Sequential beat-to-beat P and T wave delineation and waveform estimation in ECG signals: Block Gibbs sampler and marginalized particle filter

Abstract

, et al.. Sequential beat-to-beat P and T wave delineation and waveform estimation in ECG signals: Block Gibbs sampler and marginalized particle filter. Signal Processing, Elsevier, 2014Elsevier, , vol. 104, pp. 174-187. <10.1016Elsevier, /j.sigpro.2014 For ECG interpretation, the detection and delineation of P and T waves are challenging tasks. This paper proposes sequential Bayesian methods for simultaneous detection, threshold-free delineation, and waveform estimation of P and T waves on a beat-to-beat basis. By contrast to state-of-the-art methods that process multiple-beat signal blocks, the proposed Bayesian methods account for beat-to-beat waveform variations by sequentially estimating the waveforms for each beat. Our methods are based on Bayesian signal models that take into account previous beats as prior information. To estimate the unknown parameters of these Bayesian models, we first propose a block Gibbs sampler that exhibits fast convergence in spite of the strong local dependencies in the ECG signal. Then, in order to take into account all the information contained in the past rather than considering only one previous beat, a sequential Monte Carlo method is presented, with a marginalized particle filter that efficiently estimates the unknown parameters of the dynamic model. Both methods are evaluated on the annotated QT database and observed to achieve significant improvements in detection rate and delineation accuracy compared to state-of-the-art methods, thus providing promising approaches for sequential P and T wave analysis.

Detection of T‐Wave Beat‐By‐Beat Variations prior to Ventricular Arrhythmias Onset in ICD‐Stored Intracardiac Electrograms: The Endocardial T‐Wave Alternans Study (ETWAS)

“…The processing consisted in two steps. First, a Bayesian model was used to estimate simultaneously the T‐wave location and waveform in each cardiac beat. Second, a set of 10 parameters was computed to characterize each detected T wave.…”

“…In brief, beat‐to‐beat T‐waveform estimation was performed using a Bayesian model to estimate on a beat‐by‐beat basis simultaneously with the T‐wave position, the T waveform, and the baseline. The used algorithm is a beat‐by‐beat version of the one previously detailed, which was originally designed for being applied to a bloc of successive beats (10–20 beats). This technique has been proven to be reliable in defining waveforms positions and morphologies on surface ECG with almost 100% sensitivity and specificity even in the presence of noise or wandering baseline since modeling provides a denoised estimation of each T wave.…”

Extraction and Analysis of the P Peak Morphology

“…The proposed algorithm was evaluated on the annotated QT database and compared with state-of-the-art methods. Its on-line characteristic is ideally suited for realtime ECG monitoring and arrhythmia analysis [13]. V.S.…”

Five Day Attachment ECG Electrodes for Longitudinal Bio-Sensing Using Conformal Tattoo Substrates

“…To our knowledge (see Section IV) there have been no previous systematic studies on the motion artefacts obtained using conformal electrodes. Combined with more sophisticated signal processing for R peak detection (particularly Kalman or particle filtering to eliminate incorrectly detected peaks [28]), or the collection of co-located artifact correlated signals (accelerometer or impedance) to remove the artifact using adaptive filter techniques already established for ECG signals [29], it is likely that a heart rate will be extractable during motion periods, but substantial algorithm development work will be required first.…”