@inproceedings{eb16f13eb38e440b80103c69b4f4a2cf,
title = "Heart sound anomaly and quality detection using ensemble of neural networks without segmentation",
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.",
author = "Morteza Zabihi and Rad, {Ali Bahrami} and Serkan Kiranyaz and Moncef Gabbouj and Katsaggelos, {Aggelos K.}",
year = "2016",
month = mar,
day = "1",
language = "English (US)",
series = "Computing in Cardiology",
publisher = "IEEE Computer Society",
pages = "613--616",
editor = "Alan Murray",
booktitle = "Computing in Cardiology Conference, CinC 2016",
address = "United States",
note = "43rd Computing in Cardiology Conference, CinC 2016 ; Conference date: 11-09-2016 Through 14-09-2016",
}