Open AccessFeature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis
Author(s) -
Ricardo Rodríguez,
Adriana Mexicano,
Jiří Bíla,
Salvador Cervantes,
Rafael Ponce
Publication year - 2015
Publication title -
journal of applied research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 30
ISSN - 1665-6423
DOI - 10.1016/j.jart.2015.06.008
Subject(s) - principal component analysis , pattern recognition (psychology) , artificial intelligence , hilbert transform , qrs complex , computer science , feature extraction , signal (programming language) , license , sensitivity (control systems) , filter (signal processing) , speech recognition , engineering , computer vision , medicine , electronic engineering , cardiology , programming language , operating system
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