Key result
The proposed adaptive hybrid filter optimized with the Honey Badger Optimization algorithm achieved higher signal-to-noise ratios (e.g., 83.65 for MIT-BIH ARR) compared to existing algorithms.
Why the study?
ECG signals are vital for determining cardiac health, motivating the use of swarm intelligence approaches to enhance adaptive hybrid filters and Empirical Wavelet Transforms for signal denoising.
Absolute Event Rate: 83.65% vs 78.154%
An optimized adaptive hybrid filter using the Honey Badger Optimization algorithm significantly improves ECG signal denoising compared to existing algorithms.
May improve ECG denoising in computational models; leaves open clinical validation before adoption.
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.
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Balasubramanian Subbiah (2022) studied ECG signal noise. Optimized Adaptive Hybrid Filter with Empirical Wavelet Transform and Honey Badger Optimization vs. Existing algorithms (PSO, AOA, MVO) was evaluated on Signal-to-noise ratio (SNR). The proposed adaptive hybrid filter optimized with the Honey Badger Optimization algorithm achieved higher signal-to-noise ratios (e.g., 83.65 for MIT-BIH ARR) compared to existing algorithms.
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