{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:45:38Z","timestamp":1785249938816,"version":"3.55.0"},"reference-count":87,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T00:00:00Z","timestamp":1733702400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T00:00:00Z","timestamp":1733702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2022J011146"],"award-info":[{"award-number":["2022J011146"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10489-024-06013-9","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T06:47:41Z","timestamp":1733726861000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Automatic detection of obstructive sleep apnea through nonlinear dynamics of single-lead ECG signals"],"prefix":"10.1007","volume":"55","author":[{"given":"Liangjie","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fenglin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengzhi","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8353-8265","authenticated-orcid":false,"given":"Wei","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,9]]},"reference":[{"key":"6013_CR1","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1109\/TITB.2012.2188299","volume":"16","author":"B Xie","year":"2012","unstructured":"Xie B, Minn H (2012) Real-time sleep apnea detection by classifier combina- tion. IEEE Trans Inf Technol Biomed 16:469\u2013477","journal-title":"IEEE Trans Inf Technol Biomed"},{"issue":"14","key":"6013_CR2","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1001\/jama.283.14.1829","volume":"283","author":"FJ Nieto","year":"2000","unstructured":"Nieto FJ, Young TB, Lind BK, Shahar E, Samet JM, Redline S, Pickering TG (2000) Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study. Jama 283(14):1829\u20131836","journal-title":"Jama"},{"issue":"12","key":"6013_CR3","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.1109\/TBME.2009.2029563","volume":"56","author":"MO Mendez","year":"2009","unstructured":"Mendez MO, Bianchi AM, Matteucci M, Cerutti S, Penzel T (2009) Sleep apnea screening by autoregressive models from a single ECG lead. IEEE Trans Biomed Eng 56(12):2838\u20132850","journal-title":"IEEE Trans Biomed Eng"},{"key":"6013_CR4","doi-asserted-by":"crossref","unstructured":"Sharma M, Raval M, Acharya UR (2019) A new approach to identify obstructive sleep apnea using an optimal orthogonal wavelet filter bank with ECG signals. Inf Med Unlocked 16:100170","DOI":"10.1016\/j.imu.2019.100170"},{"key":"6013_CR5","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.compbiomed.2018.06.011","volume":"100","author":"M Sharma","year":"2018","unstructured":"Sharma M, Agarwal S, Acharya UR (2018) Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ecg signals. Comput Bio Med 100:100\u2013113","journal-title":"Comput Bio Med"},{"key":"6013_CR6","unstructured":"Lichstein KL, Perlis ML (eds.) (2003) Treating sleep disorders: Principles and practice of behavioral sleep medicine. John Wiley & Sons"},{"key":"6013_CR7","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2018.03.011","volume":"294","author":"K Li","year":"2018","unstructured":"Li K, Pan W, Li Y, Jiang Q, Liu G (2018) A method to detect sleep apnea based on deep neural network and hidden markov model using single-lead ECG signal. Neurocomput 294:94\u2013101","journal-title":"Neurocomput"},{"issue":"4","key":"6013_CR8","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1109\/TITB.2009.2034975","volume":"14","author":"A Burgos","year":"2009","unstructured":"Burgos A, Goni A, Illarramendi A, Bermudez J (2009) Real-time detection of apneas on a PDA. IEEE Trans Inf Technol Biomed 14(4):995\u20131002","journal-title":"IEEE Trans Inf Technol Biomed"},{"issue":"1","key":"6013_CR9","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1109\/JBHI.2013.2266279","volume":"18","author":"BL Koley","year":"2013","unstructured":"Koley BL, Dey D (2013) On-line detection of apnea\/hypopnea events using SpO$$_{ 2}$$ Signal: A rule-based approach employing binary classifier models. IEEE J Biomed Health Inf 18(1):231\u2013239","journal-title":"IEEE J Biomed Health Inf"},{"key":"6013_CR10","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.procs.2013.09.041","volume":"21","author":"L