrees and dimensionality of the nonlinearities [35]. But
this should not be a serious problem for this project as all sets of recorded data were
processed offline. Popular techniques for implementing nonlinear adaptive filters
include the non-recursive polynomial model based on the Volterra series expansion,
the recursive polynomial model based on nonlinear difference equations, the
multilayer perception neural network and the radial basis function neural network
[35].
The nonlinear structures investigated in this project are three Cascade models (LN,
NL, LNL) which consist of dynamic linear and static nonlinear elements (Figure 3.4).
They were considered instead of Volterra or Wiener series because they provide a
parsimonious and efficient method of increasing the level of nonlinearity and
overcome the restriction of requiring large amounts of data to obtain robust parameter
estimates. Previous work [44, 45] has also shown them to be effective in representing
nonlinear biological systems. Moreover, cascade models simplify the analysis of
nonlinear response of systems since their nonlinear polynomial element remains in
two dimensional space regardless of their order. As they are subsets of the Volterra
series, it is also possible to transform their parameters into Volterra form. The block
diagrams of the three cascade models are shown in Figure 3.4.
26. - 21 -
The LN (Linear Nonlinear) or Wiener model, consists of dynamic linear element
followed by a static nonlinearity, which is chosen to be a polynomial function given
by,
๐(๐ฅ( ๐ก)) = โ ๐( ๐)
๐ฅ ๐
(๐ก)
๐
๐=0 (3.10)
Where Q is the polynomial order, c its coefficients and x the input signal to the
nonlinearity. The output of the LN model is thus calculated as,
๐ฆ( ๐ก) = โ ๐( ๐)
(โ โ(๐)๐ข(๐ก โ ๐)๐โ1
๐=0 ) ๐๐
๐=0 (3.11)
The NL (Nonlinear Linear) or Hammerstein model consists of a static nonlinearity
followed by a dynamic linear element. Its output is given by,
๐ฆ( ๐ก) = โ โ(๐)(โ ๐(๐)
๐ข(๐ก โ ๐) ๐๐
๐=0 )๐โ1
๐=0 (3.12)
The LNL (Linear Nonlinear Linear) or Wiener Hammerstein model consists of two
dynamic linear elements h(๐) and g(๐), separated by a static nonlinear element. Its
model output can be written as,
๐ฆ( ๐ก) = โ ๐(๐)๐โ1
๐=0 โ ๐( ๐)
(โ โ(๐)๐ข(๐ก โ ๐ โ ๐)๐โ1
๐=0 ) ๐๐
๐=0 (3.13)
Figure (3.4) Block diagrams of (A) LN, (B) NL and (C) LNL cascade models [46] .
One of the most widely used iterative methods for estimating the parameters of these
block structures was developed by Korenberg and Hunter [46,47], referred to as KH
method in this project. They minimized the cost functions using Levenberg Marquart
gradient descent method which combines the robust convergence properties of
steepest descent method with the faster convergence of the Gauss Newton method [49,
27. - 22 -
50]. As there are already many discussions on these algorithms, only their summaries
[46] are provided in Table 3-1, Table 3-2 and Table 3-3 for LN, NL and LNL models
respectively.
Table 3-1 [46]
Table 3-2 [46]
Table 3-3 [46]
The method adopted in the current study is a variant of the standard KH method,
called Smoothed Korenberg Hunter method which was developed in [46]. It estimates
the linear elements of the cascade models using the least square method instead of the
cross correlation approach as this approach is less dependent on the properties of the
28. - 23 -
input signal. It also incorporates a Butterworth low pass filter to reduce the estimation
error in the linear elements of the LN and NL models and in the second linear element
of the LNL model.
3.3 Areview of Adaptive Motion Artefact Reductionin ECG signals
The adaptive methods, mainly the linear ones have been investigated in numerous
studies [39,40,41,42] to suppress various types of interferences in ECG recordings
such as, power line disturbance, baseline wander and motion artefact, all showing
promising results. As adaptive methods were adopted exclusively for motion artefact
reduction in this study, a brief overview of similar previous work is presented in this
section.
One necessary step of adaptive interference cancellation is the recording of an
additional channel of reference signal which is assumed to be highly correlated with
the noise in some unknown manner but uncorrelated with the ECG measurements.
One of the major differences between various adaptive filtering schemes proposed
previously is the choice of relevant reference signal. Skin-electrode impedance [39],
electrode acceleration [40], and skin strain [20] have all been demonstrated to be
effective in representing motion artefact superimposed on ECGs. But less evidence is
available showing that these algorithms could reduce the motion artefact in a
consistent manner for all combinations of electrodes placements and movement types.
This is possibly due to the fact that different combinations of electrodes arrangements
and motions introduced produce reference signals of various qualities in the sense that
some reference channels have a higher correlation with the artefact components than
other. Hence different performances occur. An example of adaptive motion artefact
reduction in ECG signal is shown in Figure 3.5,
Figure (3.5) An example of adaptive motion artefact removal [38]
Devlin et al. [39] used electrode / skin impedance variations to estimate and reduce
motion artifact in ECG signals, so as to reduce false positive alarms. The time-varying
impedance of the electrode / skin interface was monitored by passing a small AC
current through the ECG electrode and measuring the resultant potential difference
across the electrodes. Artifacts were introduced by scratching and tugging on the
electrodes, and abrupt physical movements. The results showed that 82% of the false
positive alarms can be eliminated, with a loss of 7% of the true positives. However,
29. - 24 -
the torso impedance will introduce the possibility of impedance artifact generated by
breathing or heart pumping.
