At last, extracted feature classification is done via GWO-MSVM, SVM, Adaboost, ANN and Naive Bayes classifier to classify the ECG signal database into normal or abnormal ECG signal. The peak points are detected by peak detection algorithm, and the signal features are extracted using statistical parameters. From signal, the excess clamour is sliced by Butterworth filter. The preprocessing stage includes filtering of input signal via low pass, high pass including Butterworth filter in order to remove clamour of high frequency. The paper proposes strategy classifying ECG signal using various technique. The information is utilized to analyze abrupt cardiac function like arrhythmias and conduction disturbance. The electrocardiogram (ECG) signal is a method that uses electrodes to record cardiac rates along with sensing minute electrical fluctuations for each cardiac rate.
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