scholarly journals Sleep Apnea Detection by a Recurrent Neural Network based on Long Short-Term Memory

2020 ◽  
Author(s):  
Keita Nishio ◽  
Takashi Kaburagi ◽  
Satoshi Kumagai ◽  
Toshiyuki Matsumoto ◽  
Yosuke Kurihara
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Apnea Detection ◽  
Long Short Term Memory

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

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6067
Author(s):  
Hung-Chi Chang ◽  
Hau-Tieng Wu ◽  
Po-Chiun Huang ◽  
Hsi-Pin Ma ◽  
Yu-Lun Lo ◽  
...  
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Apnea Hypopnea Index ◽  
Short Term ◽  
Term Memory ◽  
Obstructive Sleep ◽  
Long Short Term Memory

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±4.5. The overall accuracy of detecting abnormal OSA, CSA and HYP events was 92.3%.

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