Tong et al. [40] compared two other kinds of reference signals which are obtained by
a 2-axis anisotrophic magnetoresistive (AMR) sensor and a 3-axis accelerometer
(ACC) sensor fabricated on the electrodes, as shown in Figure 3.6. ACC sensor was
shown to perform better than the AMR sensor and one possible reason is that ACC
signal is more linearly correlated with the motion artefact than the AMR output.
Motion artifact was induced at the left arm electrode site using three methods: (1)
pressing on the electrode; (2) pushing on the skin around the electrode and (3) pulling
on the electrode lead wire. In one of the data sets, all of these motions were
introduced simultaneously. Performance of the adaptive method was evaluated by the
signal L2 norm and MaxMin statistic before and after filtering. A third percentage
improvement showed that the overall noise reduction rate ranged from 24% to 91%
depending upon the motion type and the reference sensor.
(a) AMR sensor (b) ACC sensor
Figure (3.6) Integration of a motion sensorinto an electrode [40]
Based on the conclusion that motion artefact resulted from skin potential changes
caused by skin deformation [26], Hamilton et al. [41] mounted a miniature
displacement sensor on the ECG electrode to measure the skin stretch signal directly.
It was assumed that this reference signal should more reliably reduce motion artifact
since it was responsible for artefact generation. Motion interference was manually
generated by pulling on either side of the skin and pushing on the center of the
electrode. The results indicated that adaptive filtering could effectively suppress
motion artifact. Filters with three or four coefficients achieved considerable reduction
but higher orders didnโt contribute greatly to further interference cancellation. The
applicability of the approach was limited by the expensive sensors required at that
time, but this may not be a serious problem under current standards.
Possibly inspired by the method adopted by Devlin and Mark [40] mentioned above,
Hamilton and Curley [42] compared the effectiveness of sensor based to impedance
based adaptive motion artefact removal. Instead of measuring 20kHz impedance
signal, they used lower frequency impedance fluctuations since lower frequency
impedance should be more sensitive to changes in skin impedance and less sensitive
30. - 25 -
to changes in torso impedance. The displacement sensors were also replaced by
optical bend sensors, measuring the skin stretch signal around the electrode. For each
recording, motion artefact were induced by pressing directly on the electrode attached
to the forearm. The experiments showed an inverse correlation between artifact
attenuation and impedance frequency and impedance based algorithms produced
similar performance to sensor based methods. Although artifact reduction increased
with the order of the filter, the improvement was not obvious for filter orders higher
than four.
In contrast to attaching motion sensors on the electrodes, Raya and Sison [2] fixed
either uniaxial or dual-axial accelerometer on the subjectโs lower back, at the
lumbosacral level. Both the lead โ ECG and accelerometer data are recorded
simultaneously and band limited to retain only the necessary information while the
subject was cycling on the bicycle ergometer. It was found that a single axis noise
reference produced better results than dual-axis noise reference irrespective of the
adaptive filtering algorithms applied. One possible explanation is that major
kinematic acceleration component is usually found in the vertical direction. Although
simple Lease Mean Square (LMS) algorithm posed less computational load, recursive
least square (RLS) algorithm produced a superior performance. RLS converges faster
than LMS and it can track the rapidly varying environment compared with LMS.
In Luo and Tompkinsโ [43] research, the auxiliary input for adaptive filtering was the
motion noise signal obtained from the voltage difference between two adjacent
electrodes located on the arm, near the right biceps muscle. The artifacts were induced
by raising the right lower arm from a relaxed position until the lower arm makes
contact with the brachium. To validate the hypothesis that the dominant disturbance is
localized in the region immediately surrounding the electrodes, two types of motion
artefact were measured from two different electrodes placements while the primary
input remained the same standard leadโ ก, as shown in Figure 3.7. The reference input
for the upper adaptive filter is the potential difference between electrode 1and 3 while
that for the lower system is the difference between electrode 4 and 3. The experiment
results proved the above assumption and the performance of RLS and LMS algorithm
was also compared. It was found that RLS algorithm converged much faster than the
LMS algorithm which is particularly advantageous in reducing rapidly varying
brachial artifact. Furthermore, the RLS algorithm produced a considerable
enhancement on low frequency baseline wander.
31. - 26 -
Figure (3.7) Comparison of different motion noise references [43]
All of the above studies base their artefact reduction systems on linear adaptive
structures which have been shown to produce considerable signal enhancement. To
the best of the authorโs knowledge, nonlinear systems have not been used to suppress
motion artefact in ECG signals. However, it is possible that nonlinear adaptive filters
can perform as well as or even better than linear ones under certain circumstances. In
fact, the residual motion artifact, observed in all the above-mentioned studies, may
result from the nonlinear relationship between skin stretch and motion artifacts. It has
been shown that the skin potential variations changes with the stretch force in a
logarithmic manner [31]. This is the major motivation for including nonlinear
structures and relevant algorithms for motion artefact reduction in electrocardiogram
in the current study. Examples of nonlinear noise reduction can be found in other
applications, e.g powerline interference where neural network [51, 52] or fuzzy rule
based adaptive nonlinear filter [53,54] have been explored. Nonlinear Bayesian
filtering technique has also been applied for denoising ECG signals acquired in a
magnetic resonance environment [55].
32. - 27 -
Chapter 4
ECG Signal Acquisitionand Motion Artefact
Generation
4.1 Introduction
The content of this chapter provides an explanation of the experimental methods
adopted for ECG signal acquisition and motion artefact generation. The objective of
the experiment is to introduce motion artefact to ECG signals through controlled
vibration of an electromagnetic shaker and voluntary motions of the subjects. This
combination of movements had been used in previous undergraduate [3] and MSc [4]
projects. The vibration of the shaker and the motion of the subjectโs hand was
monitored and recorded simultaneously by single-axis accelerometer and tri-axial
accelerometer respectively. These accelerations serve as reference signals to
adaptively reduce motion artefact in chapter 5. To quantitatively evaluate and
compare the effectiveness of different adaptive algorithms, another channel of the
same lead ECG was acquired with electrodes on the chest that were negligibly
affected the subjectโs motions. The reconstructed ECG signals were compared with
this presumably ideal ECG template to determine how much motion artefact had been
removed.
