MRFO Based LU-Net Approach and Sparsity-Assisted Signal Smoothing for ECG Signal Denoising

Abstract

Electrocardiographic (ECG) signals are vital for identifying and assessing cardiac problems. However, a variety of noises can contaminate ECG data, which affects the utility of ECG signals in application. Errors may be induced by patient movements, electromagnetic noise in surrounding devices, or muscle contraction artifacts. Traditional methods have often struggled with balancing effective noise reduction while preserving critical signal details, leading to compromised diagnostic accuracy. Various methods like adaptive filtering, wavelet methods, and EMD are used to denoise ECG signals to prevent noisy inference, but they may suffer with non-stationary noise or complex interference patterns. To address the aforementioned difficulties, an optimized deep learning approach and smoothing filter is designed for effectively increase the quality and reduce noise in the ECG signal. Initially, noisy ECG signals are obtained from the ECG heartbeat categorization dataset. The collected ECG raw signal is decomposed by the Multivariate dynamic mode decomposition (MDMD) technique for obtaining both high-frequency and low-frequency components of multivariate time-series data. Then, noise existing in both high frequency components is effectively removed by applying the LU-Net technique. Manta ray foreign optimization (MRFO) approach is utilized to select the learning rate and batch size of the LU-Net classifier in an optimal manner. The Integrate-and-Fire Time Encoding Machine (IF-TEM) method is used to reconstruct the denoised ECG signal. Signal sparsity assisted signal smoothing (SASS) approach is used to denoise and enhance the quality of ECG signal. The proposed MDLUTESS denoising method is compared with existing methods and its effectiveness is assessed using performance metrices like SNR, PSNR, MSE were 42, 53 dB, and 0.0017. Thus the proposed method successfully eliminates noise from the ECG signals.