An Intelligent Diagnostic System for Congenital Heart Defects

Amiri, Amir Mohammad; Armano, Giuliano

doi:10.14569/ijacsa.2013.040714

Cited by 2 publications

(1 citation statement)

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“…where τ is named maximum effective lag and can be calculated as τ = 1 2f l , where f l is the expected lower frequency bound of the desired signal, here the heart sound. By assuming f l = 50 Hz according to the literature in [19]- [22], the proper time-lags are supposed to be around the peaks of the autocorrelation envelope with at most τ = 10 ms difference. Number of detectable peaks in envelope of auto-correlation is affected by quality and regularity of heart sound.…”

Section: ) Swt-pcamentioning

confidence: 99%

A Blind Filtering Framework for Noisy Neonatal Chest Sounds

et al. 2022

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Chest sound-as the first and most commonly available vital signal for newborns-contains affluent information about their cardiac and respiratory health. However, neonatal lung sound auscultation is currently challenging and often unreliable due to the noise and interference, particularly for preterm infants. The noise often overlaps with the heart and lung contents in both time and frequency. Moreover, the frequency band of the useful components varies from one case to another, making it difficult to separate by fixed band-pass filtering. In this study, a single-channel Blind Source Separation (SCBSS) framework is proposed to separate newborns' lung and heart sounds from noisy chest sounds recorded by a digital stethoscope. This method first decomposes the signal into a multi-resolution representation using a timefrequency transform, and then applies source separation algorithms, to find proper ad hoc frequency filters. In the simulation scenario, two different time-frequency transforms are considered; Stationary Wavelet Transform (SWT) with dyadic bases, and Continuous Wavelet Transform (CWT) with redundant bases. The transforms are followed by three different source separation methods, namely Principal Component Analysis (PCA), Periodic Component Analysis (πCA), and Second Order Blind Identification (SOBI). The yielded combinations are applied to the chest sounds recorded from ninety-one preterm and full-term newborns. The results show that compared to raw signals, fixed band-pass filtering and seven other separation methods, the heart and lung sounds extracted by the proposed methods have higher quality index and also result in more reliable heart and respiratory rate estimation.INDEX TERMS Neonatal chest sound analysis, single-channel blind source separation, heart sound filtering, lung sound filtering.

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Section: ) Swt-pcamentioning

confidence: 99%