This chapter commences by describing the equipment preparation & stimulus signal
generation process. Next, the methods used to calibrate the shaker accelerometer
system and relevant results are presented. It concludes by explaining the experimental
procedures of ECG corruption and acceleration signals acquisition.
33. - 28 -
4.2 Methods
4.2.1 Equipment Set Up
The set of equipment used in this experiment can be divided into two subsystems:
vibration module and ECG acquisition module. The vibration module, aiming to
introduce controlled motion through the subjectโs hand, consists of a computer, D/A
converter, analog filter, shaker amplifier and shaker while the ECG signal is recorded
using the Data Acquisition System produced by Biopac Systems, USA [66]. The
vertical acceleration of the shaker, analog filter output, amplifier output and tri-axial
acceleration of the subjectโs hand were also recorded simultaneously with ECG
channels by its integrated analog to digital converter module and stored in the same
computer. The block diagram and a photograph of the equipment set up are shown in
Figure 4.1 and Figure 4.2 respectively. The โsig-accโ in Figure 4.1 denotes the vertical
acceleration of the shaker while โTri-accโ the three axis accelerations of the subjectโs
right hand.
Figure (4.1) Block diagram of the system set-up
Figure (4.2) Photograph of the system set-up
A detailed explanation of each component and its functionality in the system is
presented below,
Computer
USB-1408FS
Analog Output
Analog Filter
Amplifier
Shaker
Sig-acc
Subject
Right Hand
Signal
Conditioner
ECG 100C
Amplifier
Biopack
Data Acquisition System
Stimulus Signal Recorded ECG
Tri-acc
34. - 29 -
Vibration module:
1. USB-1408FS
The stimulus signals generated in MATLAB
are of digital format. They were converted to
analog signals using D/A convertor, USB-
1408FS (Figure 4.3), produced by Measure-
ment Computing in Hungary to drive the
shaker.
Figure (4.3) USB-1408FS
2. Analog low pass Filter
Dual channel elliptic filter, type: VBF/23, made by Kemo Limited Electronic, UK.
The voltage output of the USB-1408FS always contains harmonics of the 15 Hz
sinusoidal excitation signal because of the D/A conversion process. Before the
stimulus was sent to the shaker, an analog low pass filter was use to attenuate these
harmonics and transmit only the fundamental frequency component.
3. Shaker Amplifier
TPA series power amplifier manufactured by HH electronics, UK. The filtered
stimulus signals were amplified by this amplifier to drive the shaker with the
determined magnitude.
4. Shaker with a horizontal handle
An electromagnetic shaker (Figure 4.4) with a horizontal
handle was used to introduce controlled vibration to the
subjects through the right hands.
5. Single Axis Accelerometer
6. Tri-Axial Accelerometer
Model type: ADXL330, manufactured by Analog
Devices, USA. The small, low power tri-axial
accelerometer, shown in red window in Figure 4.5
was fixed onto the electrode to monitor and record the
motion of the subjectโs right hand during the
experiment. The orientation of its three axes is also
shown in the picture using orthogonal lines.
Figure (4.5) Tri-axial accelerometer
on electrode [3]
Figure (4.4) Shaker and single
axis accelerometer
Model type: 353B52, manufactured by PCB Piezotronics,
USA. The vertical vibration of the shaker was monitored
and recorded using this single axis accelerometer (Figure
4.4 inside the red oval window) attached on top of it. The
readings of this accelerometer were amplified 100 times
before being sent to the Biopac data acquisition system.
35. - 30 -
Data Acquisition Module:
1. ECG 100C
This an electrocardiogram amplifier for the signals
detected by the disposable electrodes. The gain was
set to 1000 times in this project.
2. Analog-to-Digital Converter (MP 100 System)
This system consists of a universal interface module
UIM 100C for organizing the ECG and reference
signals and a High performance data acquisition unit
MP100ACE for transmitting the digital signals to the
computer.
3. Acqknowledge 3.9.0 software
This software can simultaneously display and store all channels of signals recorded by
the MP 100 system. It is also used to set the working parameters such as sampling
frequency and recording duration depending on various motion types involved.
4.2.2 Method of Stimulus Signal Generation
The excitation signals of this experiment were generated in MATLAB. The 15Hz
sinusoidal stimulus signal, with amplitude values varying from minus one to one volts
was generated using function: sin, while the Bandlimited Gaussian White Noise
(BLWN) signal using its random number generation function: randn and Butterworth
low pass filter. The sampling frequency was set to 2000 Hz. This was determined after
a series of trials on different sampling frequencies and then by comparing the power
spectra of the resulting output of the DAC. It was found that this was high enough to
cause negligible distortions to the outputs of D/A converter. The default settings of the
function randn create random numbers from a Gaussian distribution with a mean of
zero and a standard deviation of one. This GWN was then forward and backward
filtered by a 4th
order Butterworth low pass filter with a cut off frequency of 20Hz to
avoid any phase delay. The resulting sinusoidal, BLWN stimulus signals were saved
into a single vector as an excitation signal. This facilitates the experimental procedure
not only because it keeps the vibration intensity the same for all the subjects, but also
enables the measurement of the influence of both types of induced movements in a
single recording. The temporal fluctuations of the stimulus signals and their power
spectra estimates are shown in Figure 4.7. The method for computing these power
spectra is Welch method with block size of 8000 and 50% overlap. It can be seen that
the band pass filtered GWN had much lower amplitude than the sinusoidal one.
