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Various techniques including Fuzzy Logic, Artificial Neural Networks, Genetic Algorithms, and Support Vector Machines have been proposed for ECG feature extraction, each with specific advantages and limitations.
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ECG Feature Extraction plays a significant role in diagnosing most of the cardiac diseases.One cardiac cycle in an ECG signal consists of the P-QRS-T waves.This feature extraction scheme determines the amplitudes and intervals in the ECG signal for subsequent analysis.The amplitudes and intervals value of P-QRS-T segment determines the functioning of heart of every human.Recently, numerous research and techniques have been developed for analyzing the ECG signal.For subsequent analysis of ECG signals its fundamental features like amplitudes and intervals are required which determine the functioning of heart.The proposed schemes were mostly based on Fuzzy Logic Methods, Artificial Neural Networks (ANN), Genetic Algorithm (GA), Support Vector Machines (SVM), and other Signal Analysis techniques.All these techniques and algorithms have their advantages and limitations.This proposed paper discusses various techniques and transformations proposed earlier in literature for extracting feature from an ECG signal.
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Sahay et al. (2015) conducted a review in Cardiac diseases. ECG Feature Extraction Techniques was evaluated. Various techniques including Fuzzy Logic, Artificial Neural Networks, Genetic Algorithms, and Support Vector Machines have been proposed for ECG feature extraction, each with specific advantages and limitations.
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