Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis

This paper presents a novel approach for QRS complex detection and extraction of electrocardiogram signals for different types of arrhythmias. Firstly, the ECG signal is filtered by a band pass filter, and then it is differentiated. After that, the Hilbert transform and the adaptive threshold techni...

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Bibliographic Details
Published inJournal of applied research and technology Vol. 13; no. 2; pp. 261 - 269
Main Authors Rodríguez, R., Mexicano, A., Bila, J., Cervantes, S., Ponce, R.
Format Journal Article
LanguageEnglish
Published Elsevier España, S.L.U 01.04.2015
Subjects
Adaptive threshold
Electrocardiogram signals
Hilbert transform
Principal Component Analysis
Hilbert transform
Electrocardiogram signals
Adaptive threshold
Principal Component Analysis
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ISSN1665-6423
DOI10.1016/j.jart.2015.06.008

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Summary:This paper presents a novel approach for QRS complex detection and extraction of electrocardiogram signals for different types of arrhythmias. Firstly, the ECG signal is filtered by a band pass filter, and then it is differentiated. After that, the Hilbert transform and the adaptive threshold technique are applied for QRS detection. Finally, the Principal Component Analysis is implemented to extract features from the ECG signal. Nineteen different records from the MIT-BIH arrhythmia database have been used to test the proposed method. A 96.28% of sensitivity and a 99.71% of positive predictivity are reported in this testing for QRS complexity detection, being a positive result in comparison with recent researches.
ISSN:1665-6423
DOI:10.1016/j.jart.2015.06.008