Figure (4.6) ECG acquisition
module
36. - 31 -
(A) (B)
(C) (D)
Figure (4.7) Stimulus signals generated using MATLAB functions and their power spectra. (A)
Sinusoidal excitation signal and its power spectrum estimate (B). (C) BLWN excitation signal and its
power spectrumestimate (D).
To induce a sufficient amount of motion artefact during the sinusoidal input, the
amplification level was set to introduce the maximum acceleration permitted by the
university ethics committee (Appendix B). But the wide dynamic range of the
bandlimited GWN creates practical difficulties for the shaker amplifier and the shaker
because the huge spikes in the BLWN will almost certainly drive the shaker out of its
vibrating limits at this intensity, pushing the handle to strike the upper and lower
bounds of the cylinder. This will introduce erroneous acceleration readouts and
potentially damage the shaker. Although it is possible to manually decrease the
amplification level for BLWN stimulus during the recording session, this would
probably not able to keep the vibration dose constant to each subject. To solve this
problem, the amplitude of this GWN stimulus signal was firstly rescaled to have half
as much energy as the sinusoidal signal, as shown in Figure 4.8-A. Next, a small
number of points whose values were higher than 1.5 or low than -1.5 were set to half
of their original magnitudes to reduce its dynamic spread, resulting in restricted
BLWN as shown in Figure 4.8-B,
0 10 20 30 40 50 60
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Time/s
Voltage/V
Sinusoidal stimulus signal
0 10 20 30 40 50 60 70 80 90 100
-100
-90
-80
-70
-60
-50
-40
-30
-20
-10
0
10
Frequency/Hz
Power/Frequency(dB/Hz)
Welch Power Spectral Density Estimate
0 10 20 30 40 50 60
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Time/s
Voltage/V
Bandlimited GWN stimulus signal
0 10 20 30 40 50 60 70 80 90 100
-100
-90
-80
-70
-60
-50
-40
-30
-20
-10
0
10
Frequency/Hz
Power/Frequency(dB/Hz)
Welch Power Spectral Density Estimate
37. - 32 -
(A) (B)
Figure (4.8) Modified BLWN stimulus. (A) Rescaled BLWN signal. (B) Restricted BLWN signal.
This modification produced negligible effects on its power spectrum and the restricted
random signal can still be regarded as white in the frequency range of 0 to 20Hz. The
amount of vibration energy introduced to the subject from this modified BLWN signal
would be about half as much as that through sinusoidal excitation.
To drive the electromagnetic shaker using the above created digital signals. The
USB-1408FS D/A converter was connected to the computer via a specially designed
MATLAB program which allowed the transmission of excitation signals to the shaker
amplifier. The user interface for loading and outputting excitation stimulus is shown
below in Figure 4.9,
Figure (4.9) Program user interface of the USB-1408FS
4.2.3 Equipment Calibration
The purpose of this calibration procedures presented in this section is to make sure
that every part of the system functions as expected in the sense that they are able to
reproduce the desired controlled motion with insignificant discrepancies. Only the
vibration module was calibrated. As the vibration subsystem is made up of four
components, the output of every part is recorded and analyzed in time and frequency
domain for both the 15 Hz sinusoidal and bandlimited white noise excitations. The
amplitudes of these excitation signals were set to the same levels as those introduced
to human subjects in the recording sessions. As the motion of the shaker was
monitored by a single axis accelerometer, this accelerometer was checked first. The
responses of the whole subsystem were calibrated subsequently.
0 10 20 30 40 50 60
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
Time/s
Voltage/V
Rescaled BLWN stimulus signal
0 10 20 30 40 50 60
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Time/s
Voltage/V
Restricted BLWN stimulus signal
38. - 33 -
Calibration of the Single Axis Accelerometer
The single axis accelerometer used is model 353B52 produced by PCB Piezotronics,
USA. The sensitivity of the one used, as indicated in its calibration card, is 47.1๐๐ฃ/
๐. To validate the performance of this accelerometer, a one pound coin was placed on
top of the shaker where the handle was also mounted, following the practice in [4].
The shaker was then driven by 15 Hz sinusoidal stimulus with varying levels of
amplification. When this coin started jumping on the shaker, it means its acceleration
had reached that of gravity gโ9.81m/s2
. The measured peak voltages of the output of
the accelerometer (Figure 4.10-A) at this point were used to check the calibration.
(A) (B)
Figure (4.10) Calibration of the single axis accelerometer.
The peaks of the voltage output at this vibration intensity were found to be around
47.5๐๐ฃ/๐, which is very similar to the value of the calibration card. Considering the
level of uncertainty within the process, especially the determination of the minimal
amplitude of the coin to start bouncing, perfect agreement should not be expected. As
a result, the previously calculated sensitivity level was adopted in the remaining part
of the experiment and a value of 4.8๐๐ฃ/๐๐ โ2
was used. The corresponding
acceleration values at the bouncing moment are also plotted in Figure 4.10-B.
System Calibration with Sinusoidal Excitation
Apart from the single axis accelerometer, all the other components of the vibration
module were also calibrated. The shaker amplifier was set to the maximally permitted
level specified in the ethics form approved by the university ethics committee. There
is also the intensity which the subject experienced during the actual recording sessions.