Almazaydeh","year":"2013","unstructured":"Almazaydeh L, Elleithy K, Faezipour M, Abushakra A (2013) Apnea detection based on respiratory signal classification. Procedia Comput Sci 21:310\u2013316","journal-title":"Procedia Comput Sci"},{"key":"6013_CR11","doi-asserted-by":"crossref","unstructured":"Sabil A, Vanbuis J, Baffet G, Feuilloy M, Le Vaillant M, Meslier N, Gagnadoux F (2019) Automatic identification of sleep and wakefulness using single-channel EEG and respiratory polygraphy signals for the diagnosis of obstructive sleep apnea. J Sleep Res 28(2):e12795","DOI":"10.1111\/jsr.12795"},{"issue":"3","key":"6013_CR12","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1109\/TITB.2010.2087386","volume":"15","author":"M Bsoul","year":"2010","unstructured":"Bsoul M, Minn H, Tamil L (2010) Apnea MedAssist: real-time sleep apnea monitor using single-lead ECG. IEEE Trans Inf Technol Biomed 15(3):416\u2013427","journal-title":"IEEE Trans Inf Technol Biomed"},{"issue":"1","key":"6013_CR13","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11325-018-1672-0","volume":"23","author":"H Hilmisson","year":"2019","unstructured":"Hilmisson H, Lange N, Duntley SP (2019) Sleep apnea detection: accuracy of using automated ECG analysis compared to manually scored polysomnography (apnea hypopnea index). Sleep and Breath 23(1):125\u2013133","journal-title":"Sleep and Breath"},{"issue":"3","key":"6013_CR14","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1109\/JBHI.2018.2842919","volume":"23","author":"A Zarei","year":"2018","unstructured":"Zarei A, Asl BM (2018) Automatic detection of obstructive sleep apnea using wavelet transform and entropy-based features from single-lead ECG signal. IEEE J Biomed Health Inf 23(3):1011\u20131021","journal-title":"IEEE J Biomed Health Inf"},{"key":"6013_CR15","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.sleep.2015.11.021","volume":"20","author":"WY Pan","year":"2016","unstructured":"Pan WY, Su MC, Wu HT, Su TJ, Lin MC, Sun CK (2016) Multiscale entropic assessment of autonomic dysfunction in patients with obstructive sleep apnea and therapeutic impact of continuous positive airway pressure treatment. Sleep Med 20:12\u201317","journal-title":"Sleep Med"},{"key":"6013_CR16","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.cmpb.2017.01.001","volume":"140","author":"N Pombo","year":"2017","unstructured":"Pombo N, Garcia N, Bousson K (2017) Classification techniques on computerized systems to predict and\/or to detect Apnea: A systematic review. Comput Method Program Biomed 140:265\u2013274","journal-title":"Comput Method Program Biomed"},{"key":"6013_CR17","doi-asserted-by":"crossref","unstructured":"Pinho A, Pombo N, Silva BM, Bousson K, Garcia N (2019) Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection. Applied Soft Computing, https:\/\/doi.org\/10.1016\/j.asoc.2019.105568","DOI":"10.1016\/j.asoc.2019.105568"},{"key":"6013_CR18","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.bspc.2016.05.009","volume":"29","author":"AR Hassan","year":"2016","unstructured":"Hassan AR (2016) Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting. Biomed Signal Process Contr 29:22\u201330","journal-title":"Biomed Signal Process Contr"},{"key":"6013_CR19","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.compbiomed.2016.08.012","volume":"77","author":"H Sharma","year":"2016","unstructured":"Sharma H, Sharma KK (2016) An algorithm for sleep apnea detection from single-lead ECG using Hermite basis functions. Comput Bio Med 77:116\u2013124","journal-title":"Comput Bio Med"},{"issue":"1","key":"6013_CR20","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.emc.2005.08.013","volume":"24","author":"JL Garvey","year":"2006","unstructured":"Garvey JL (2006) ECG techniques and technologies. Emergency Med Clinics 24(1):209\u2013225","journal-title":"Emergency Med Clinics"},{"issue":"1887","key":"6013_CR21","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1098\/rsta.2008.0232","volume":"367","author":"A Voss","year":"2008","unstructured":"Voss A, Schulz S, Schroeder R, Baumert M, Caminal P (2008) Methods derived from