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Characteristics of phonocardiography waveforms that influence automatic feature recognition

Lookup NU author(s): Scott Stainton, Dr Charalampos Tsimenidis, Professor Alan Murray

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE Computer Society, 2016.

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Abstract

© 2016 CCAL. Phonocardiography is a very common diagnostic test, especially for the study of heart valve function. However, this test is still almost entirely manual using a stethoscope because of the difficulties in analysing waveforms with excessive acoustic noise, and with subtle clinical characteristics requiring good hearing for detection. The PhysioNet phonocardiography data were analysed to assess the characteristics that related to successful detection of normal or abnormal characteristics. After processing to reduce the effect of noise, the mean signal level in comparison to the processed peak valve sounds was 45±15%. There was a tendency for the signal level to be higher in the abnormal recordings, but this was significant only in one of the five PhysioNet databases, by 8% (p=0.002). It was noted that one database had significantly higher noise levels than the other four. Autocorrelation was used to analyse the processed waveforms, with successful automated detection in 58% of recordings of peaks associated with both the first and second heart sounds. This was more effective in the normal group with a 5% (p=0.01) greater success rate than in the abnormal group. For all the data analysed, there was only one small significant difference between the normal and abnormal groups, and so combined data are reported. The autocorrelation time to the subsequent heart beat provided the heart beat interval, and was 0.83±0.19 s (mean ± SD). The first and second heart sounds relative to the heart beat interval had a timing of 37±6% and 65±6%, and an amplitude 43±21% and 37±20% respectively. We have shown that noise is a significant problem, and that first and second heart sounds can be identified automatically with 58% success.


Publication metadata

Author(s): Stainton S, Tsimenidis C, Murray A

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Computing in Cardiology Conference (CinC) 2016

Year of Conference: 2016

Pages: 1173-1176

Online publication date: 02/03/2017

Acceptance date: 02/04/2016

Date deposited: 22/05/2017

ISSN: 2325-887X

Publisher: IEEE Computer Society

URL: http://ieeexplore.ieee.org/document/7868957/

Library holdings: Search Newcastle University Library for this item

ISBN: 9781509008964


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