The outputs of the D/A converter, analog low pass filter, amplifier and the single axis
accelerometer are shown in Figure 4.11. It can be observed that the few number of
harmonics at the output of the USB-1408FS (B) were effectively removed by the low
pass filter (D), although some were reintroduced by the amplifier (F). From the results
in Figure 4.10-H, it is concluded that the shaker exhibited quite strong nonlinear
effects which produced a series of higher harmonics. It should be noted that the 50 Hz
power line interference is also quite obvious in the power spectrum. This is probably
due to the fact the accelerometer is located just above the shaker which is driven by an
0 0.5 1 1.5 2 2.5 3
-60
-40
-20
0
20
40
60
Time/s
Voltage/mV
Voltage output of single-axis accelerometer
0 0.5 1 1.5 2 2.5 3
-15
-10
-5
0
5
10
15
Time/s
Acc/ms-2
Acceleration of the shaker
39. - 34 -
alternating current. This effect was also observed in the noisy limb ECG which was
measured while the hand was placed onto the vibrating shaker. The other multiple
peaks existing between the large harmonics might result from the aliasing effect of the
sampling practice.
Although higher harmonics of the fundamental excitation frequency were also noticed
in many of the noisy ECG recordings, they are normally much weaker than those
present in the acceleration readings. Adaptively reducing the motion artefact of the
ECG signals using these as reference inputs can actually corrupt the ECG with those
harmonics instead of removing them. This will be seen more clearly chapter 5. In
spite of their lower magnitudes, all of the acceleration measurements are band pass
and notch filtered as well.
(A) (B) (C)
(D) (E) (F)
(G) (H)
Figure (4.11) Time and frequency domain responses of different components of the vibration module
for 15 Hz sinusoidal input. (A) Output voltage of DAC and its power spectrum estimate (B). (C)
Output voltage of the low pass filter and its power spectrum estimate (D). (E) Output voltage of the
amplifier and its power spectrum estimate (F). (G) Output voltage of the shaker accelerometer and its
power spectrumestimate (H).
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Time/s
Voltage/V
Output voltage of DAC for white noise input
0 10 20 30 40 50 60 70 80 90 100
-90
-80
-70
-60
-50
-40
-30
-20
-10
0
10
X: 15
Y: 1.568
Frequency/Hz
Power/Frequency(dB/Hz)
Welch Power Spectral Density Estimate
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Time/s
Voltage/V
Output voltage of the low pass filter
0 10 20 30 40 50 60 70 80 90 100
-90
-80
-70
-60
-50
-40
-30
-20
-10
0
10
X: 15
Y: 1.459
Frequency/Hz
Power/Frequency(dB/Hz)
Welch Power Spectral Density Estimate
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
-3
-2
-1
0
1
2
3
Time/s
Voltage/V
Output voltage of the amplifier
0 10 20 30 40 50 60 70 80 90 100
-80
-70
-60
-50
-40
-30
-20
-10
0
10
Frequency/Hz
Power/Frequency(dB/Hz)
Welch Power Spectral Density Estimate
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
-20
-15
-10
-5
0
5
10
15
20
Time/s
Acc/ms-2
Acceleration of the shaker
0 10 20 30 40 50 60 70 80 90 100
-60
-50
-40
-30
-20
-10
0
10
20
30
X: 15
Y: 23.22
Frequency/Hz
Power/Frequency(dB/Hz)
Welch Power Spectral Density Estimate
X: 30
Y: -7.149
X: 45
Y: -12.41
40. - 35 -
System Calibration with Bandlimited White Noise Excitation
The same set of procedures was carried out with bandlimited white noise input. The
results of calibration process are shown in Figure 4.12 from which it can be seen that
the D/A converter, low pass filter and the shaker amplifier preserve the whiteness of
the excitation signal with negligible level of distortion. The shaker, however,
attenuated the low frequency components, giving rise to a colored white noise
vibration. This could seriously limit the amount of energy transmitted to the subject
through random excitation whose energy is already only half as strong as the
sinusoidal excitation. The motion artefact are more difficult to reveal in the ECG
recording measured under such condition, as are discussed more rigorously in later
chapters.
(A) (B) (C)
(D) (E) (F)
(G) (H)
Figure (4.12) Time and frequency domain responses of different components of the vibration module
for bandlimited white noise input. (A) Output voltage of DAC and its power spectrumestimate (B). (C)
Output voltage of the low pass filter and its power spectrum estimate (D). (E) Output voltage of the
amplifier and its power spectrum estimate (F). (G) Output voltage of the shaker accelerometer and its
power spectrumestimate (H).
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Time/s
Voltage/V
Output voltage of DAC for white noise input
0 10 20 30 40 50 60 70 80 90 100
-70
-60
-50
-40
-30
-20
-10
Frequency/Hz
Power/Frequency(dB/Hz)
Welch Power Spectral Density Estimate
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
-1
-0.5
0
0.5
1
1.5
Time/s
Voltage/V
Output voltage of the low pass filter
0 10 20 30 40 50 60 70 80 90 100
-80
-70
-60
-50
-40
-30
-20
-10
Frequency/Hz
Power/Frequency(dB/Hz)
Welch Power Spectral Density Estimate
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Time/s
Voltage/V
Output voltage of the amplifier
0 10 20 30 40 50 60 70 80 90 100
-70
-60
-50
-40
-30
-20
-10
Frequency/Hz
Power/Frequency(dB/Hz)
Welch Power Spectral Density Estimate
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
-20
-15
-10
-5
0
5
10
15
20
Time/s
Acc/ms-2
Acceleration of the shaker
0 10 20 30 40 50 60 70 80 90 100
-60
-50
-40
-30
-20
-10
0
10
Frequency/Hz
Power/Frequency(dB/Hz)
Welch Power Spectral Density Estimate
41. - 36 -
From the perspective of adaptive filtering, the exact manners of how the shaker
vibrates simply do not matter as long as we have good quality recordings of the ECG
and accelerations. This is because the adaptive algorithms are specifically designed to
automatically adjust its parameters depending on different inputs. This means that
while the white noise is not white under practical conditions, this does not
fundamentally alter the ability to assess the performance of adaptive algorithms. The
reason why so much attention has been paid to the behavior of the vibration module is
that strange shaker motions can possibly induce erroneous measurements of or erratic
noise to both the ECG and acceleration acquisitions from which sensible
interpretations of the adaptive methods can never be achieved.
