A Comparison of Wavelet and Simplicity-Based Heart Sound and Murmur Segmentation Methods

Abstract

Stethoscopes are the most commonly used medical devices for diagnosing heart conditions because they are inexpensive, noninvasive, and light enough to be carried around by a clinician. Auscultation with a stethoscope requires considerable skill and experience, but the introduction of digital stethoscopes allows for the automation of this task. Auscultation waveform segmentation, which is the process of determining the boundaries of heart sound and murmur segments, is the primary challenge in automating the diagnosis of various heart conditions. The purpose of this thesis is to improve the accuracy and efficiency of established techniques for detecting, segmenting, and classifying heart sounds and murmurs in digitized phonocardiogram audio files. Two separate segmentation techniques based on the discrete wavelet transform (DWT) and the simplicity transform are integrated into a MATLAB software system that is capable of automatically detecting and classifying sound segments.The performance of the two segmentation methods for recognizing normal heart sounds and several different heart murmurs is compared by quantifying the results with clinical and technical metrics. The two clinical metrics are the false negative detection rate (FNDR) and the false positive detection rate (FPDR), which count heart cycles rather than sound segments. The wavelet and simplicity methods have a 4% and 9% respective FNDR, so it is unlikely that either method would not detect a heart condition. However, the 22% and 0% respective FPDR signifies that the wavelet method is likely to detect false heart conditions, while the simplicity method is not. The two technical metrics are the true v murmur detection rate (TMDR) and the false murmur detection rate (FMDR), which count sound segments rather than heart cycles. Both methods are equally likely to detect true murmurs given their 83% TMDR. However, the 13% and 0% respective FMDR implies that the wavelet method is susceptible to detecting false murmurs, while the simplicity method is not. Simplicity-based segmentation, therefore, demonstrates superior performance to wavelet-based segmentation, as both are equally likely to detect true murmurs, but only the simplicity method has no chance of detecting false murmurs.

A Comparative Phonocardiography Study: Two Wavelet Based Methods for Fetal Heart Sound Detection

“…Simplicity and especially the eigenvalue spectrum is another means to measure the complexity of a fPCG signal. The background noise and murmurs in PCG signals are characterized as high complex components while heart sounds as less complex components of the signals [5,7]. Low complexity implies high simplicity making the heart sounds separate from the background noise.…”

“…A WT-S based method was proposed by the D.Kumar et al in [5] for heart murmur segmentation while an Energy based and S based segmentation method for features computed from multi-level Wavelet decomposition coefficients was proposed by J.Vepa et al in [6]. Moreover J.Korver presented a study about the comparison of WT-S based heart sound and murmur segmentation methods in [7].…”