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• W4386700716 abstract "Walking gait is an important function of people that many internal and external factors effect on gait parameters. Furthermore, cardiovascular diseases are one of the main threats to human life and account for about one third of deaths worldwide. The lack of study in evaluation relationship between cardiovascular factors and gait exist. In addition, the ECG signal is an important basis for the diagnosis of cardiovascular diseases. Therefore, removing ECG signal noise has become big challenge in medical and engineering fields [1]. Thakur et al. [2] used methods based on adaptive filtering and obtained promising results. In this research, an algorithm to remove the noise of ECG signals during gait movement is proposed, which uses wavelet transform, adaptive soft threshold contraction and modification of Donoho's formula. How can to ECG noise remove using wavelet transform during the gait? The proposed algorithm is tested with ECG signals (MIT-BIH arrhythmia database) with standard Gaussian noise added. The signal noise removal algorithm proposed in this paper is based on the method of Johnston and Donoho [3]. It has been proven that this method has been successfully applied to a wide class of non-stationary signals. Thresholding is used to smooth or remove some precise wavelet coefficients of the original signal. Then the noise-free signal obtained in the time domain is reconstructed using modified coefficients. Determining the threshold is a very important issue, because contraction significantly affects the quality of ECG morphology. Then the denoising problem is formulated to find the optimal wavelet basis functions and the optimal threshold for the noise signal. The main studied ECG signal (with the number of sample points N=1024) is shown in Fig. 1(a) and the ECG noise signal is shown in Fig. 1(b). DWT is applied to the noisy signal at all possible levels of decomposition (scale). After determining the threshold value, the detail coefficients of the wavelet are filtered (with a soft adaptive threshold). The inverse DWT is applied to the approximate coefficients and the resulting details, and the estimate of the removed signal is obtained. The reconstructed ECG signal without noise is shown in Fig. 1(c), the noise extracted from the ECG signal using the traditional adaptive soft algorithm based on wavelet contraction, in Fig. 1(d) and the noise extracted from the ECG signal using the new adaptive algorithm based on threshold The wavelet is shown in Fig. 1(e).Download : Download high-res image (158KB)Download : Download full-size image Fig. 1: The steps of the ECG noise removal using wavelet transform during the gait Discrete wavelet transform allows the successful removal of non-stationary electrocardiographic signals. The obtained results show that the proposed threshold for removing ECG noise is more suitable than the Donoho threshold and can be successfully applied in the processing of ECG signals." @default.
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• W4386700716 date "2023-09-01" @default.
• W4386700716 modified "2023-09-27" @default.
• W4386700716 title "ECG noise removal using wavelet transform during the gait" @default.
• W4386700716 doi "https://doi.org/10.1016/j.gaitpost.2023.07.163" @default.
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