As part of the PhysioNet / Computing in Cardiology Challenge 2016, this work focuses on automatic classification of normal / abnormal phonocardiogram (PCG) recording, with the aim of quickly identifying subjects that need further expert diagnosis. To improve the robustness of the classifiers by increasing the number of training samples, the recordings were windowed into 5 second segments and our classifiers were trained to classify these segments. Overall recording classification was then generated using a voting scheme from classification results of its segments. Our features include spectrograms and Mel-frequency cepstrum coefficients. Our best submission result during the official phase (evaluated on a random 20% of the hidden test set) has a score of 0.813, with 0.735 sensitivity and 0.892 specificity. Two more submissions are still being evaluated.
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Normal / abnormal heart sound recordings classification using convolutional neural network
Semantic Scholar · Computer Science · 2016
Abstract
As part of the PhysioNet / Computing in Cardiology Challenge 2016, this work focuses on automatic classification of normal / abnormal phonocardiogram (PCG) recording, with the aim of quickly identifying subjects that need further expert diagnosis. To improve the robustness of the classifiers by increasing the number of training samples, the recordings were windowed into 5 second segments and our classifiers were trained to classify these segments. Overall recording classification was then generated using a voting scheme from classification results of its segments. Our features include spectrograms and Mel-frequency cepstrum coefficients. Our best submission result during the official phase (evaluated on a random 20% of the hidden test set) has a score of 0.813, with 0.735 sensitivity and 0.892 specificity. Two more submissions are still being evaluated.