Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis

 

R. Rodríguez^a*, A. Mexicano^b, J. Bila^c, S. Cervantes^d, R. Ponce^b

 

^a Department of Mechatronics, Technological University of Ciudad Juarez, Ciudad Juárez, Chihuahua, México. *Corresponding author. E-mail address: ricardo_rodriguez@utcj.edu.mx

^b Postgraduate Studies and Research Division, Technological Institute of Ciudad Victoria, Ciudad Victoria, Tamaulipas, México.

^c Department of Instrumentation and Control Engineering, Czech Technical University in Prague, Prague, Czech Republic.

^d Faculty of Chemical Sciences and Engineering, Autonomous University of the State of Morelos, Cuernavaca, Morelos, México.

 

Abstract

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.

Keywords: Adaptive threshold; Hilbert transform; Principal Component Analysis; Electrocardiogram signals.

 

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Acknowledgements

This work was supported by PROMEP, grant No. PROMEP/103.5/13/9045, and by Technological University of Ciudad Juarez.

 

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