Vibration Intensity of the Shaker
In addition to checking that every component of the system responds properly to each
type of excitation signal, efforts must also be made to ensure that the vibration
intensity of the shaker does not exceed the maximum level approved by the university
ethics committee. The Root Mean Square (RMS) acceleration of the vibrating handle
should be no higher than 10 ๐/๐ 2
for the 15Hz sinusoidal input and 20 ๐/๐ 2
for
the band limited Gaussian white noise input. Adjusting amplification to different
magnitudes, a series of measurements were carried out to reveal the relationship
between RMS amplifier voltage output and RMS handle acceleration. In Figure 4.13,
it can be seen that the RMS voltage of the amplifier should not be higher than 1.4V to
conform to ethics requirement. As has been shown in previous studies [3,4], the
motion artefact may not appear effectively in some subjectsโ ECG recordings. The
gain of the amplifier was therefore set to just a little less than the maximum
permissible level for all the subjects to maximize the possibility of inducing sufficient
amount of motion artefact. As the variance of the BLWN stimulus had already been
rescaled to half of that of sinusoidal stimulus, there is no need to check the random
excitation energy limit.
Figure (4.13) Acceleration sensitivity of the shaker
4.2.4 Experimental Procedure
A description of the experimental procedures of ECG corruption and reference signal
acquisition performed on seven healthy students (six male and one female) is
provided in this section. These sets of ECGs and reference recordings were labeled as
0 0.2 0.4 0.6 0.8 1 1.2 1.4
0
1
2
3
4
5
6
7
8
9
10
RMS voltage from the amplifier/V
RMSaccelerationoftheshaker/(m/s2
)
Calibration of the shaker for 15Hz sinusoidal input
42. - 37 -
Subject 1 to Subject 7 to protect their identity. Another set of data acquired in a
previous project [4] where 10 Hz sine wave vibration was also introduced through
two kinds of handles (vertical and horizontal) was taken and labeled as subject 8 in
this report. Because of the format of the data, the accelerations recordings of subjects
(Subject 1 to 7) measured in this project have the unit of ๐/๐ 2
while those of subject
8 were rescaled to have arbitrary unit. In order to ensure that each subject is healthy
and is willing to have their data collected, a consent form (Appendix C) and a brief
health questionnaire (Appendix D) were provide to them to sign and fill in before the
start of each experiment. The subjects have the right to withdraw from the experiment
at any moment without having to give reasons.
Figure 4.14 and Table 4-1 show the schematic and the exact locations of electrodes
placement for both Limb and Chest ECG recordings. As shown in Figure 4.14, two
channels of the same lead โ ก ECG were recorded simultaneously with a sampling
rate of 200 Hz. Channel one ECG (Limb ECG) which was exposed to motion artefact
was measured as the potential difference between the back of the right hand and the
left leg, and the tri-axial accelerometer was pasted on top of the electrode by double
sided adhesive. Channel two ECG (Chest ECG) was measured as the potential
difference between the sternum and the left leg. This pair of electrodes is intended to
be unaffected or only negligibly affected by the motion artefact and can thus serve as
the โidealโ template for algorithm evaluation after adaptive filtering. This practice of
measuring one channel of noisy ECG and another channel of clean ECG for reference
has been adopted by many previous studies such as [3,4,32]. These two-channel ECG
recordings result in a total of six disposable electrodes attached to specific skin sites
during all the three parts of this experiment. Selected skin sites were sterilized using
alcohol-base wipes before electrodes attachment to improve electrical conduction.
.
Figure (4.14) Placement of electrodes on the subject.Adapted from [20].
Table 4-1 Electrode Locations
Positive electrode Negative electrode Ground
Channel One Left lower leg Middle of the sternum Left clavicle
Channel Two Left lower leg Back of the right hand Left clavicle
43. - 38 -
It should be noted that in most clinical situations, electrodes are seldom placed on
hands because of the interference that maybe present. In this project, however, motion
artefact are desired, thus such a configuration will facilitate the process of artefact
generation. But this may give rise to other types of noise and artefact such as EMG,
which could undermine the performance analysis of various adaptive algorithms since
they are very likely to be uncorrelated with the acceleration reference signals and
hence cannot be reliably eliminated. The raw ECG recordings are hence band pass
filtered and notch filtered before any further analysis. In addition, despite of the
careful observation of the subjectsโ movements, in some cases, the expected clean
channel was also corrupted by the motions, as will be shown in chapter 5. But this
would not be a serious problem after the signal preprocessing steps explained in
chapter 5. After all of the electrodes have been securely fixed and the equipment has
been switched on, every subject experienced a three-session experiment, each of
which lasted no longer than 15 minutes. Details of the steps involved in each of these
sessions are presented below,
Sessionone: Resting state ECG
The subjects were asked to remain seated and relax while one minute of their two
channels resting state ECG were recorded.
Sessiontwo: Shaker vibration
The subjects were asked to grasp firmly but not too hard the added mass under the
horizontal handle with their right hand while the shaker was driven by the following
sequence of stimuli:
1. Two minutes (two sessions of one minute) of 15 Hz sinusoidal excitation.
2. Two minutes (two sessions of one minute) of bandlimited white noise excitation.
The two channels of ECG signals were recorded simultaneously during the shaker
vibrations. Limits on the permissible vibration intensities are 10 m/s2
for the
sinusoidal excitation and 20 m/s2
for the BLWN excitation.
