ECG Wave Segmentation Algorithm for Complete P-QRS-T Detection

Electrocardiogram (ECG) is a simple and fast test that provides the graphical representation of the electrical signal obtained by the heart’s activity and is widely employed to detect functional heart conditions. Indeed, its segmentation and feature extraction are required to produce a diagnosis. This paper focuses on the development of a segmentation technique for QRS complex, T waves and P waves of ECG waveforms that can be useful to detect cardiac diseases. Despite many proposed algorithms have already successfully accomplished the problem of QRS complex ECG segmentation, there is still a lack in the performance of these detectors on P and T waves. The proposed algorithm is based on a threshold calculated using the Otsu’s method to detect R-peaks in the ECG waveform. In addition, it allows to segment all the other ECG waves using wavelet filters and local maxima algorithm. Each segmented wave is defined with onset and offset. This the method is evaluated on ECG signals acquired with a single lead heart rate monitor verifying if a wave and its onset and offset are detected or not and using mean time error and IOU (Intersection Over Union) as evaluation metrics. Experimental results presented in this paper demonstrate that the proposed algorithm achieves 100 % of R-peak and QRS complex detection for the considered dataset. In addition, also P peak and T peak are detected with a score of 100 %.