{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T17:18:23Z","timestamp":1793121503901,"version":"4.5.0"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T00:00:00Z","timestamp":1708300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Sleep apnoea syndrome (SAS) is a serious sleep disorder and early detection of sleep apnoea not only reduces treatment costs but also saves lives. Conventional polysomnography (PSG) is widely regarded as the gold standard diagnostic tool for sleep apnoea. However, this method is expensive, time-consuming and inherently disruptive to sleep. Recent studies have pointed out that ECG analysis is a simple and effective diagnostic method for sleep apnea, which can effectively provide physicians with an aid to diagnosis and reduce patients\u2019 suffering.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To this end, in this paper proposes a LightGBM hybrid model based on ECG signals for efficient detection of sleep apnea. Firstly, the improved Isolated Forest algorithm is introduced to remove abnormal data and solve the data sample imbalance problem. Secondly, the parameters of LightGBM algorithm are optimised by the improved TPE (Tree-structured Parzen Estimator) algorithm to determine the best parameter configuration of the model. Finally, the fusion model TPE_OptGBM is used to detect sleep apnoea. In the experimental phase, we validated the model based on the sleep apnoea ECG database provided by Phillips-University of Marburg, Germany.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The experimental results show that the model proposed in this paper achieves an accuracy of 95.08%, a precision of 94.80%, a recall of 97.51%, and an F1 value of 96.14%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>All of these evaluation indicators are better than the current mainstream models, which is expected to assist the doctor\u2019s diagnostic process and provide a better medical experience for patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnins.2024.1324933","type":"journal-article","created":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T23:27:47Z","timestamp":1708298867000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Application of LightGBM hybrid model based on TPE algorithm optimization in sleep apnea detection"],"prefix":"10.3389","volume":"18","author":[{"given":"Xin","family":"Xiong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aikun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfeng","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunwu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruixiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiran","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiancong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,2,19]]},"reference":[{"key":"ref1","first-page":"2623","article-title":"OPTUNA: a nextgeneration hyperparameter optimization framework","author":"Akiba","year":"2019"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s00408-007-9055-5","article-title":"The economic impact of obstructive sleep apnea","volume":"186","author":"Alghanim","year":"2008","journal-title":"Lung"},{"key":"ref3","first-page":"6","article-title":"A novel deep learning approach for detection of sleep apnea from ECG signal","author":"Anbalagan","year":"2023"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1378\/chest.124.4.1400","article-title":"The relationship between congestive heart failure, sleep apnea, and mortality in older men","volume":"124","author":"Ancoli-Israel","year":"2003","journal-title":"Chest"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"S1703","DOI":"10.3233\/BME-151470","article-title":"Sleep apnea classification based on respiration signals by using ensemble methods","volume":"26","author":"Avc\u0131","year":"2015","journal-title":"Bio-Med. Mater. Eng."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1109\/TITB.2010.2087386","article-title":"Apnea med assist: real-time sleep apnea monitor using single-Lead ECG.IEEE trans","volume":"15","author":"Bsoul","year":"2011","journal-title":"Inf. Technol.Biomed."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1109\/TITB.2009.2034975","article-title":"Real-time detection of apneas on a PDA","volume":"14","author":"Burgos","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.psychres.2019.02.011","article-title":"The first night effect during polysomnography, and patients\u2019 estimates of sleep quality","volume":"274","author":"Byun","year":"2019","journal-title":"Psychiatry Res."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1093\/sleep\/30.3.291","article-title":"Sleep-disordered breathing and cardiovascular risk","volume":"30","author":"Caples","year":"2007","journal-title":"Sleep"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"5037","DOI":"10.3390\/s20185037","article-title":"Deep recurrent neural networks for automatic detection of sleep apnea from single channel respiration signals","volume":"20","author":"ElMoaqet","year":"2020","journal-title":"Sensors"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"106591","DOI":"10.1016\/j.knosys.2020.106591","article-title":"Accurate detection of sleep apnea with long short-term memory network based on RR interval signals","volume":"212","author":"Faust","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2020.3017246","article-title":"A sleep apnea detection method based on unsupervised feature learning and single-lead electrocardiogram","volume":"70","author":"Feng","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2003","journal-title":"Circulation"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10877-011-9323-z","article-title":"Classification of sleep apnea types using wavelet packet analysis of short-term ECG signals","volume":"26","author":"Gubbi","year":"2012","journal-title":"J. Clin. Monit. Comput."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"6379","DOI":"10.1038\/s41598-023-33170-7","article-title":"Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity","volume":"13","author":"Han","year":"2023","journal-title":"Sci. Rep."},{"key":"ref16","first-page":"45","article-title":"A comparative study of various classifiers for automated sleep apnea screening based on single-lead electrocardiogram","author":"Hassan","year":"2015"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1093\/sleep\/29.3.299","article-title":"The economic cost of sleep disorders","volume":"29","author":"Hillman","year":"2006","journal-title":"Sleep"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"3149","DOI":"10.5555\/3294996.3295074","article-title":"Lightgbm: a highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1109\/TITB.2009.2031639","article-title":"Automated scoring of obstructive sleep apnea and hypopnea events using short-term electrocardiogram recordings","volume":"13","author":"Khandoker","year":"2009","journal-title":"IEEE Trans. Inf. Technol.Biomed."