Sessionthree: Voluntary motion
This session focused on introducing motion artefact from common daily activities.
The subjects were asked to take three books out of a bag placed in front of them and
then put the bag to another location neat them, mainly using the right hand. This type
of arrangement mimics those motions that are commonly carried out by students and
are sufficiently large to generate motion artefact. From the preliminary results on
these sets of recordings, the performances of adaptive methods were often observed to
be very poor. Hence in some cases, the subjects were also asked to randomly and
leisurely wave his right hand. Again, two channels of ECG signals and tri-axial
accelerations of the right hand were recorded during the subjectโs motion.
44. - 39 -
Chapter 5
Applicationof Adaptive Filters to ECG Motion
Artefact Reduction
5.1 Introduction
The aim of this chapter is to explore which method performs well under what
condition and to then explore whether the relationship between subjectโs motions and
resulting artefact can be modeled through the inclusion of nonlinearity. Outcomes of
applying adaptive methods to motion artefact reduction are provided. Before
experimentally recorded ECG signals were processed, computer simulations were
performed to review and validate some important properties of various adaptive filters.
This also provided useful guidance on method selection in real life situations. For
each type of movement involved in the actual experiment, both linear and nonlinear
structures and algorithms were applied to compare their performances and investigate
the motion artefact generation process.
To evaluate the effectiveness of different adaptive filters, a series of subjective and
objective measures were reviewed and discussed. More importantly, the number of
parameters each structure should contain was optimized according to the selected
quantitative criteria since it can have a considerable effect on model accuracy and
computational requirements but, surprisingly, is not emphasized in many previous
studies [3, 4, 39, 40].
The contents of this chapter are organized as follows. Firstly, signal preprocessing
methods are introduced. Next, results of computer simulations are presented, with
their significance and usages briefly summarized. It concludes with adaptive motion
artefact reduction in experimentally recorded ECG signals, performance evaluation
and parameter analysis.
45. - 40 -
5.2 Methods
This subsection commences with a description of the signal preprocessing methods
before adaptive filters were applied. Next, a brief description of different adaptive
filtering algorithms compared in this project is given. The section concludes with an
explanation of the approaches used for algorithm performance evaluation and
parameter number optimization.
5.2.1 Signal Preprocessing
Despite the great care taken during the experiments, it is still impossible to completely
avoid artefact arising from sources other than the subjectsโ motion. These types of
noise are normally uncorrelated with the reference signal and hence cannot be reduced
by adaptive methods. This can potentially lead to unjust interpretations of the
effectiveness of the proposed techniques and cause difficulties for performance
evaluation. Before any further analysis, the raw ECG and reference signal recordings
were preprocessed. As explained in section 2.3, ECG signals recorded from our
experimental settings tended to be corrupted by a slow time varying drift (baseline
wander) (Figure 5.1-A). Because of all the monitoring devices powered by alternating
current, especially the amplifier and the shaker, some of the ECG recordings and
acceleration signals were also disturbed by the 50 Hz mains frequency, as shown in
Figure 5.1-B and Figure 5.1-G. These two interferences can be decreased by a serial
combination of high pass filter and 50 Hz notch filter. This should be the preferred
approach since it preserves most of the important features of the original ECG signals
and motion artefact, while attenuating contributions from other sources. However, the
electrode on the subjectโs right hand, although placed on the bones, may also capture
high frequency EMG noise from the muscles in the hand. Electrical measurement
noise from the amplifier, the moving wires and the data acquisition system will also
contribute to this randomness and distortions. Thus, in addition to applying a 4th
order
Butterworth high pass filter with cut off frequency of 0.5 Hz and a 50 Hz IIR notch
filter to reduce the baseline wander and mains interference, a 4th
order 35Hz
Butterworth low pass filter was also applied in the forward and reverse direction,
introducing zero phase delay. The Biopac ECG amplifier module has inbuilt high pass
filters and 50Hz notch filter but only the 0.05 Hz high pass filter was applied in this
study.
The results of preprocessing a segment of raw resting state ECG are shown in Figure
5.1. The subject whose ECG was being recorded was sitting still and relaxed so that
motion artefact were kept at a minimum level, but the signal quality acquired was
very poor (Figure 5.1-A,B) due to the abovementioned reasons. The preprocessed
acquisitions show a much higher signal to noise ratio since 50 Hz noise has been
clearly attenuated and the baseline drift has been removed (Figure 5.1-C). Similar
improvements can be observed in the frequency domain (Figure 5.1-D). Power
spectra, estimated using the Welch method with window length of 1000 points and 50%
46. - 41 -
overlap, of preprocessed acceleration recordings are shown in Figure 5.1-E,F.
Comparing with Figure 4.10-H and Figure 4.11-H, only the peak at fundamental
frequency and first harmonic were preserved for the sinusoidal acceleration signal and
the mains frequency interference has been effectively attenuated for both types of
shaker vibrations.
(A) (B)
(C) (D)
(E) (F)
Figure (5.1) Signal preprocessing of a segment of raw ECG recording where no motion was involved.
(A) Raw ECG recording corrupted by baseline wander and 50 Hz mains frequency. (B)Power spectrum
estimate of corrupted ECG. (C) Time domain representation of preprocessed ECG signal after band
pass and 50 Hz notch filtering. (D) Power spectrum estimate of preprocessed ECG. (E) Power spectrum
of preprocessed sinusoidalacceleration and (F) Power spectrum of preprocessed BLWN acceleration.
