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

Fatiha Bouaziz; Daoud Boutana; Messaoud Benidir

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

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Author(s): Fatiha Bouaziz ^{1, 2} ; Daoud Boutana ¹ ; Messaoud Benidir ³
- Affiliations: 1: Electronic Department, Jijel University, BP98 Ouled Aissa, Jijel 18000, Algeria;
  2: Mechatronic Laboratory (LMT), Jijel University, Jijel, Algeria;
  3: Laboratory of signals and systems (L2S), UMR8506 CNRS – SUPELEC, Paris-Sud University, Paris, France
Source: Volume 8, Issue 7, September 2014, p. 774 – 782
DOI: 10.1049/iet-spr.2013.0391 , Print ISSN 1751-9675, Online ISSN 1751-9683

Received 01/10/2013, Accepted 20/02/2014, Revised 03/02/2014, Published 28/05/2014

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%.

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

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