Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds | IEEE Conference Publication | IEEE Xplore

Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds


Abstract:

The goal of the 2016 PhysioNet/CinC Challenge is the development of an algorithm to classify normal/abnormal heart sounds. A total of 124 time-frequency features were ext...Show More

Abstract:

The goal of the 2016 PhysioNet/CinC Challenge is the development of an algorithm to classify normal/abnormal heart sounds. A total of 124 time-frequency features were extracted from the phonocardiogram (PCG) and input to a variant of the AdaBoost classifier. A second classifier using convolutional neural network (CNN) was trained using PCGs cardiac cycles decomposed into four frequency bands. The final decision rule to classify normal/abnormal heart sounds was based on an ensemble of classifiers combining the outputs of AdaBoost and the CNN. The algorithm was trained on a training dataset (normal= 2575, abnormal= 665) and evaluated on a blind test dataset. Our classifier ensemble approach obtained the highest score of the competition with a sensitivity, specificity, and overall score of 0.9424, 0.7781, and 0.8602, respectively.
Date of Conference: 11-14 September 2016
Date Added to IEEE Xplore: 02 March 2017
ISBN Information:
Electronic ISSN: 2325-887X
Conference Location: Vancouver, BC, Canada

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