Pseudo synchronization resampling to enhance T wave alternans detection

T wave alternans (TWA) detection and its corresponding time alignment algorithm are often confined by the phase fluctuation caused by heart rate variability (HRV). A pseudo synchronization resampling (PSR) method was presented to enhance the TWA detection in ECG data contaminated by HRV. Two important steps, instantaneous heart rate (IHR) estimation and ECG data resampling, were designed to build the PSR method which filtered the stretch effect of HRV. Synthesized data and real ECG data were provided to test the PSR method, and the number of invalid beats in alignment was used as test standard. The invalid beats in synthesized data vanished after PSR only with the simple absolute IHR estimation, and those in real ECG data reduced from 59.3% to 31.3% if the PSR with relative IHR was used. The results proved that the PSR operation could significantly improve the beats alignment and enhance T wave alternans detection.

[1]  R J Cohen,et al.  Electrical alternans and cardiac electrical instability. , 1988, Circulation.

[2]  R. Verrier,et al.  Modified moving average analysis of T-wave alternans to predict ventricular fibrillation with high accuracy. , 2002, Journal of applied physiology.

[3]  Juan Pablo Martínez,et al.  Methodological principles of T wave alternans analysis: a unified framework , 2005, IEEE Transactions on Biomedical Engineering.

[4]  W. Kinsner,et al.  Instantaneous heart rate: Should RR-intervals be resampled? , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Daniel Austin,et al.  Enhanced modified moving average analysis of T-wave alternans using a curve matching method: a simulation study , 2008, Medical & Biological Engineering & Computing.

[6]  G.D. Clifford,et al.  An open-source standard T-Wave alternans detector for benchmarking , 2008, 2008 Computers in Cardiology.

[7]  E. V. Garcia T‐Wave Alternans: Reviewing the Clinical Performance, Understanding Limitations, Characterizing Methodologies , 2008, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[8]  Gari D Clifford,et al.  Synthetic ECG generation and Bayesian filtering using a Gaussian wave-based dynamical model , 2010, Physiological measurement.

[9]  Hans-Georg Müller,et al.  Functional Data Analysis , 2016 .