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A Study on: ECG Feature Extraction Technique


Bincy Issac , ChristKnowledgeCity; Sandya Venugopal, ChristKnowledgeCity


ECG, ANN, Support Vector Machine (SVM), Venticular Fibrillation (VF)


The cardiogram is nothing however the recording of the heart’s electrical activity. ECG Feature Extraction plays a significant role in diagnosis most of the internal organ diseases. One cycle in associate cardiogram signal consists of the P-QRS-T waves. This feature extraction theme determines the amplitudes and intervals within the cardiogram signal for future analysis. The amplitudes and intervals price of P-QRS-T segment determines the functioning of heart of each human. Recently, various analysis and techniques are developed for analyzing the cardiogram signal. The planned schemes were largely supported symbolic logic Methods, Artificial Neural Networks (ANN), Genetic formula (GA), Support Vector Machines (SVM), and alternative Signal Analysis techniques. Of these techniques and algorithms have their blessings and limitations. This paper discusses various techniques and transformations planned earlier in literature for extracting feature from associate cardiogram signal. Additionally this paper conjointly provides a comparative study of varied ways proposed by researchers in extracting the feature from cardiogram signal.

Other Details

Paper ID: IJSRDV4I120023
Published in: Volume : 4, Issue : 12
Publication Date: 01/03/2017
Page(s): 104-106

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