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Detection of atrial fibrillation using continuous wavelet transform and wavelet coherence

Detection of atrial fibrillation using continuous wavelet transform and wavelet coherence Atrial fibrillation (AF) is a type of heart ailment that occurs when atria beats quicker than normal to move blood from atria to the ventricles. Our present study proposes a technique to detect AF ECG patterns with the use of continuous wavelet transform (CWT), wavelet coherence (WTC) is presented. The wavelet coherence function finds common frequencies between two signals and evaluates similarity of the two signals. The mother wavelet used is db4. The ECG variation of atrial fibrillation (AF) is observed in lead II of ECG. For the detection of normal and AF beats, WTC output values are given as the input features for the Levenberg-Marquardt neural network (LMNN) classifier. The data was collected from MIT/BIH AF database. Keywords: continuous wavelet transform; CWT; wavelet coherence; WTC; AF beats; LMNN classifier; MIT/BIH AF database. Reference to this paper should be made as follows: Padmavathi, K and Sri Ramakrishna, K. (2015) `Detection of atrial fibrillation using continuous wavelet transform and wavelet coherence', Int. J. Systems, Control and Communications, Vol. 6, No. 4, pp.292­304. Biographical notes: Kora Padmavathi is currently working in ECE Department as an Associate Professor in Gokaraju Rangaraju Institute of Engineering and Technology (GRIET), Hyderabad. She has completed http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Systems, Control and Communications Inderscience Publishers

Detection of atrial fibrillation using continuous wavelet transform and wavelet coherence

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Publisher
Inderscience Publishers
Copyright
Copyright © 2015 Inderscience Enterprises Ltd.
ISSN
1755-9340
eISSN
1755-9359
DOI
10.1504/IJSCC.2015.072519
Publisher site
See Article on Publisher Site

Abstract

Atrial fibrillation (AF) is a type of heart ailment that occurs when atria beats quicker than normal to move blood from atria to the ventricles. Our present study proposes a technique to detect AF ECG patterns with the use of continuous wavelet transform (CWT), wavelet coherence (WTC) is presented. The wavelet coherence function finds common frequencies between two signals and evaluates similarity of the two signals. The mother wavelet used is db4. The ECG variation of atrial fibrillation (AF) is observed in lead II of ECG. For the detection of normal and AF beats, WTC output values are given as the input features for the Levenberg-Marquardt neural network (LMNN) classifier. The data was collected from MIT/BIH AF database. Keywords: continuous wavelet transform; CWT; wavelet coherence; WTC; AF beats; LMNN classifier; MIT/BIH AF database. Reference to this paper should be made as follows: Padmavathi, K and Sri Ramakrishna, K. (2015) `Detection of atrial fibrillation using continuous wavelet transform and wavelet coherence', Int. J. Systems, Control and Communications, Vol. 6, No. 4, pp.292­304. Biographical notes: Kora Padmavathi is currently working in ECE Department as an Associate Professor in Gokaraju Rangaraju Institute of Engineering and Technology (GRIET), Hyderabad. She has completed

Journal

International Journal of Systems, Control and Communications – Inderscience Publishers

Published: Jan 1, 2015

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