Tampere University of Technology

TUTCRIS Research Portal

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

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


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

Publication series

ISSN (Electronic)2325-887X


ConferenceComputing in cardiology conference
Period1/01/00 → …


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.

Publication forum classification

Field of science, Statistics Finland