P and T wave analysis in ECG signals using Bayesian methods

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of the cardiovascular system, electrocardiography and some physiology notions. A literature
review of ECG processing methods is also presented. In Chapter 2, a multiple-beat processing
window based Bayesian model for P and T wave delineation and waveform estimation is
studied. Two Gibbs-type sampling methods (a partially collapsed Gibbs sampler and a block
Gibbs sampler) are proposed to generate samples distributed according to the posterior of the
proposed Bayesian model. The generated samples are used to estimate the unknown model
parameters and hyperparameters. The wave detection and delineation criteria based on the
posterior distributions are also presented. Simulation results performed on the standard annotated
QT database (QTDB) [LMGM97] as well as a comparison with other state-of-art methods
are also given. In the purpose of making the model more suitable for real-time applications,
a beat-to-beat Bayesian model for P and T wave delineation is proposed in Chapter 3. Gibbs
sampling methods and sequential Monte Carlo method (particle filters) are studied to estimate
the unknown parameters of the beat-to-beat model. A comparison with the window based approach
as well as other alternative methods on QTDB is reported. In Chapter 4, the window
based Bayesian model and the Gibbs sampling methods are first adapted to deal with TWA
detection in surface ECGs. Then, the beat-to-beat approach is applied on real intracardiac
electrograms (EGMs) provided by St. Jude Medical, Inc to deal with endocardial TWA
analysis.
The main contributions of this thesis are summarized below.
• Chapter 1. The physiological basis of the ECG is briefly introduced as four aspects: (1)
physiology of the specific structures of the heart, (2) electrophysiology of the heart and
the origin of the ECG, (3) ECG measurement and registration, (4) ECG interpretation in
clinical context. Based on the initiative of ECG computer-aided detection and computeraided
diagnosis, a brief review of the ECG signal processing methods proposed in the
literature is given, with the emphasis on P and T wave detection and delineation.
• Chapter 2. We introduce a new Bayesian model based on a multiple-beat processing
window which simultaneously solves the P and T wave delineation and the waveform
estimation problems. This model is based on a modified Bernoulli-Gaussian sequence with
minimum distance constraint [KTHD12] for the wave locations and appropriate priors
for the amplitudes, wave impulse responses and noise variance. A recently proposed
partially collapsed Gibbs sampler which exploits this minimum distance constraint is
adapted to the proposed model to estimate the unknown parameters [LMT10]. Then, a
modified version of this Bayesian model is proposed to consider the baseline within each
non-QRS component and to represent P and T waves by their respective dimensionality
reducing expansion according to Hermite basis functions. The local dependency of the
ECG signal is expressed by a block constraint. To alleviate numerical problems related
to the modified Bayesian model, a block Gibbs sampler is studied [LKT + 11, LTM + 11].
The proposed PCGS and block Gibbs sampler overcome the slow convergence problem
encountered with the classical Gibbs sampler. The resulting algorithms are validated
using the entire annotated QT database. A comparison with other benchmark methods
showed that the proposed Bayesian methods provide a reliable detection and an accurate
delineation for a wide variety of wave morphologies. In addition, the proposed Bayesian
methods can provide accurate waveform estimation and allow for the determination of
confidence intervals which indicate reliability information about the estimates.