{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T20:21:34Z","timestamp":1790799694037,"version":"4.1.0"},"reference-count":35,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,25]],"date-time":"2020-10-25T00:00:00Z","timestamp":1603584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005057","name":"National Tsing Hua University","doi-asserted-by":"publisher","award":["107Q2516E1"],"award-info":[{"award-number":["107Q2516E1"]}],"id":[{"id":"10.13039\/501100005057","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chang Gung University, Taiwan","award":["CMRPG3H0251"],"award-info":[{"award-number":["CMRPG3H0251"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u00b14.5. The overall accuracy of detecting abnormal OSA, CSA and HYP events was 92.3%.<\/jats:p>","DOI":"10.3390\/s20216067","type":"journal-article","created":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T03:51:47Z","timestamp":1603684307000},"page":"6067","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network"],"prefix":"10.3390","volume":"20","author":[{"given":"Hung-Chi","family":"Chang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hau-Tieng","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Department of Statistical Science, Duke University, Durham, NC 27708, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Po-Chiun","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsi-Pin","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Lun","family":"Lo","sequence":"additional","affiliation":[{"name":"Department of Thoracic Medicine, Healthcare Center, Chang Gung Memorial Hospital, School of Medicine, Chang Gung University, Taipei 33302, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6781-7312","authenticated-orcid":false,"given":"Yuan-Hao","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1093\/aje\/kws342","article-title":"Increased prevalence of sleep-disordered breathing in adults","volume":"177","author":"Peppard","year":"2013","journal-title":"Am. J. Epidemiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"479","DOI":"10.5664\/jcsm.6506","article-title":"Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline","volume":"15","author":"Kapur","year":"2017","journal-title":"J. Clin. Sleep Med."},{"key":"ref_3","unstructured":"Berry, R.B. (2020, October 25). The AASM Manual for the Scoring of Sleep and Associated Events. Available online: http:\/\/aasm.org\/clinical-resources\/scoring-manual\/."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pevernagie, D.A., Gnidovec\u2013Strazisar, B., Grote, L., Heinzer, R., McNicholas, W.T., Penzel, T., Randerath, W., Schiza, S., Verbraecken, J., and Arnardottir, E.S. (2020). On the rise and fall of the apnea\u2013hypopnea index: A historical review and critical appraisal. J. Sleep Res.","DOI":"10.1111\/jsr.13066"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1183\/09059180.00004213","article-title":"Ambulatory monitoring in the diagnosis and management of obstructive sleep apnoea syndrome","volume":"22","author":"Pepin","year":"2013","journal-title":"Eur. Respir. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"737","DOI":"10.5664\/jcsm.27032","article-title":"Clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients","volume":"3","author":"Collop","year":"2007","journal-title":"J. Clin. Sleep Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2018.03.011","article-title":"A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal","volume":"294","author":"Li","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/TBME.2013.2278020","article-title":"Nocturnal Awakening and Sleep Efficiency Estimation Using Unobtrusively Measured Ballistocardiogram","volume":"61","author":"Jung","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","first-page":"7","article-title":"A neural network system for detection of obstructive sleep apnea through SpO2 signal features","volume":"3","author":"Almazaydeh","year":"2012","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"S1703","DOI":"10.3233\/BME-151470","article-title":"Sleep apnea classification based on respiration signals by using ensemble methods","volume":"26","author":"Avci","year":"2015","journal-title":"Bio-Med. Mater. Eng."},{"key":"ref_11","unstructured":"Jane, R., Fiz, J.A., Mesquita, J., and Morera, J. (September, January 30). Snoring Analysis for the Screening of Sleep Apnea Hypopnea Syndrome with a Single-Channel Device Developed using Polysomnographic and Snoring Databases. Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine an Biology Society (EMBC), Boston, MA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1111\/j.1365-2869.2011.00935.x","article-title":"Classification algorithms for predicting sleepiness and sleep apnea severity","volume":"21","author":"Eiseman","year":"2012","journal-title":"J. Sleep Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"De Chazal, P., and Sadr, N. (2016, January 16\u201320). Sleep apnoea classification using heart rate variability, ECG derived respiration and cardiopulmonary coupling parameters. Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591410"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Rodrigues, P.P., Santos, D.F., and Leite, L. (2015, January 22\u201325). Obstructive sleep apnoea diagnosis: The Bayesian network model revisited. Proceedings of the 28th International Symposium on Computer-Based Medical Systems, Sao Carlos, Brazil.","DOI":"10.1109\/CBMS.2015.47"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s13534-017-0055-y","article-title":"Obstructive sleep apnoea detection using convolutional neural network based deep learning framework","volume":"8","author":"Dey","year":"2018","journal-title":"Biomed. Eng. