Abstract
With the development of medical technology, many diseases can be cured. However, the mortality rate of cardiovascular disease is still high and showing an upward trend. Reducing the mortality of such diseases is one of the difficulties that modern medicine needs to overcome. Heart sound auscultation is one of the most basic detection methods for cardiovascular disease, but it is more difficult for inexperienced medical staff. Therefore, it’s urgent to develop assistive technology to assist heart sound auscultation.
According to previous works [1,2,3,4], it was concluded that deep learning has a notable effect on heart disease detection. The aim of this study is to develop a detection system to assist heart sound auscultation. Firstly, in the pre-experiment by debugging the batch size of CNN model, we determined that the accuracy of the model is the highest with the batch size of 256, which is 93.07%. Then, we used, a combined CNN and Long Short-Term Memory (LSTM) neural network model for heart sound detection, and obtained an accuracy of 91.06%.
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Huai, X., Panote, S., Choi, D., Kuwahara, N. (2020). Heart Sound Recognition Technology Based on Deep Learning. In: Duffy, V. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Posture, Motion and Health. HCII 2020. Lecture Notes in Computer Science(), vol 12198. Springer, Cham. https://doi.org/10.1007/978-3-030-49904-4_36
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