Tehnički vjesnik, Vol. 32 No. 2, 2025.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20240728001884
Enhanced Motion Artifact Mitigation in ECG Signals using Nonlinear Autoregressive Networks with Cat Swarm Optimization and Fractional Calculus
Ibrahim Almohimeed
orcid.org/0000-0003-2054-6134
; College of Applied Medical Sciences, Majmaah University, Department of Medical Equipment Technology, Al Majmaah, Saudi Arabia
Ahmed Farag Salem Babetat
; Dr. Mohammad Alfagih Hospital, Riyadh
Mohammed Nasser Almraikhi
; Internship Unit, College of Medicine, Majmaah University, Al Majmaah, Saudi Arabia
Mohamed Yacin Sikkandar
orcid.org/0000-0002-7971-1504
; College of Applied Medical Sciences, Majmaah University, Department of Medical Equipment Technology, Al Majmaah, Saudi Arabia
*
Bader Dhaidan Owayyid Owayjah
; Internship Unit, College of Medicine, Majmaah University, Al Majmaah, Saudi Arabia
Ali Abdullah Almukil
; College of Applied Medical Sciences, Majmaah University, Department of Medical Equipment Technology, Al Majmaah, Saudi Arabia
Abdulrhman Abdulaziz Almajhad
; Clinical Skills Center Unit, College of Medicine, Majmaah University, Al Majmaah, Saudi Arabia
* Dopisni autor.
Sažetak
The Motion artifacts in ECG data can lead to inaccurate circulatory state analysis. Recently, there has been growing interest in methods to mitigate motion distortion in ECG signals. One major challenge is to reduce motion artifacts without affecting the underlying ECG signal, as the motion artifacts and the ECG signal often overlap. Adaptive noise cancellers have proven effective in reducing motion artifacts, provided that an appropriate noise reference, which correlates with the noise in the ECG signal, is available. However, the correlation between motion distortion and the noise reference is not always consistent, and using an inappropriate noise reference can contaminate the ECG data. In this research, we present NARX_CSOFC, an advanced approach that significantly enhances the capabilities of the nonlinear autoregressive network with exogenous inputs (NARX) architecture by incorporating nonlinear combinations of input variables to globally estimate any nonlinear function. The method employs cat swarm optimization with fractional calculus (CSOFC) to find optimal solutions. The proposed NARX_CSOFC demonstrates substantial improvements in artifact reduction: achieving a 12 dB improvement for ECG artifacts, a 16 dB improvement for EMG artifacts, and a 15 dB improvement for random noise artifacts, as measured by Signal-to-Noise Ratio (SNR) and Mean Squared Error (MSE) compared to existing techniques.
Ključne riječi
cat swarm optimization; ECG signal processing; fractional calculus; motion artifacts; nonlinear autoregressive network
Hrčak ID:
328576
URI
Datum izdavanja:
27.2.2025.
Posjeta: 852 *