摘要
多导睡眠监测仪运行异常状态监测由于非稳态离散故障信号的影响,导致监测精度低和监测耗时长,提出基于深度学习的多导睡眠监测仪运行异常状态监测方法。识别多导睡眠检测仪中运行的异常信号,分离存在损伤的非稳态离散故障信号,建立异常状态数据转换通道,根据深度学习算法获取监测仪运行数据特征,完成多导睡眠监测仪运行异常状态监测。实验结果表明,监测方法的监测精度在90%以上,平均监测耗时仅为1.52s,该方法有效提高了监测精度,降低了监测耗时。
Due to the influence of unsteady discrete fault signals,the monitoring of abnormal state of polysomnography leads to low monitoring accuracy and long monitoring time.A method for monitoring abnormal state of polysomnography based on deep learning is proposed.Identify abnormal signals running in the polysomnography detector,separate the non-steady discrete fault signals with damage,and establish a data conversion channel for abnormal state;obtain the operating data characteristics of the monitor according to the deep learning algorithm,and complete the abnormal state of the polysomnography monitor.monitor.The experimental results show that the monitoring accuracy of the monitoring method is more than 90%,and the average monitoring time is only 1.52s.This method effectively improves the monitoring accuracy and reduces the monitoring time.
作者
陈蕾
金剑
Chen Lei;Jin Jian(Sleep Medicine Center Of Neurology Department Of Wuhan NO.1 Hospital,Hubei Wuhan,430000;Equipment Department Of Wuhan NO.1 Hospital,Hubei Wuhan,430000)
出处
《现代科学仪器》
2022年第4期234-238,248,共6页
Modern Scientific Instruments
关键词
深度学习
多导睡眠监测仪
运行异常状态
离散故障信号
异常信号幅值
小波变换函数
Deep learning
Polysomnography monitor
Operation abnormal state
Discrete fault signal
Abnormal signal amplitude
Wavelet transform function
