P and T wave analysis in ECG signals using Bayesian methods

General Introduction
The human heart, a magical muscular pump which is typically about the same size as a
fist, circulates blood through a human’s body all along their life. Despite the tremendous
efforts that have been dedicated to the cardiology study, the heart has not yet revealed all its
secrets. According to a fact sheet released by the World Health Organization in September
2011, cardiovascular diseases are still the number one cause of death globally [WHO11].
The electrocardiogram (ECG), a graphical representation of temporal differences in potential
electrical forces that lead to the cardiac muscle contraction, has been recognized as the
simplest non-invasive method of collecting electrical signal from the beating heart that provides
extremely useful information to the doctors. It all started when Augustus D. Waller published
the first human ECG recorded with a capillary electrometer in 1887. Subsequently, Willem
Einthoven, who received the Nobel Prize in 1924 for his development of the first lead system
for ECG recording, identified the five deflection points in the cardiac cycle by naming them P,
Q, R, S and T which are still being used in the present standards (see Fig. 1.7). Since then a
huge knowledge base has been generated covering clinical and engineering aspects of electrocardiography.
ECG analysis has become a routine part of any complete medical evaluation.
In the last few decades electronic recorders have been developed for digital recording of the
ECG signal and they are available in such a compact form that the user can wear them for
ECG recording without much of obstruction in the routine activities. Since a huge volume of
ECG data is generated by the wearable ECG recorders, automated methods are preferred for
analysis of the ECG signal. On the other hand, increasing comfort usually results in signals with
reduced quality. For instance, electrodes that are incorporated in garments generally provide
signals with a lower signal-to-noise ratio (SNR) and more artifacts than contact electrodes
directly glued to the body [GHM07]. Therefore, the development of efficient and robust signal
processing methods for ECG computer-aided detection (CADe) and computer-aided diagnosis
(CADx) is a subject of major importance.
Most of the clinically useful information in ECGs can be obtained from the intervals, amplitudes,
and wave shapes (morphologies). The QRS complex is the most characteristic waveform
of the ECG signal. Its high amplitude makes QRS detection easier than other waves. Thus,
it is generally used as a reference within the cardiac cycle. Algorithms for P and T waves
detection and delineation (determination of peaks and limits of the individual P and T waves)
usually begin with QRS detection. Search windows are then defined before and after the QRS
location to seek for the other waves. Finally, an appropriate strategy is used to enhance the
distinctive features of each wave in order to find its peaks and limits. Because of the low slope
and magnitude of P and T waves, as well as the presence of noise, interference, and baseline
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