Abstract
The aim of this paper is a removal of noise from electrocardiogram (ECG) signals. A dual unscented Kalman filter based on multilayer perceptron (MLP) has been proposed for removing the artificial white, colored Gaussian noises and non-stationary muscle artifact from ECG signals. The (MLP) is used as the nonlinear functional form of the unknown model. Dual Unscented Kalman filter (UKF) is used for the part of the algorithm that estimates the clean state and the weights of the network. The obtained results are compared with other enhancement conventional filters, such as, normalized least mean square (NLMS) and Butterworth filter (BF). The quantitative study of output of the different methods has been presented based on mean squared error (MSE), signal to noise ratio (SNR) and peak signal to noise ratio (PSNR). By considering these parameters, the experimental comparative analysis has shown that the UKF-MLP had optimal performance and capability than conventional filters for denoising ECG signal.
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Gaamouri, S., Bousbia-Salah, M., Hamdi, R. (2020). Performance Study of Neural Network Unscented Kalman Filter for Denoising ECG Signal. In: Bouhlel, M., Rovetta, S. (eds) Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.2. SETIT 2018. Smart Innovation, Systems and Technologies, vol 147. Springer, Cham. https://doi.org/10.1007/978-3-030-21009-0_2
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