Abstract
In this paper, we present a new method to categorize the heart sound signals into Normal, Murmur, Extra Heart Sound and Artifact with Convolutional Neural Network (CNN) model. The experimental dataset are from the PASCAL heart sound classification workshop, 2011. Our method mainly contains three steps as follows. Firstly, we propose cross- cutting and framing to enlarge the data. The data used for training and test is three times than before. Secondly, we manage the heart sound signals via Butterworth filter and Fast Fourier Transform simply. Finally the captured frequency features are input into the CNN model for classification. Compared with the traditional heart sounds classification approaches-selecting features and designing classifier, our method overcomes the uncertain in selecting features and improves the self-learning ability. The experimental results show that deep learning network model have higher classification effectiveness for the data with much noise. The global identification rate reaches 98%, more effective than previous related studies.
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Acknowledgments
This work is supported in part by the National Natural Science Founding of China (61171142, 61401163, U1636218), Science and Technology Planning Project of Guangdong Province of China (2014B010111003, 2014B010111006), the Fundamental Research Funds for the Central Universities (2017MS045), the Diabetes Intelligent Wear Monitoring Equipment and Complications Prevention and Control Cloud Platform (2016B010108008), and Guangzhou Key Lab of Body Data Science (201605030011).
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Li, T., Qing, C., Tian, X. (2018). Classification of Heart Sounds Based on Convolutional Neural Network. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_24
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DOI: https://doi.org/10.1007/978-981-10-8530-7_24
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