Adaptive Quantum-Inspired Evolution for Denoising PCG Signals in Unseen Noise Conditions

This study investigates a novel Quantum-inspired Evolutionary Algorithm (QiEA) for denoising phonocardiogram signals using a fractional-order digital differentiator FIR filter (FODD). The filter coefficients were optimised using the proposed QiEA with Adaptive Rotation Gate Operator (ARGO). The proposed algorithm accelerates convergence towards optimal solutions based on fitness feedback, improving filter optimisation while clamping rotation angles to maintain algorithm stability. The performance metrics utilised were Scale-Invariant Signal-to-Distortion Ratio (SI-SDR), Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Correlation Coefficient (CC) and Zero Crossing Rate (ZCR). The results demonstrate that the proposed algorithm achieved a 25% improvement in the SI-SDR metric in highly noisy environments for pathological PCG signals. Detailed comparative analyses were conducted for single and combined-noise models against current state-of-the-art Deep learning (DL) methods. It exhibited an increase in the SI-SDR metric in the presence of APGN, by 17% and 24% for single and combined-noise models, respectively. In the presence of AWGN, the output increased by 9% and 11%, respectively, for both models in highly noisy environments. To quantify suppression of noise, ZCR was evaluated for the PCG signals before and after denoising. ZCR reduced by 98.4% revealing removal of high frequency noise artifacts for both pathological and non-pathological signals. The results position the proposed algorithm as a data-efficient, noise-adaptable alternative solution critical in resource-constrained dynamic situations across unseen noise patterns. These improved results can be attributed to reliance on evolutionary strategies rather than learned weights. The findings presented in this paper can have a significant impact on healthcare monitoring systems and real-time processing in critical systems.