Commenced in January 2007
Paper Count: 31819
Automatic Classification of Periodic Heart Sounds Using Convolutional Neural Network
Abstract:This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1315911Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 947
 Andre Esteva1, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” in Nature 542, 115–118 (02 February 2017).
 Pranav Rajpurkar, Awni Hannun, Masoumeh Haghpanahi, Codie Bourn, and Andrew Ng, “Cardiologist-Level Arrhythmia Detection With Convolutional Neural Networks,” in https://arxiv.org/abs/1707.01836.
 Qiyu Chen, Weibin Zhang, Xiang Tian y, Xiaoxue Zhang, Shaoqiong Chen and Wenkang Lei, “Automatic Heart and Lung Sounds Classification using Convolutional Neural Networks,” in http://ieeexplore.ieee.org/abstract/document/7820741/.
 Jonathan Rubin, Rui Abreu, Anurag Ganguli, Saigopal Nelaturi, Ion Matei, Kumar Sricharan, “Recognizing Abnormal Heart Sounds Using Deep Learning,” in https://arxiv.org/abs/1707.04642.
 Liu C, Springer D, Li Q, Moody B, Juan RA, Chorro FJ, Castells F, Roig JM, Silva I, Johnson AE, Syed Z, Schmidt SE, Papadaniil CD, Hadjileontiadis L, Naseri H, Moukadem A, Dieterlen A, Brandt C, Tang H, Samieinasab M, Samieinasab MR, Sameni R, Mark RG, Clifford GD, “An open access database for the evaluation of heart sound algorithms,” Physiological Measurement 2016;37(9).
 Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 2014;15(1):1929–1958.
 Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Rafal Jozefowicz, Yangqing Jia, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Mike Schuster, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org.
 Kingma D, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv14126980 2014.