TY - JOUR A2 - Yao, Jiafeng AU - Shen, Fanlin AU - Cheng, Siyi AU - Li, Zhu AU - Yue, Keqiang AU - Li, Wenjun AU - Dai, Lili PY - 2020 DA - 2020/12/12 TI - Detection of Snore from OSAHS Patients Based on Deep Learning SP - 8864863 VL - 2020 AB - Obstructive sleep apnea-hypopnea syndrome (OSAHS) is extremely harmful to the human body and may cause neurological dysfunction and endocrine dysfunction, resulting in damage to multiple organs and multiple systems throughout the body and negatively affecting the cardiovascular, kidney, and mental systems.临床上,医生通常使用标准PSG帮助诊断PSG判断一个人是否有apnea综合症 多维数据,如脑波、心率和血液氧饱和本文介绍一种识别OSAHS方法,方便病人在日常生活中监控自己以避免延迟处理第一,我们从理论上分析正常人和OSAHS病人在时间和频率域间打呼声之差第二,打呼声与aprnea事件相关联,非apnea相关打呼声通过深学习分类,然后OSAHS症状的严重程度得到了识别。论文提议的算法中,打呼数据特征通过三种特征提取法提取,即MFCC、LPCC和LPFC并使用CNN和LSTM分类实验结果显示MFCC特征提取法和LSTM模型的精度最高,当它被二分分类时为87%此外,AHI值可以通过算法系统获取,算法系统可判定OSAHS的严重程度SN-2040-2295UR-https://doi.org/101155/20864863DO-10.1155/20864863JF-HindawiKW-ER