Model-Based ECG Fiducial Points Extraction Using a Modified EKF Structure

1. Model-Based ECG Fiducial Points Extraction Using a Modified EKF Structure Presented by: Omid Sayadi Biomedical Signal and Image Processing Lab (BiSIPL), Sharif University of Technology, Tehran, Iran

2. Contents: • Introduction and Problem Statement • Theoretical Background • Model-Based Approaches • Modified EKF Structure • Simulation and Results • Conclusion & Future Work

3. Introduction • Heart: a hollow muscular organ which through a coordinated muscle contraction generates the force to circulate blood throughout the body. • Electrocardiogram: a graph representing the electrical activity of heart, also called ECG. • 5 dominant characteristic waveforms and FPs, • Single/Multiple beat features, including: • Amplitude features, • Time intervals, • Wave durations.

4. Problem Statement • Arrhythmia Investigation, detection, diagnosis and treatment: • Ischemia • Sinus Bradycardia • Wolf Parkinson White • Branch Bundle Block (BBB) • Ventricular Tachycardia (VT) • Ventricular Bigeminy/Trigeminy • Atrial/Ventricular Flutter (AFL/VFL) • Premature Atrial Contraction (APC) • Atrial/Ventricular Fibrillation (AF/VF) • Premature Ventricular Contraction (PVC) • Major Problems: • Decision Dependency, • Variability, • Noise and Drifts, • Lack of sufficient morphological information.

5. Problem Statement • Goal: • Adaptive usage of the underlying ECG dynamical mechanism. • Accuracy achievement for Arrhythmia Investigation: • Beat Detection, • Beat Classification, • Fiducial Points Extraction, • Interval Timing Calculation, • Feature Generation.

6. Contents: • Introduction and Problem Statement • Theoretical Background • Model-Based Approaches • Modified EKF Structure • Simulation and Results • Conclusion & Future Work

7. Theoretical Background • ECG Dynamical Model (EDM):

8. Theoretical Background • EDM fit to an arbitrary ECG cycle: • A prior estimate of the 5 Gaussian functions • Nonlinear fit with Least Squares Error (LSE) • For an ECG waveform: • Cycle to Cycle fit.

9. Contents: • Introduction and Problem Statement • Theoretical Background • Model-Based Approaches • Modified EKF Structure • Simulation and Results • Conclusion & Future Work

10. Model-Based Approaches • Mathematical Nonlinear Modeling: • Least Square Error Fit: If we integrate the last equation of EDM, we conclude that: An Optimization Problem: where, s : Recorded ECG z : ECG generated by EDM

11. Model-Based Approaches • Adaptive Tracking: • Considering the nonlinear underlying dynamics for estimation → Extended Kalman Filter (EKF=linearized KF) • The discrete polar form of EDM: sampling period (discretization step) result of discrete derivation: random white noise which represents the baseline wander effects and models other additive sources of process noise

12. Model-Based Approaches • EKF formulation:

13. Contents: • Introduction and Problem Statement • Theoretical Background • Model-Based Approaches • Modified EKF Structure • Simulation and Results • Conclusion & Future Work

14. Modified EKF Structure • Remember the ECG Dynamical Model (EDM): • EKF2 (Sameni et al 2005) • ECG and wrapped Phase of ECG → states, • Gaussian parameters, angular frequency and baseline → noises,

15. Modified EKF Structure • EKF17 (Sayadi and Shamsollahi, IEEE TBME, 2008) • ECG, wrapped Phase and the Gaussian parameters → states, • Angular frequency, baseline and the associated noises to the Gaussian parameters model → noises, EKF2 EKF17 • Advantages: • GMM parameters are considered as the states, • Ability to reconstruct ECG (i.e. for compression tasks), • Ability to show the features related to the fiducial points.

16. Modified EKF Structure • AR(1) GMM parameters → Modified EKF (EKF17) • Process equations: • Observation equations:

17. Modified EKF Structure • Linearized state-space model at each time instant around the most recent state estimation:

18. Modified EKF Structure • Interpretation of GMM parameters of EDM: • FP extraction fluctuative parts of the estimations • Tachogram (RR-interval variability) extraction

19. Contents: • Introduction and Problem Statement • Theoretical Background • Model-Based Approaches • Modified EKF Structure • Simulation and Results • Conclusion & Future Work

20. Results • Estimated Gaussians’ parameters with EKF17 for record 231 (MIT-BIH database)

21. Results • Fiducial points extraction results for records 106 and 117: (MIT-BIH database)

22. Results • Numerical performance evaluation:

23. Contents: • Introduction and Problem Statement • Theoretical Background • Model-Based Approaches • Modified EKF Structure • Simulation and Results • Conclusion & Future Work

24. Conclusion • An EDM-based ECG fiducial points extraction scheme was proposed. In summary: • It is very simple, very precise and has a low computational cost, • It needs a non-accurate initial estimate for the KF, • The AR(1) models provides a simple dynamics for the newly introduced state variables (i.e. GMM parameters), • The modification is applied to the process, not the observations, • No thresholding is used in determination of FPs, • It uses the underlying dynamics for ECG signal, so it can be adapted to any ECG having five major PQRST waveforms, • There is an intrinsic denoising using the EDM, • The method guarantees adaptive tracking of the morphological characteristics of the ECG signal.

25. Future Work • Fitting the model to highly abnormal ECGs such as bundle blocks, • Modifications of the model: Using more than 5 Gaussians, • Modifications of the model: Using a lag-normal function, • Improving the method using more precise dynamics for the GMM parameters, instead of the AR(1), • Incorporating the effects of baseline drifts.

26. Thank You ☺