Automatic methods for the analysis and recognition of the Electrocardiogram of the electrocardiogram

Abstract

Cardiac diseases rank first in the cases of death all over the world; Electrocardiogram (ECG) bears valuable information about the person health state. Therefore, ECG became a standard tool for heart disease exploration. Beats segmentation is a necessary step before disease type identification. The segmentation is based on the QRS detection. In this thesis, we proposed three different methods for ECG segmentation. First, an optimized Pan-Tompkins algorithm is developed, in which the parameters of the benchmark algorithm are optimized using the particle swarm optimization (PSO). Second, the QRS is detected in the time-scale domain; the stationary wavelet transform is applied to the filtered ECG signal to enhance the QRS wave, and then thresholding is carried out to extract the wanted signal. Finally, a machine learning technique is used to identify the QRS. In particular, a deep learning autoencoder is trained by standard datasets for the purpose of QRS detection