Publications Scientifiques
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Item A deep neural network approach to QRS detection using autoencoders(Elsevier, 2021) Belkadi, Mohamed Amine; Daamouche, Abdelhamid; Melgani, FaridObjective: In this paper, a stacked autoencoder deep neural network is proposed to extract the QRS complex from raw ECG signals without any conventional feature extraction phase. Methods: A simple architecture has been deeply trained on many datasets to ensure the generalization of the network at inference. Results: The proposed method achieved a QRS detection accuracy of 99.6% using more than 1042000 beats which is competitive with all state-of-the-art QRS detectors. Moreover, the proposed method produced only 0.82% of Detection Error Rate using six unseen datasets containing more than 1470000 beats. Thus confirms the high performance of our method to detect QRSs. Conclusion: Stacked autoencoder neural networks are very effective in QRS detection. At inference, our algorithm processes 1042309 beats in less than 25.32 s. Thus, it is favorably comparable with state-of-the-art deep learning methods. Significance: The stacked autoencoder is an efficient tool for QRS detection, which could replace conventional systems to help practitioners make fast and accurate decisionsItem A robust QRS detection approach using stationary wavelet transform(Springer, 2021) Belkadi, Mohamed Amine; Daamouche, AbdelhamidAccurate QRS detection is crucial for reliable ECG signal analysis and the development of automatic diagnosis tools. In this paper, we propose a simple yet efficient new algorithm for QRS detection using the Stationary Wavelet Transform (SWT). The wavelet transform has been extensively exploited for QRS detection and proved to be an efficient mathematical tool for scale analysis; it provides good frequency components estimation for the input signal and has good localization capability. The proposed procedure exploits solely the first level approximation coefficients of the wavelet transform applied to the bandpass-filtered ECG signal. Therefore, it resulted in a reduced complexity algorithm compared to the existing methods which use many decomposition levels. Thresholding has been implemented using the Pan-Tompkins procedure which is known to be very powerful. Our approach has been assessed over the MIT/BIH benchmark database, the MIT noise stress test database for noise robustness evaluation and the European ST-T database. The obtained results show competitive performance with state-of-the-art algorithms. The proposed scheme achieved a sensitivity of 99.83%, a positive predictivity of 99.94% and a detection error rate of 0.228% using Lead I MIT-BIH Database, this performance is one of the best results over this benchmark, and 99.35% of sensitivity, 99.76% of positive predictivity and detection error rate of 0.9% using the European ST-T Database, hence, our algorithm achieved high performance on Holter environment. Using the MIT noise stress test database, our algorithm achieved 98.77% of sensitivity, 91.01% of positive predictivity, and 10.12% of DER. Thus, our algorithm is robust and outperforms state-of-the-art algorithms on noisy recordingsItem A new method for accurate QRS detection using stationary wavelet transform(Mohamed Amine Belkadi;, 2017) Belkadi, Mohamed Amine; Daamouche, AbdelhamidIt is well-known that the wavelet transform is a very useful mathematical tool for scale analysis, with very accurate frequency components estimation for the input signal. In this paper, we propose a new efficient method for QRS detection by employing the Stationary Wavelet Transform (SWT) also known as short wavelet transform. Our approach has been tested over MIT/BIH benchmark database. The obtained results are in a good agreement with the published works. Globally, we achieved a sensitivity of 99.733%, specificity of 99.922% and an error rate of 0.345% using Lead I ECGItem ECG as a biometric for individual's identification(IEEE, 2017) Sellami, Abdelkader; Zouaghi, Amine; Daamouche, AbdelhamidIn this paper, we investigate a new method to analyze electrocardiogram (ECG) signal, extract the features, for the real time human identification using single lead human electrocardiogram. The proposed system extracts special parts of the ECG signal starting from the P wave, the QRS complex and ending with the T wave for that we used the multiresolution wavelet analysis. Different features are selected and reconstructed from both amplitude and time interval of the ECG signal. The matching decisions are evaluated on the basis of correlation coefficient between the features and the Radial Basis function network classifier is introduced for validation and comparison. The performance evaluation was carried out on four ECG public databases with a total of 149 persons subjected to different physical activities and heart conditions, the preliminary results indicate that the system achieved an accuracy of 90-93%