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IET Wireless Sensor Systems

Micro & Nano Letters

The Journal of Engineering

Multiresolution wavelet-based QRS complex detection algorithm suited to several abnormal morphologies

Content Provider	IET Digital Library
Author	Bouaziz, Fatiha Boutana, Daoud Benidir, Messaoud
Abstract	The electrocardiogram (ECG) signal is considered as one of the most important tools in clinical practice in order to assess the cardiac status of patients. In this study, an improved QRS (Q wave, R wave, S wave) complex detection algorithm is proposed based on the multiresolution wavelet analysis. In the first step, high frequency noise and baseline wander can be distinguished from ECG data based on their specific frequency contents. Hence, removing corresponding detail coefficients leads to enhance the performance of the detection algorithm. After this, the author's method is based on the power spectrum of decomposition signals for selecting detail coefficient corresponding to the frequency band of the QRS complex. Hence, the authors have proposed a function g as the combination of the selected detail coefficients using two parameters λ 1 and λ 2, which correspond to the proportion of the frequency ranges of the selected detail compared with the frequency range of the QRS complex. The proposed algorithm is evaluated using the whole arrhythmia database. It presents considerable capability in cases of low signal-to-noise ratio, high baseline wander and abnormal morphologies. The results of evaluation show the good detection performance; they have obtained a global sensitivity of 99.87%, a positive predectivity of 99.79% and a percentage error of 0.34%.
Starting Page	774
Ending Page	782
Page Count	9
ISSN	17519675
Volume Number	8
e-ISSN	17519683
Issue Number	Issue 7, Sep (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/8/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2013.0391
Journal	IET Signal Processing
Publisher Date	2014-05-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Abnormal Morphology Arrhythmia Database Bioelectric Signal Biology And Medical Computing Decomposition Signal Digital Signal Processing Discrete Wavelet Transform ECG Data ECG Signal Electrical Activity in Neurophysiological Processes Electrocardiogram Signal Electrocardiography Electrodiagnostics And Other Electrical Measurement Technique Function Theory And Analysis High Frequency Noise Integral Transforms in Numerical Analysis Medical Signal Detection Multiresolution Wavelet Analysis Patient Cardiac Status Power Spectrum Method QRS Complex Detection Algorithm Signal Detection Signal to Noise Ratio
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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