中图分类号： TP399 文献标识码： A DOI：10.16157/j.issn.0258-7998.200960 中文引用格式： 牟俊杰，姚刚，孙涛. 基于CNN-LSTM神经网络的声纹识别系统设计[J].电子技术应用，2021，47(3)：75-78. 英文引用格式： Mu Junjie，Yao Gang，Sun Tao. Design of vocieprint recognition system based on CNN-LSTM neural network[J]. Application of Electronic Technique，2021，47(3)：75-78.
Design of vocieprint recognition system based on CNN-LSTM neural network
Abstract： For warning of cardiovascular disease,in order to early detect the change of heart and lung voice representing the signs of danger,the vocieprint recognition system based on CNN-LSTM is designed. Using the Internet of Things technology coalescing the heart rate sensor chip, single-chip computer, electronic stethoscope, such as equipments,it can monitor the heart rate in real-time, early warn.And the cardiopulmonary sound recognition model based on the CNN-LSTM algorithm is trained, results show that the loss value is 0.082, accuracy rate of 0.908. The system is forward-looking and has a complete structural framework, which can effectively avoid the waste of medical resources, preposite the countermeasures for cardiovascular diseases.It has a broad application prospect in the market, and plays a significant role in promoting smart medical treatment.
Key words : CNN；LSTM；features extraction；MFCC；cardiovascular disease；vocieprint recognition
在人口老龄化日益严重的当下，心血管疾病不断威胁老年人健康，引发社会广泛关注。由于医疗知识欠缺、行动不便等原因，部分老年人就医不及时，错过了抢救的黄金时间，留下永远的遗憾。开发心血管疾病方面的智能预警系统，满足庞大的老年人群体需求迫在眉睫[3]。在医疗实践中，对心血管疾病的诊断常常以心率、心肺音数据为重要支撑，国内外以DSP[4]、长短时记忆(Long Short Time Memory，LSTM)[5]、卷积神经网络[6](Convolutional Neural Network，CNN)等方法算法为手段对心血管疾病的信号诊断进行了相当多的分析，但基本均停留在理论层面，距离软硬件结合的实际应用尚有差距。各种医疗设备的聚焦点主要是信号的准确采集、分离[7-8]，基于医疗伦理等原因，对智能诊断设备的研制尚处于知识储备期，有巨大的空白亟需填补。本文设计了基于CNN-LSTM的心血管疾病预警系统，利用物联网技术采集心率和心肺音等健康指标数据，对老人的健康状况进行实时监测、预警，采用基于CNN-LSTM模型的智能算法对心肺音信号进行智能分析预警。系统着重考虑了适用性、稳定性和成本，具有较高的实用价值和完整的结构框架，是利用智慧医疗从应用层面解决心血管疾病问题的一次重要探索。
