Technological Analysis of ECG Classification based on Machine Learning and Deep Learning Techniques

The analysis and processing of the Electrocardiogram (ECG) reveals key information about the condition of the heart. The detection of the QRS wave segment in the ECG signal is crucial as it is the start of extracting relevant features for the classification of heart arrhythmia. This paper presents algorithms for each block in the typical structure of an ECG classification system with the recent researches, advantages and limitations of the presented algorithms. Some including adaptive filter, wavelet Transform ANN, GWO (Grey Wolf Optimization), deep residual Convolutional Neural Networks (DRCNN), SVM.