Abstract
ECG is a graphical representation of heart’s electrical activity such as electrical reploarization and depolarization of heart. It is an important non- stationary signal which contains the necessary information about the heart functioning so that it can be used to identify different abnormalities in heart beats and also to identify different diseases of human beings. Classification is an important process in ECG signal analysis and cardiac diseases diagnosis process. Different ECG signals as well as ECG parameters such as heart beats, features can be classified according to requirement. In this paper different classification networks have studied. SVM classifier with empirical mode decomposition represented the maximum accuracy of 99.54%. Any optimization technique can be used to increase the accuracy of SVM classifier with suitable decomposition method such as variatinal mode decomposition.
Highlights
An electrocardiography (ECG) is a graphic tracing of the electric current generated by the heart muscle during a heartbeat
The ECG signal is characterized by six peaks and valleys, which are traditionally labelled P, Q, R, S,T and U as shown in figure 1.[1]
Different classifier networks like Decision rule based algorithm, [2] general regression neural network (GRNN), [5] Support Vector Machine (SVM), [8, 9, 12, 18, 21,22,23, 26] Random forest, [11] back propagation, [13] K-nearest neighbour (KNN), [14] artificial neural network (ANN), [15] Neighbor Search (IBK), [16] convolution neural network-dual input CNN-2D [17] have been used by the researchers
Summary
An electrocardiography (ECG) is a graphic tracing of the electric current generated by the heart muscle during a heartbeat. It provides information regarding the function of the heart. The ECG signal is characterized by six peaks and valleys, which are traditionally labelled P, Q, R, S,T and U as shown in figure 1.[1]
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