biostudies-literatureIwasaki APrecursory Research for Embryonic Science and TechnologyJapan Society for the Promotion of Science1821-1829https://www.ebi.ac.uk/biostudies/studies/S-EPMC8590683biostudies-literatureUnknown25(4)<h4>Purpose</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.Sleep & breathing = Schlaf & AtmungScreening of sleep apnea based on heart rate variability and long short-term memory.PMC8590683PMJPR1859KAHENHI 17H00872Fujiwara KSumi YIwasaki AKano MNakayama CKadotani HMatsuo MfalseScreening of sleep apnea based on heart rate variability and long short-term memory.<h4>Purpose</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.2021-01-01T00:00:00Z2021 Dec2024-02-15T17:31:33.421Z2022-02-11T13:09:57.896ZS-EPMC85906833342318310.1007/s11325-020-02249-0