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Design of Abnormal Heart Sound Recognition System Based on HSMM and Deep Neural Network
Hai Yin1, Qiliang Ma2, Junwei Zhuang1
1School of Biomedical Engineering and Medical Imaging, Xianning Medical College, Hubei University of Science and Technology, Xianning, 437100, People's Republic of China.
Introduction:
Heart sound signal is an important physiological signal of human body, and the identification and research of heart sound signal is of great significance.
Methods:
For abnormal heart sound signal recognition, an abnormal heart sound recognition system, combining hidden semi-Markov models (HSMM) with deep neural networks, is proposed. Firstly, HSMM is used to build a heart sound segmentation model to accurately segment the heart sound signal, and then the segmented heart sound signal is subjected to feature extraction. Finally, the trained deep neural network model is used for recognition.
Results:
Compared with other methods, this method has a relatively small amount of input feature data and high accuracy, fast recognition speed.
Discussion:
HSMM combined with deep neural network is expected to be deployed on smart mobile devices for telemedicine detection.
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S1 (First Heart Sound)-
S1 is made by the closure of the mitral and tricuspid valves (atrioventricular valves), marking the beginning of systole.
S2 (Second Heart Sound)-
S2 is made by the closure of the aortic and pulmonic valves (semilunar valves), marking the end of the systole.