0 1 2 3 4 5 6 7 8 9 10
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Time/s
Voltage/V
Raw ECG recording
0 10 20 30 40 50 60
-55
-50
-45
-40
-35
-30
-25
-20
Frequency/Hz
Power/Frequency(dB/Hz)
Welch Power Spectral Density Estimate
0 1 2 3 4 5 6 7 8 9 10
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Time/s
Voltage/V
Preprocessed ECG
0 10 20 30 40 50 60
-90
-80
-70
-60
-50
-40
-30
-20
Frequency/Hz
Power/Frequency(dB/Hz)
Welch Power Spectral Density Estimate
0 10 20 30 40 50 60
-70
-60
-50
-40
-30
-20
-10
0
10
20
30
Frequency/Hz
Power/Frequency(dB/Hz)
Power Spectrum of Raw and Preprocessed Acc
Raw Acc
Preprocessed Acc
0 10 20 30 40 50 60
-60
-50
-40
-30
-20
-10
0
10
Frequency/Hz
Power/Frequency(dB/Hz)
Power Spectrum of Raw and Preprocessed Acc
Raw Acc
Preprocessed Acc
47. - 42 -
Another issue which needs consideration is that the strength of ECG signal varies
between different positions of the subject, as well as the quality of electrical contact,
giving the chest and limb ECG distinct amplitudes. This will result in difficulties in
assessing the performances of adaptive methods as will be explained in later sections.
Linear regression of the preprocessed chest and limb ECGs was carried out to reduce
this difference in signal magnitudes and offset levels. A comparison of the pair of
ECG recordings before and after linear regression is shown in Figure 5.2-A,B. It
should be noted that before linear regression the chest ECG is much larger than the
limb ECG, but after the process, they are on the similar scale. Because of this, the
ECG in later parts of this thesis is denoted as having arbitrary units.
(A) (B)
Figure (5.2) Effects of linear regression. (A) ECG acquisitions before linear regression. (B) ECG
acquisitions after linear regression.The changes in the signal amplitudes should be noted.
5.2.2 Adaptive Motion Artefact Reduction
The configuration of adaptive motion artefact reduction in ECG signals used in this
project is shown in Figure 5.3. The motion artefact are generated from two sources:
the controlled vibration of the shaker transmitted through the right hand and voluntary
movements of the subjectโs right hand. The differences between various approaches
lie in the selected filter structure and the corresponding algorithm for parameter
update. Both linear and nonlinear adaptive methods were applied in the current study,
details of their mathematical backgrounds and properties can be found in Chapter 3.
Figure (5.3) Schematic of motion artefact reduction system using adaptive filters.
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
-0.5
0
0.5
1
1.5
2
Time/s
Voltage/V
Clean Chest ECG
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
-0.5
0
0.5
1
1.5
Time/s
Voltage/V
Clean Limb ECG
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
-0.5
0
0.5
1
Time/s
Voltage/V
Clean Chest ECG
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
-0.5
0
0.5
1
Time/s
Voltage/V
Clean Limb ECG
48. - 43 -
The adopted linear adaptive structures involved a transversal filter model and either
LMS or RLS coefficient estimation algorithm. Such a filter structure was chosen
mainly because of its simplicity, stability and effectiveness in adaptive noise reduction,
as has been proved in numerous previous studies [40, 41, 42, 43]. Their performances
and the adaptive algorithms had been initially validated using computer simulated
noisy ECGs and reference accelerations. This not only provided a brief review of the
important properties of linear adaptive methods, but also laid the foundation for
scheme selection and interpretation in practical situations. The application of linear
adaptive filters to experimentally acquired ECGs is presented next. The adaptation
coefficients or forgetting factors were selected individually instead of setting to the
same value for each type of motion and all subjects. The specific value was decided
by comparing the Normalized Cross Correlation (NCC) between the last eight
seconds of the reconstructed ECG and the clean ECG. The ones which resulted in the
highest degree of resemblance (visual inspection) were selected after a series of trials.
The nonlinear models investigated in this project were cascade models which include
LN (Linear-Nonlinear) or Wiener model, NL (Nonlinear-Linear) or Hammerstein
model and LNL (Linear-Nonlinear-Linear) or Wiener Hammerstein model. They were
chosen, instead of Volterra or Wiener series, because they provide a parsimonious
means of increasing the order of nonlinearity, which is especially important for
practical applications [46]. Furthermore, cascade models, subsets of Volterra series,
simplify the analysis and interpretation of model parameters as their nonlinear
polynomial elements remain in two dimensional space regardless of their order and
can be transformed into Volterra form, if so desired. Computer simulations to these
cascade models were not carried out because of time constraints. The outcomes of
nonlinear modeling the experimentally recorded limb ECGs were contrasted with
those of linear methods to check if the motion artefact can be better represented with
the additional nonlinearities.
5.2.3 Performance Evaluation and Parameter Number Optimization
The model order optimization process was often emphasized less in previous studies
but is of great practical importance as the parameter number has a considerable effect
on model performance and computation requirement for coefficients estimation.
Before the comparison of different filter structures, the number of parameters each
model contains should be chosen according to some evaluation standards. The
problem of determining how different adaptive approaches have performed in
experimentally recorded ECGs is not as straightforward as in computer simulations
where both the artefact and clean ECG are available. In simulations, the amount of
motion artefact reduction and the extent of signal morphology recovery can be exactly
quantified and compared using signal-to-noise-ratios(SNR)[56,57],root-mean-square
error[58,59] or cross correlation coefficient[59]. But practical difficulties exist in our
current experimental settings because of the following. The two channels ECG
recordings are never identical, even though located on the body with the aim of
measuring the same โECG leadโ. This is to be expected since the two pairs of
