Publication | Closed Access
A Comparative Approach to ECG Feature Extraction Methods
25
Citations
22
References
2012
Year
Unknown Venue
Electrophysiological EvaluationEngineeringComparative ApproachPattern RecognitionElectrocardiographyMedicineBiosignal ProcessingWearable TechnologyFeature ExtractionSignal ProcessingBiostatisticsElectrophysiologyIndependent Component AnalysisTimefrequency AnalysisFrequent MethodsEcg Feature ExtractionCardiologyWaveform Analysis
This paper discusses six most frequent methods used to extract different features in Electrocardiograph (ECG) signals namely Autoregressive (AR), Wavelet Transform (WT), Eigenvector, Fast Fourier Transform (FFT), Linear Prediction (LP), and Independent Component Analysis (ICA). The study reveals that Eigenvector method gives better performance in frequency domain for the ECG feature extraction.
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