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CLASSIFICATION OF ATRIAL FIBRILLATION PRONE PATIENTS USING ELECTROCARDIOGRAPHIC PARAMETERS IN NEURO-FUZZY MODELING

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method and results using neuro-fuzzy modeling in patient classification

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Mirela Ovreiu a PhD, Marc Petre a PhD, Daniel Simon b PhD, Daniel Sessler a MD, and C Allen Bashour a MD a The Cleveland Clinic Anesthesiology Institute Cleveland, Ohio, USA b Cleveland State University Electrical and Computer Engineering Department Cleveland, Ohio, USA CLASSIFICATION OF ATRIAL FIBRILLATION PRONE PATIENTS USING ELECTROCARDIOGRAPHIC PARAMETERS IN NEURO-FUZZY MODELING
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data & Methods TABLE 1 – Subject Demographics TABLE 2 – ECG parameters for model input 46-88 70 15 28 AF 23-88 range 59 average age (years) 23 female 32 male gender Control ms Inflection Point ms Duration P-wave scalar Approximate Entropy (ApEn) non-linear scalar Low Frequency Power normalized (LFn) scalar Low/High Frequency Ratio (LF/HF) mV Magnitude scalar Energy Ratio scalar Shape ms 2 Low Frequency Power (LF) ms 2 High Frequency Power (HF) ms 2 Total Power (TP) frequency ms 2 Root Mean Squared Differences between Adjacent NN intervals (RMSSD) ms 2 Standard Deviation of beat-to-beat intervals (SDNN) ms Mean beat-to-beat interval (NN) linear HRV #/min Premature Atrial Contractions/min Atrial ectopic activity Unit Parameter Domain
Data & Methods (continued) ,[object Object],. . . m 1 m k w 2 w 1 w k . . . Mean NN P - wave energy ratio i nput / fuzzification layer fuzzy rule base layer defuzzification/ output layer y AF classification index . . . PAC/min .
Results ,[object Object],[object Object],[object Object],[object Object],0 25 50 75 100 1 2 3 4 5 6 7 8 9 Time (hours prior to AF onset or end of registration) % sensitivity specificity accuracy FIGURE 2 – Historical Neuro-Fuzzy Classification Performances
Summary & Next Steps ,[object Object],[object Object],[object Object],FIGURE 3 - An overview of the proposed clinical monitoring system. Parameters derived in real-time from patient ECG signals will be interpreted by the neuro-fuzzy model which calculates a single parameter, the AF index. The index is then displayed on the patient monitor to indicate the patient’s current risk of going into AF to CVICU staff. Thank You!

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CLASSIFICATION OF ATRIAL FIBRILLATION PRONE PATIENTS USING ELECTROCARDIOGRAPHIC PARAMETERS IN NEURO-FUZZY MODELING

  • 1. Mirela Ovreiu a PhD, Marc Petre a PhD, Daniel Simon b PhD, Daniel Sessler a MD, and C Allen Bashour a MD a The Cleveland Clinic Anesthesiology Institute Cleveland, Ohio, USA b Cleveland State University Electrical and Computer Engineering Department Cleveland, Ohio, USA CLASSIFICATION OF ATRIAL FIBRILLATION PRONE PATIENTS USING ELECTROCARDIOGRAPHIC PARAMETERS IN NEURO-FUZZY MODELING
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  • 3. Data & Methods TABLE 1 – Subject Demographics TABLE 2 – ECG parameters for model input 46-88 70 15 28 AF 23-88 range 59 average age (years) 23 female 32 male gender Control ms Inflection Point ms Duration P-wave scalar Approximate Entropy (ApEn) non-linear scalar Low Frequency Power normalized (LFn) scalar Low/High Frequency Ratio (LF/HF) mV Magnitude scalar Energy Ratio scalar Shape ms 2 Low Frequency Power (LF) ms 2 High Frequency Power (HF) ms 2 Total Power (TP) frequency ms 2 Root Mean Squared Differences between Adjacent NN intervals (RMSSD) ms 2 Standard Deviation of beat-to-beat intervals (SDNN) ms Mean beat-to-beat interval (NN) linear HRV #/min Premature Atrial Contractions/min Atrial ectopic activity Unit Parameter Domain
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  • 6.