A robust low-cost adaptive filtering technique for phonocardiogram signal denoising

Abstract

ObjectivePhonocardiogram (PCG) represents the recordings of various heart sounds. To diagnose the different ailments of the heart, it is required to analyze these PCG signals. However, recording PCG signals is challenging since it is prone to surrounding noise signals. Therefore, there is a need to denoise the PCG signal before being used for advanced processing. This paper proposes an Adaptive Noise Cancellers-based filter model for effectively denoising and recovering the PCG signal. MethodThis work introduces an optimum adaptive filter structure for estimating a noise-free signal with high accuracy using Least Mean Square (LMS) algorithm. A noisy signal is processed through multiple adaptive filter stages connected in series in the proposed work. Multiple stages are automatically added, and each stage filter’s step size process is dynamically changed. The estimate of clean PCG signal approximated using this multistage cascaded adaptive filter architecture is subsequently used in the next module to recover the clean PCG signal with high accuracy. ResultsThe proposed robust multistage adaptive filter is evaluated for denoising synthetic and experimental PCG signals corrupted by Gaussian and pink noise of various input Signal to noise (SNR) levels. The experimental data are taken from the physionet database (Classification of Heart Sound Recordings: The PhysioNet/Computing in Cardiology Challenge 2016). The results demonstrate that the robust multistage filter model performs remarkably well. DiscussionCompared with various filter configurations, the proposed filter structure achieves an 8-50% reduction in MAE values and the 45–87% reduction in MSE values. Further, there is an improved SNR of 15–60%, ANR of 15–65%, and PSNR improvement by 7–25% comparatively. The correlation between the clean signal and its estimate obtained using the proposed filter model is more than 0.99. ConclusionUsing an LMS adaptive filter in the proposed filter model offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy. In the future, the suggested robust multistage adaptive filter model can be tested for real-time performance when improved convergence speed and accuracy are desired.