A simple proposition for heart sound signal de-noising for effective components identification in normal and abnormal cases

Abstract

Mass scale, simple, efficient, minimum infrastructure-based engineering approach with an inbuilt detection capability of Heart Sound Signal (HSS) can save many lives due to cardiovascular diseases in a rural area of any developing country lacking super medical facilities and expert medical practitioners. De-noising of HSS is a crucial step for reliable and accurate identification sound components such as S1, S2, and others as HSS gets easily corrupted with random internal and external noise sources. In recent years, Wavelet-based filtering methods are the most common for de-noising of HSS for which selection of correct wavelet, level of decomposition, the best threshold selection are crucial for which excellent domain expertise is inevitable. Though these methods provide excellent returns in high-frequency noise/murmurs elimination, low-frequency noise/murmurs in abnormal HSS cannot be detected adequately as the noise/murmur spectrum overlaps with HSS component. This paper introduces a simple but very effective Power Law Algorithm (PLA) that performs de-noising in the time domain for efficient Heart Sound component identification both in normal and abnormal cases. A comparative study with qualitative (based on the visual parameter) and quantitative analysis (based on SNR and FIT coefficient) on de-noised HSSs has been performed that clearly shows PLA based method improves quality components identification than using wavelet thresholding. Further, time-domain feature parameters and power spectral density representation of the PLA-based de-noised signal exhibits a definite indication of the algorithm's efficacy towards heart sound classification both for Normal or Abnormal classes.