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

Morteza Zabihi*, Ali Bahrami Rad, Serkan Kiranyaz, Moncef Gabbouj, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

115 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationComputing in Cardiology Conference, CinC 2016
EditorsAlan Murray
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781509008964
StatePublished - Mar 1 2016
Event43rd Computing in Cardiology Conference, CinC 2016 - Vancouver, Canada
Duration: Sep 11 2016Sep 14 2016

Publication series

NameComputing in Cardiology
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X


Other43rd Computing in Cardiology Conference, CinC 2016

ASJC Scopus subject areas

  • Computer Science(all)
  • Cardiology and Cardiovascular Medicine


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