Optimal delineation of PCG sounds via false‐alarm bounded segmentation of a wavelet‐based principal components analyzed metric

Abstract

SUMMARYThe aim of this study is to describe a new false-alarm probability (FAP) bounded unified framework for segmentation of the phonocardiogram (PCG) signal sounds registered by an electronic stethoscope board. To meet this end, first the original PCG signal is pre-processed by application of an appropriate bandpass finite-duration impulse response (FIR) filter and then by implementation of à trous discrete wavelet transform (DWT) to the filtered signal for extracting several dyadic scales. Then, after choosing a proper scale, a fixed sample size sliding window is moved on the selected scale and in each slide, six feature vectors namely summation of the nonlinearly amplified Hilbert transform, summation of absolute first order differentiation, summation of absolute second-order differentiation, curve length, area and variance of the excerpted segment are calculated. Then, all feature trends are normalized and utilized to construct a newly proposed principal components analyzed geometric index (PCAGI) (to be used as the segmentation decision statistic (DS)) by application of a linear orthonormal projection. Next, using an adaptive smoothing filter (ASF), the obtained metric is modulated and freed from the fast fluctuations occurring in the vicinity of events onset and offset locations which consequently results in enhancement of edge detection accuracy. Later, histogram parameters of the filtered DS metric are used for the regulation of the -level Neyman-Pearson classifier for FAP-bounded delineation of the PCG events. To assess the performance quality of the proposed PCG segmentation algorithm, the method was applied to all 85 records of Nursing Student Heart Sounds database including stenosis, insufficiency, regurgitation, gallop, septal defect, sound split, rumble, murmur, clicks, friction rub and snap disorders with different sampling frequencies. The method was also applied to the records obtained from an electronic stethoscope board designed for fulfillment of this study in the presence of high-level power-line noise and external disturbing sounds and as a result, no false positive or false negative errors were detected. High robustness against measurement noises of the electronic stethoscopes, acceptable detection-segmentation accuracy of PCG events in the presence of severe heart valvular and arrhythmic dysfunctions within a tolerable computational burden (processing time) and having no parameter dependency on the acquisition sampling frequency can be mentioned as important merits and capabilities of the proposed PCAGI-based PCG events detection-segmentation algorithm.

Wavelet Packet Entropy for Heart Murmurs Classification

“…Kaiser window with cut-off frequencies of 25 and 700 Hz was used because heart sound signals are in the range less than 700 Hz [2, 14, 21]. The frequency range of 49–51 was also removed to eliminate the power line noise.…”

“…This structure can be continued further to decompose the following approximations and details to reach to a proper level for representing PCG signals of desired murmurs. From the literature, it can be concluded that levels 6 to 8 were generally chosen for analyzing PCG signals of different pathological heart sounds [3, 12, 14, 20, 21]. …”

Detection of pathological heart sounds

“…The detection and boundary identification of main HS's in cardiac sounds using an expert frequency-energy-based metric has been proposed (Naseri and Homaeinezhad 2013). Homaeinezhad et al (2011) have also suggested a wavelet-based principal components analyzed metric for the delineation of PCG sounds. The introduction of probabilistic models for HS segmentation has led to improved precision.…”

Diagnosis of Heart Valve Disorders through Trapezoidal Features and Hybrid Classifier

“…identification of the first and the second heart sound (S1 & S2). There are a number of researches that were addressed PCG segmentation [4], [5], although some works were reported for classifying heart sound without segmentation [6]- [8]. In order to extract discriminant features, an appropriate signal analysis technique is required.…”