A robust sliding window adaptive filtering technique for phonocardiogram signal denoising

Objective The digital sound recording of several heart sounds (HSs) is named phonocardiogram (PCG) signal. Analysing these PCG signals is essential for diagnosing diverse sorts of heart disorders. Nevertheless, owing to troubling surrounding noise signals, PCG signal recording is challenging. So, before utilising PCG for advanced processing, de‐noising the PCG signal is executed. A new sliding window adaptive noise cancellers (SWANC)‐centred filter technique is proposed in this paper for de‐noising along with recovering the PCG signal effectively. Method Utilising the least mean square (LMS), an SW optimum adaptive filter (AF) structure is introduced in this work for estimating a De‐noised signal (DS) with better accuracy. Here, via the SWAF stage, a noisy signal is processed. An SW of fixed duration slides over the signal; also, the signal is filtered utilising the AF in each window. Utilising this SWAF architecture, this method approximates the PCG signal's clean version with better accuracy. Results The proposed robust SWAF is analogized to experimental PCG signals that were corrupted by Gaussian noise (GN) together with pink noise (PN) with distinct noise levels (NLs). From the physionet database, the experiential data are acquired. The outcomes exhibited that a remarkable performance was attained by the robust SWAF model. Discussion A 2–10 times decrease in MSE values was attained by the proposed filter structure when analogized with conventional LMS filter configuration. Further, the SNR is improved by 3 times and comparatively, the PSNR enhancement was 4%–25%. The association betwixt the clean signal along with its estimate is greater than 0.92. Conclusion A cost‐efficient hardware installation of ANC with higher accuracy was offered by the SW LMS AF model. For obtaining desired convergence speed and accuracy, such models are tested for real‐time performances in the future.