Key result
The proposed multiresolution wavelet-based QRS complex detection algorithm achieved a global sensitivity of 99.87%, a positive predictivity of 99.79%, and a percentage error of 0.34%.
A novel multiresolution wavelet-based algorithm for QRS detection shows excellent performance with 99.87% sensitivity and 99.79% positive predictivity, even in cases of low signal-to-noise ratio and abnormal morphologies.
May aid QRS detection in noisy or abnormal ECGs; leaves open rigorous clinical validation before adoption.
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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Bouaziz et al. (2014) studied Arrhythmia. Multiresolution wavelet-based QRS complex detection algorithm was evaluated on QRS complex detection performance (sensitivity, positive predictivity, percentage error). The proposed multiresolution wavelet-based QRS complex detection algorithm achieved a global sensitivity of 99.87%, a positive predictivity of 99.79%, and a percentage error of 0.34%.
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