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Heart sound anomaly and quality detection using ensemble of neural networks without segmentation

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationComputing in Cardiology Conference, CinC 2016
PublisherIEEE
Pages613-616
Number of pages4
ISBN (Electronic)9781509008964
DOIs
Publication statusPublished - 1 Mar 2017
Publication typeA4 Article in a conference publication
EventComputing in cardiology conference -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)2325-887X

Conference

ConferenceComputing in cardiology conference
Period1/01/00 → …

Abstract

Phonocardiogram (PCG) signal is used as a diagnostic test in ambulatory monitoring in order to evaluate the heart hemodynamic status and to detect a cardiovascular disease. The objective of this study is to develop an automatic classification method for anomaly (normal vs. abnormal) and quality (good vs. bad) detection of PCG recordings without segmentation. For this purpose, a subset of 18 features is selected among 40 features based on a wrapper feature selection scheme. These features are extracted from time, frequency, and time-frequency domains without any segmentation. The selected features are fed into an ensemble of 20 feedforward neural networks for classification task. The proposed algorithm achieved the overall score of 91.50% (94.23% sensitivity and 88.76% specificity) and 85.90% (86.91% sensitivity and 84.90% specificity) on the train and unseen test datasets, respectively. The proposed method got the second best score in the PhysioNet/CinC Challenge 2016.

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