Robust automatic detection of P wave and T wave in electrocardiogram

The Electrocardiogram (ECG) is a significant tool to investigate the electrical activity of the heart. Even though P and T waves reveal very useful information, their accurate detection is a difficult and challenging task on account of noise, baseline drift and odd morphologies. The need for a rapid and effective detection algorithm motivated us to propose two automated methods: the percentile based Automatic Detection (pAD) and the Graphical based Automatic Detection (gAD). Both algorithms use statistical and probabilistic concepts to achieve adequate delineation and detection of the waves. The former uses the percentile as an adaptive threshold to define the location of these waves. The latter uses a "feature wave-bank" to train a graphical probabilistic model named as Hidden Conditional Random Field (HCRF). The gAD algorithm takes advantage of the monotonicity and the slope of an ECG to detect and collect waves, which imports to the graphical model and classifies them to P or T waves. The efficiency of our proposed algorithms has been evaluated on 10 long-term (24-hour) ECG recordings of MIT-BIH Normal Sinus Rhythm Database. The training set we used for gAD was very small, only the 0.2% of the total number of the available waves. The results show a significant and promising detection accuracy rate.