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Quiceno-Manrique, A., Alonso-Hernandez, J., Travieso-Gonzalez, C., Ferrer-Ballester, M., and Castellanos-Dominguez, G. (2009, January 3\u20136). Detection of obstructive sleep apnea in ECG recordings using time-frequency distributions and dynamic features. Proceedings of the 31st IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Minneapolis, MN, USA.","DOI":"10.1109\/IEMBS.2009.5333736"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mendez, M., Ruini, D., Villantieri, O., Matteucci, M., Penzel, T., and Bianchi, A. (2007, January 22\u201326). Detection of sleep apnea from surface ECG based on features extracted by an autoregressive model. Proceedings of the 29th IEEE International Conference on Engineering in Medicine and Biology Society (EMBS), Lyon, France.","DOI":"10.1109\/IEMBS.2007.4353742"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Novak, D., Mucha, K., and Al-Ani, T. (2008, January 20\u201325). Long short-term memory for apnea detection based on heart rate variability. Proceedings of the 30th IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Vancouver, BC, Canada.","DOI":"10.1109\/IEMBS.2008.4650394"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1111\/j.1365-2702.2008.02323.x","article-title":"Evaluation of the performance of using mean absolute amplitude analysis of thoracic and abdominal signals for immediate indication of sleep apnoea events","volume":"17","author":"Ng","year":"2008","journal-title":"J. Clin. Nursing"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Garde, A., Dekhordi, P., Ansermino, J.M., and Dumont, G.A. (2016, January 16\u201320). Identifying individual sleep apnea\/hypoapnea epochs using smartphone-based pulse oximetry. Proceedings of the 38th IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591408"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1109\/JBHI.2018.2823265","article-title":"A Review of Obstructive Sleep Apnea Detection Approaches","volume":"23","author":"Mendonca","year":"2019","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"531","DOI":"10.5664\/JCSM.1328","article-title":"Obstructive sleep apnea devices for out-of-center (OOC) testing: Technology evaluation","volume":"7","author":"Collop","year":"2011","journal-title":"J. Clin. Sleep Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1109\/JBHI.2016.2636778","article-title":"Sleep apnea detection based on thoracic and abdominal movement signals of wearable piezo-electric bands","volume":"21","author":"Lin","year":"2017","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.acha.2010.08.002","article-title":"Synchrosqueezed Wavelet Transforms: An empirical mode decomposition-like tool","volume":"30","author":"Daubechies","year":"2011","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1111\/rssb.12039","article-title":"Nonparametric and adaptive modeling of dynamic seasonality and trend with heteroscedastic and dependent errors","volume":"76","author":"Chen","year":"2014","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.acha.2012.08.008","article-title":"Instantaneous frequency and wave shape function (I)","volume":"35","author":"Wu","year":"2013","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_28","unstructured":"Auli, M., Galley, M., Quirk, C., and Zweig, G. (2013, January 18\u201321). Joint language and translation modeling with recurrent neural networks. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Seattle, WA, USA."},{"key":"ref_29","unstructured":"Sutskever, I., Vinyals, O., and Le, Q.V. (2014, January 8\u201313). Sequence to sequence learning with neural networks. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Vinyals, O., Toshev, A., Bengio, S., and Erhan, D. (2015, January 7\u201312). Show and tell: A neural image caption generator. Proceedings of the IEEE Conference on Computer Version and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"ref_31","unstructured":"Lipton, Z.C., Kale, D.C., Elkan, C., and Wetzel, R. (2020, October 25). Learning to Diagnose with LSTM Recurrent Neural Networks. Available online: https:\/\/arxiv.org\/abs\/1511.03677."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1152\/jappl.1964.19.3.417","article-title":"Changes in respiration, heart rate, and systolic blood pressure in human sleep","volume":"19","author":"Snyder","year":"1964","journal-title":"Eur. J. Appl. Physiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"085004","DOI":"10.1088\/1361-6579\/aad5a9","article-title":"Sleep-wake classification via quantifying heart rate variability by convolutional neural network","volume":"39","author":"Malik","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"723","DOI":"10.3389\/fphys.2018.00723","article-title":"Phenotype-based and self-learning inter-individual sleep apnea screening with a level IV-like monitoring system","volume":"9","author":"Wu","year":"2018","journal-title":"Front. Physiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1093\/sleep\/21.7.749","article-title":"Reliability of scoring respiratory disturbance indices and sleep staging","volume":"21","author":"Whitney","year":"1998","journal-title":"Sleep"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6067\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:27:54Z","timestamp":1760178474000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6067"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,25]]},"references-count":35,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20216067"],"URL":"https:\/\/doi.org\/10.3390\/s20216067","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,25]]}}}