Yin Hai, Ma Qiliang, Zhuang Junwei, Yu Wei, Wang Zhongyou
School of Biomedical Engineering and Medical Imaging, Xianning Medical College, Hubei University of Science and Technology, Xianning, 437100, People's Republic of China.
School of Mathematics and Computer, Wuhan Textile University, Wuhan, 430200, People's Republic of China.
Med Devices (Auckl). 2022 Aug 19;15:285-292. doi: 10.2147/MDER.S368726. eCollection 2022.
Heart sound signal is an important physiological signal of human body, and the identification and research of heart sound signal is of great significance.
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.
Compared with other methods, this method has a relatively small amount of input feature data and high accuracy, fast recognition speed.
HSMM combined with deep neural network is expected to be deployed on smart mobile devices for telemedicine detection.
心音信号是人体重要的生理信号，对心音信号的识别与研究具有重要意义。
针对异常心音信号识别，提出一种将隐半马尔可夫模型（HSMM）与深度神经网络相结合的异常心音识别系统。首先，利用HSMM构建心音分割模型以准确分割心音信号，然后对分割后的心音信号进行特征提取。最后，使用训练好的深度神经网络模型进行识别。
与其他方法相比，该方法输入特征数据量相对较小，准确率高，识别速度快。
HSMM与深度神经网络相结合有望部署在智能移动设备上用于远程医疗检测。
