Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network

Sensors

Po-Chiun Huang¹  Hung-Chi Chang¹  Yuan-Hao Huang¹  Hsi-Pin Ma¹  Hau-Tieng Wu²  Yu-Lun Lo³ 

[1] Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan;Department of Mathematics, Duke University, Durham, NC 27708, USA;Department of Thoracic Medicine, Healthcare Center, Chang Gung Memorial Hospital, School of Medicine, Chang Gung University, Taipei 33302, Taiwan Correspondence: [e-mail] (Y.-L.L.);

关键词: Abdominal movement signal;    hypopnea;    LSTM-RNN;    neural network;    oxygen saturation;    sleep apnea syndrome;   

DOI  :  10.3390/s20216067

来源: DOAJ

【 摘 要 】

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is characterized by repeated airflow partial reduction or complete cessation due to upper airway collapse during sleep. OSAHS can induce frequent awake and intermittent hypoxia that is associated with hypertension and cardiovascular events. Full-channel Polysomnography (PSG) is the gold standard for diagnosing OSAHS; however, this PSG evaluation process is unsuitable for home screening. To solve this problem, a measuring module integrating abdominal and thoracic triaxial accelerometers, a pulsed oximeter (SpO2) and an electrocardiogram sensor was devised in this study. Moreover, a long short-term memory recurrent neural network model is proposed to classify four types of sleep breathing patterns, namely obstructive sleep apnea (OSA), central sleep apnea (CSA), hypopnea (HYP) events and normal breathing (NOR). The proposed algorithm not only reports the apnea-hypopnea index (AHI) through the acquired overnight signals but also identifies the occurrences of OSA, CSA, HYP and NOR, which assists in OSAHS diagnosis. In the clinical experiment with 115 participants, the performances of the proposed system and algorithm were compared with those of traditional expert interpretation based on PSG signals. The accuracy of AHI severity group classification was 89.3%, and the AHI difference for PSG expert interpretation was $5.0\pm 4.5$. The overall accuracy of detecting abnormal OSA, CSA and HYP events was 92.3%.

【 授权许可】

Unknown