Design of Abnormal Heart Sound Recognition System Based on HSMM and Deep Neural Network.

IF 1.3 Q4 ENGINEERING, BIOMEDICAL

Medical Devices-Evidence and Research Pub Date : 2022-08-19 eCollection Date: 2022-01-01 DOI:10.2147/MDER.S368726

Hai Yin, Qiliang Ma, Junwei Zhuang, Wei Yu, Zhongyou Wang

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引用次数: 1

Abstract

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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基于HSMM和深度神经网络的异常心音识别系统设计。

导读:心音信号是人体重要的生理信号，对心音信号的识别和研究具有重要意义。方法:针对异常心音信号的识别，提出了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方法建立心音分割模型，对心音信号进行精确分割，然后对分割后的心音信号进行特征提取。最后，利用训练好的深度神经网络模型进行识别。结果:与其他方法相比，该方法输入特征数据量相对较少，且准确率高，识别速度快。讨论:HSMM结合深度神经网络有望部署在智能移动设备上，用于远程医疗检测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical Devices-Evidence and Research ENGINEERING, BIOMEDICAL-

CiteScore

2.80

自引率

0.00%

发文量

审稿时长

16 weeks