ECG Signal Denoising Using Optimized Adaptive Hybrid Filter With Empirical Wavelet Transform

Abstract

Cardiovascular diseases (CVDs) are the world's leading cause of death; therefore cardiac health of the human heart has been a fascinating topic for decades. The electrocardiogram (ECG) signal is a comprehensive non-invasive method for determining cardiac health. Various health practitioners use the ECG signal to ascertain critical information about the human heart. In this article, swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and Empirical wavelet Transforms (EWT). At first, the white Gaussian noise is added to the input ECG signal and then applied to the EWT. The ECG signals are denoised by the proposed Adaptive hybrid filter. The Honey Badge Optimization (HBO) algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters. The experiments are conducted on the MIT-BIH dataset and the proposed filter built using the HBO algorithm, attains a significant enhancement in reliable parameters, according to the testing results in terms of signal-to-noise ratio (SNR), mean difference (MD), mean square error (MSE), normalized root mean squared error (NRMSE), peak reconstruction error (PRE), maximum error (ME), and normalized root mean error (NRME) with existing algorithms namely, PSO, AOA, MVO, etc.