期刊
SLEEP AND BREATHING
卷 25, 期 4, 页码 1821-1829出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s11325-020-02249-0
关键词
Sleep apnea syndrome; Wearable sensor; Wearable sensor; Machine learning; Telemedicine
资金
- JST PRESTO [JPMJPR1859]
- JSPS KAHENHI [17H00872]
Sleep apnea syndrome (SAS) is a common sleep disorder with high undiagnosed rates. A new screening method combining heart rate measurement and long short-term memory (LSTM) showed superior sensitivity and specificity in distinguishing patients with moderate-to-severe SAS. This method, easily applicable at home using wearable heart rate sensors, may serve as an effective SAS screening system.
Purpose Sleep apnea syndrome (SAS) is a prevalent sleep disorder in which apnea and hypopnea occur frequently during sleep and result in increase of the risk of lifestyle-related disease development as well as daytime sleepiness. Although SAS is a common sleep disorder, most patients remain undiagnosed because the gold standard test polysomnography (PSG), is high-cost and unavailable in many hospitals. Thus, an SAS screening system that can be used easily at home is needed. Methods Apnea during sleep affects changes in the autonomic nervous function, which causes fluctuation of the heart rate. In this study, we propose a new SAS screening method that combines heart rate measurement and long short-term memory (LSTM) which is a type of recurrent neural network (RNN). We analyzed the data of intervals between adjacent R waves (R-R interval; RRI) on the electrocardiogram (ECG) records, and used an LSTM model whose inputs are the RRI data is trained to discriminate the respiratory condition during sleep. Results The application of the proposed method to clinical data showed that it distinguished between patients with moderate-to-severe SAS with a sensitivity of 100% and specificity of 100%, results which are superior to any other existing SAS screening methods. Conclusion Since the RRI data can be easily measured by means of wearable heart rate sensors, our method may prove to be useful as an SAS screening system at home.
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