Classification of normal/abnormal heart sound recordings based on multi-domain features and back propagation neural network | IEEE Conference Publication | IEEE Xplore

Classification of normal/abnormal heart sound recordings based on multi-domain features and back propagation neural network


Abstract:

This paper aims to classify a single PCG recording as normal or abnormal for computer-aided diagnosis. The proposed framework for this challenge has four steps: preproces...Show More

Abstract:

This paper aims to classify a single PCG recording as normal or abnormal for computer-aided diagnosis. The proposed framework for this challenge has four steps: preprocessing, feature extraction, training and validation. In the preprocessing step, a recording is segmented into four states, i.e., the first heart sound, systolic interval, the second heart sound, and diastolic interval by the Springer Segmentation algorithm. In the feature extraction step, the authors extract 324 features from multi-domains to perform classification. A back propagation neural network is used as predication model. The optimal threshold for distinguishing normal and abnormal is determined by the statistics of model output for both normal and abnormal. The performance of the proposed predictor tested by the six training sets is sensitivity 0.812 and specificity 0.860 (overall accuracy is 0.836). However, the performance reduces to sensitivity 0.807 and specificity 0.829 (overall accuracy is 0.818) for the hidden test set.
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

References

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