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| Main Author: | |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Universidad Nacional Autónoma de México
2015
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| Online Access: | https://www.redalyc.org/articulo.oa?id=47439895012 |
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Table of Contents:
- Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis R. Rodríguez A. Mexicano J. Bila S. Cervantes R. Ponce Ingeniería hilbert transform Adaptive threshold electrocardiogram signals 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 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. 2015 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47439895012 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.2 Vol.13