Heart sound classification using deep structured features


Michael Tschannen, Thomas Kramer, Gian Marti, Matthias Heinzmann, and Thomas Wiatowski


Computing in Cardiology (CinC), Vancouver, Canada, pp. 565-568, Sept. 2016.

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We present a novel machine learning-based method for heart sound classification which we submitted to the PhysioNet/CinC Challenge 2016. Our method relies on a robust feature representation--generated by a wavelet-based deep convolutional neural network (CNN)--of each cardiac cycle in the test recording, and support vector machine classification. In addition to the CNN-based features, our method incorporates physiological and spectral features to summarize the characteristics of the entire test recording. The proposed method obtained a score, sensitivity, and specificity of 0.812, 0.848, and 0.776, respectively, on the hidden challenge testing set.


Code for the scattering transform is available here.

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Copyright Notice: © 2016 M. Tschannen, T. Kramer, G. Marti, M. Heinzmann, and T. Wiatowski.

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