This document discusses techniques for removing movement artifacts from electrocardiogram (ECG) signals recorded from human subjects. It presents methods for capturing ECG signals both with and without introduced hand movement, as well as methods for signal processing and filtering artifacts using adaptive filters and assessing ECG signal quality pre- and post-filtering. The goal is to investigate the relationship between movement artifacts and motion and to remove artifacts without degrading the underlying ECG signal.

1. Removing Movement Artifacts
from ECG Signals Recorded
from Human Subjects
Eirini Nikolaou
Supervisors: Dr. David Simpson, Dr. Emiliano Rustighi
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2. Contents
 Introduction
 Purpose
 Background
 Methods
 Experimental Set-Up
 Signal Processing Methods
 Assessment Methods for the ECG’s
quality
 Summary
 References
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3. Introduction
 Electrocardiogram (ECG): Willem
Einthoven - 1903
Electrical potentials generated by the
heart’s function  amplified small
electrical signal
Movement artifacts or other sources.
Critical evaluation of cardiac health
depends on the ECG (e.g. Athletes,
Soldiers, Patients)
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4. Introduction
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Example of an ECG distorted by noise from electrical devices
and the electrodes’ connection to the skin.

5. Purpose
 Investigation of the relationship
between the movement artifact and
the movement itself
 Removal of those artifacts without
degrading the original ECG
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6. Background
 Adaptive noise cancelling: [1]
LMS algorithm
RLS algorithm
 Artifacts were introduced to the signal:
Physical activities (running, walking etc)
 Methods of assessing the quality of
the ECGs:
Coherence [2]
Mean Square Error
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7. Background
Previous Experimental Results [3],[4]:
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RLS
LMS

8. Methods
 Capturing a clean ECG
 ECG capture when hand/arm vibration
is introduced through shakers
 Signal Analysis – 3-axis
accelerometers for adaptive cancellers
 Filtering for removing artifacts
 Evaluation of quality of reconstructed
signal
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9. Experimental Set-Up
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Block Diagram of the Equipment
Used

10. Signal Processing Methods
Filtering – Using MATLAB
 Wiener Filters
 Adaptive Filters:
- Least Mean Square Algorithm
- Recursive Least Square Algorithm
 Nonlinear Filters
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11. Assessment Methods for the
ECG’s quality
 Qualitative and quantitative criteria
 Comparison with Baseline ECG
Chest electrodes, without large muscle
activity
 Averaged standard deviation
comparison
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12. Summary
 Techniques to remove ECG
movement artifacts:
Non adaptive
Adaptive
Nonlinear
 Artificial motion artifacts:
Shakers
1-axis and 3-axis accelerometers
 Assessment the ECG quality.
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13. References:
1. Milanesi, M. et al., 2006. Multichannel Techniques for
Motion Artifacts Removal from Electrocardiographic
Signals. New York, IEEE.
2. Carse, A., 2010. Removing Movement Artefacts in ECG
Signals from Human Subjects, Southampton: University of
Southampton.
3. Lee, J.-W. & Lee, G.-K., 2005. Design of an Adaptive Filter
with Dynamic Structure for ECG Signal Processing.
International Journal of Control, Automation and Systems,
Volume 3, pp. 137-142.
4. Dromer, O., Alata, O. & Bernard, O., 2009. Impedance
Cardiography Filtering using Scale Fourier Linear Combiner
based on RLS algorithm. 31st Annual International
Conference of the IEEE EMBS, 2-6 September, pp. 6930-
6933.
5. E. Nikolaou, Removing Movement Artifacts from ECG
Signals Recorded from Human Subjects, Literature Review,
Southampton: University of Southampton, 2012.
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14. Thank You!
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