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"138","DOI":"10.4015\/S1016237206000233","article-title":"A new approach for identifying sleep apnea syndrome using wavelet transform and neural networks","volume":"18","author":"Lin","year":"2006","journal-title":"Biomed. Eng., Appl., Basis Commun."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.arbres.2019.02.014","article-title":"Intra-and inter-physician agreement in therapeutic decision for sleep apnea syndrome","volume":"56","author":"Lugo","year":"2020","journal-title":"Arch. Bronconeumol."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"1799","DOI":"10.5665\/sleep.3204","article-title":"Effectiveness of three sleep apnea management alternatives","volume":"36","author":"Masa","year":"2013","journal-title":"Sleep"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1109\/JBHI.2018.2823265","article-title":"A review of obstructive sleep apnea detection approaches","volume":"23","author":"Mendonca","year":"2018","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1513\/pats.200709-155MG","article-title":"The epidemiology of adult obstructive sleep apnea","volume":"5","author":"Punjabi","year":"2008","journal-title":"Proc. Amer. Thoracic Soc."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"2875","DOI":"10.3233\/BME-141106","article-title":"Sleep apnea classification using ECG-signal wavelet-PCA features","volume":"24","author":"Rachim","year":"2014","journal-title":"Biomed. Mater. Eng."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s41782-021-00138-4","article-title":"Sleep apnea classification using random Forest via ECG","volume":"5","author":"Razi","year":"2021","journal-title":"Sleep Vigilance"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.1378\/chest.14-0970","article-title":"International classification of sleep disorders-third edition (ICSD-3)","volume":"146","author":"Sateia","year":"2014","journal-title":"Chest"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"102796","DOI":"10.1016\/j.dsp.2020.102796","article-title":"Detection of sleep apnea from heart beat interval and ECG derived respiration signals using sliding mode singular spectrum analysis","volume":"104","author":"Singh","year":"2020","journal-title":"Digital Sig. Proc."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TBME.2015.2498199","article-title":"An obstructive sleep apnea detection approach using a discriminative hidden Markov model from ECG signals","volume":"63","author":"Song","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.sleep.2020.03.017","article-title":"Obstructive sleep apnea, depression and cognitive impairment","volume":"72","author":"Vanek","year":"2020","journal-title":"Sleep Med."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"2269","DOI":"10.1109\/TBME.2015.2422378","article-title":"A novel algorithm for the automatic detection of sleep apnea from single-lead ECG","volume":"62","author":"Varon","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1210\/jcem.85.3.6484","article-title":"Sleep Apneaand DaytimeSleepinesandFatigue: Relationto visceral obesity, insulin resistance, and Hypercytokinemia","volume":"85","author":"Vgontzas","year":"2000","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.cmpb.2019.05.002","article-title":"RR interval based automated apnea detection approach using residual network","volume":"176","author":"Wang","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1109\/ACCESS.2020.3038486","article-title":"An efficient method to detect sleep hypopnea-apnea events based on EEG signals","volume":"9","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"e7731","DOI":"10.7717\/peerj.7731","article-title":"Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network","volume":"7","author":"Wang","year":"2019","journal-title":"PeerJ"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"3227","DOI":"10.3390\/app10093227","article-title":"Application of improved LightGBM model in blood glucose prediction","volume":"10","author":"Wang","year":"2020","journal-title":"Appl. Sci."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"105211","DOI":"10.1016\/j.compbiomed.2022.105211","article-title":"BI-directional long short-term memory for automatic detection of sleep apnea events based on single channel EEG signal","volume":"142","author":"Wang","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref38","first-page":"461","article-title":"Automatic obstructive sleep apnea detection based on respiratory parameters in physiological signals","author":"Yan","year":"2022"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"105124","DOI":"10.1016\/j.compbiomed.2021.105124","article-title":"Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network","volume":"140","author":"Yang","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1093\/sleep\/20.9.705","article-title":"Estimation of the clinically diagnosed proportion of sleep apnea syndrome in middle-aged men and women","volume":"20","author":"Young","year":"1997","journal-title":"Sleep"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.1056\/NEJM199304293281704","article-title":"The occurrence of sleep-disordered breathing among middle-aged adults","volume":"328","author":"Young","year":"1993","journal-title":"N. Engl. J. Med."},{"key":"ref42","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1164\/rccm.2109080","article-title":"Epidemiology of obstructive sleep apnea: a population health perspective","volume":"165","author":"Young","year":"2002","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref43","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1109\/JBHI.2018.2842919","article-title":"Automatic detection of obstructive sleep apnea using wavelet transform and entropy-based features from single-lead ECG signal","volume":"23","author":"Zarei","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.660275","article-title":"Predicting metabolite\u2013disease associations based on LightGBM model","volume":"12","author":"Zhang","year":"2021","journal-title":"Front. Genet."},{"key":"ref45","doi-asserted-by":"publisher","first-page":"1495","DOI":"10.3390\/electronics11091495","article-title":"Coronary artery disease detection model based on class balancing methods and LightGBM algorithm","volume":"11","author":"Zhang","year":"2022","journal-title":"Electronics"}],"container-title":["Frontiers in Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2024.1324933\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T23:27:51Z","timestamp":1708298871000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2024.1324933\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,19]]},"references-count":45,"alternative-id":["10.3389\/fnins.2024.1324933"],"URL":"https:\/\/doi.org\/10.3389\/fnins.2024.1324933","relation":{},"ISSN":["1662-453X"],"issn-type":[{"value":"1662-453X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,19]]},"article-number":"1324933"}}