基于口鼻流速信号与LSTM的实时睡眠呼吸暂停与低通气事件检测算法  

An Apnea and Hypopnea Episodes Detection Algorithm Based on Oronasal Airflow Signals and LSTM Networks

作　　者：杨振华 栾开昊 谭媛元 刘宏德 YANG Zhenhua;LUAN Kaihao;TAN Yuanyuan;LIU Hongde(School of Biological Science and Medical Engineering,Southeast University,Nanjing Jiangsu 210096,China)

机构地区：[1]东南大学生物科学与医学工程学院,江苏南京210096

出　　处：《信息与电脑》2022年第23期77-82,共6页Information & Computer

摘　　要：睡眠呼吸暂停低通气综合征(Sleep Apnea-Hypopnea Syndrome,SAHS)不仅影响患者生活质量,而且可能诱发严重疾病,因此提出了一种基于口鼻流速信号与长短时记忆网络(Long Short-Term Memory,LSTM)的算法。该算法对于睡眠呼吸暂停及低通气事件的实时检测准确率为95%,与之前的研究相比,具有更高的准确率。由于本方法只需单通道信号,且具有实时性强的特点,可应用于家用无创呼吸机,简化SAHS的诊断和治疗流程。Sleep Apnea-Hypopnea Syndrome(SAHS) affects patients’ quality of life and can induce serious illness.This study proposes an algorithm based on oronasal airflow signals and Long-Short-Term Memory(LSTM) neural networks to achieve real-time accurate detection of sleep apnea and hypopnea events with an accuracy rate of 95%.Compared with previous studies,this method has the characteristics of high accuracy,requiring only a single-channel signal,and strong real-time performance.Applying this method to home non-invasive ventilators can simplify the diagnosis and treatment process of SAHS.

关 键 词：长短时记忆网络(LSTM) 睡眠呼吸暂停低通气综合征(SAHS) 持续气道正压通气(CPAP) 多导睡眠监测(PSG) 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]