nonlinear dynamics for analysing heart rate variability. Philo Trans Royal Society A: Math, Phys Eng Sci 367(1887):277\u2013296","journal-title":"Philo Trans Royal Society A: Math, Phys Eng Sci"},{"issue":"1","key":"6013_CR22","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1109\/TASE.2014.2345667","volume":"12","author":"L Chen","year":"2014","unstructured":"Chen L, Zhang X, Song C (2014) An automatic screening approach for obstructive sleep apnea diagnosis based on single-lead electrocardiogram. IEEE Trans Automat Sci Eng 12(1):106\u2013115","journal-title":"IEEE Trans Automat Sci Eng"},{"issue":"3","key":"6013_CR23","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1109\/TIM.2016.2642758","volume":"66","author":"S Raj","year":"2017","unstructured":"Raj S, Ray KC (2017) ECG signal analysis using DCT-based DOST and PSO optimized SVM. IEEE Trans Instrument Measure 66(3):470\u2013478","journal-title":"IEEE Trans Instrument Measure"},{"issue":"3","key":"6013_CR24","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1088\/0967-3334\/32\/3\/002","volume":"32","author":"UR Acharya","year":"2011","unstructured":"Acharya UR, Chua ECP, Faust O, Lim TC, Lim LFB (2011) Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters. Physiologic Measure 32(3):287","journal-title":"Physiologic Measure"},{"issue":"3","key":"6013_CR25","doi-asserted-by":"crossref","first-page":"914","DOI":"10.3390\/e17030914","volume":"17","author":"A Ravelo-Garcia","year":"2015","unstructured":"Ravelo-Garcia A, Navarro-Mesa J, Casanova-Blancas U, Martin-Gonzalez S, Quintana-Morales P, Guerra-Moreno I, Hernandez-Perez E (2015) Application of the permutation entropy over the heart rate variability for the improvement of electrocardiogram-based sleep breathing pause detection. Entropy 17(3):914\u2013927","journal-title":"Entropy"},{"key":"6013_CR26","doi-asserted-by":"crossref","unstructured":"Salsone M, Vescio B, Quattrone A, Roccia F, Sturniolo M, Bono F, Quattrone A (2018) Cardiac parasympathetic index identifies subjects with adult obstructive sleep apnea: A simultaneous polysomnographic-heart rate variability study. PloS One 13(3):e0193879","DOI":"10.1371\/journal.pone.0193879"},{"key":"6013_CR27","doi-asserted-by":"crossref","unstructured":"Raiesdana S (2018) Automated sleep staging of OSAs based on ICA preprocessing and consolidation of temporal correlations. Australasian Phys Eng Sci Med 41(1): 161-176","DOI":"10.1007\/s13246-018-0624-0"},{"key":"6013_CR28","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.cmpb.2017.12.020","volume":"156","author":"F Vaquerizo-Villar","year":"2018","unstructured":"Vaquerizo-Villar F, Alvarez D, Kheirandish-Gozal L, Gutierrez-Tobal GC, Barroso-Garcia V, Crespo A, Hornero R (2018) Utility of bispectrum in the screening of pediatric sleep apnea-hypopnea syndrome using oximetry recordings. Comput Method Program Biomed 156:141\u2013149","journal-title":"Comput Method Program Biomed"},{"key":"6013_CR29","doi-asserted-by":"crossref","unstructured":"Martin-Gonzalez S, Navarro-Mesa JL, Julia-Serda G, Ramirez-Avila GM, Ravelo-Garcia AG (2018) Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold. PloS One 13(4):e0194462","DOI":"10.1371\/journal.pone.0194462"},{"key":"6013_CR30","doi-asserted-by":"crossref","unstructured":"Pearson M, Faust O (2019) Heart-rate based sleep apnea detection using Arduino. J Mech Med Bio 19(01):1940006","DOI":"10.1142\/S0219519419400062"},{"key":"6013_CR31","doi-asserted-by":"crossref","unstructured":"Sivakumar S, Nedumaran D (2018) Discrete time-frequency signal analysis and processing techniques for non-stationary signals. J Applied Math Phys 6(09): 1916","DOI":"10.4236\/jamp.2018.69163"},{"key":"6013_CR32","doi-asserted-by":"crossref","unstructured":"Ghobadi Azbari P, Mohaqeqi S, Ghanbarzadeh Gashti N, Mikaili M (2016) Introducing a combined approach of empirical mode decomposition and PCA methods for maternal and fetal ECG signal processing. The J Maternal-Fetal & Neonatal Med 29(19): 3104-3109","DOI":"10.3109\/14767058.2015.1114089"},{"key":"6013_CR33","first-page":"372","volume":"94","author":"L Lu","year":"2016","unstructured":"Lu L, Yan J, de Silva CW (2016) Feature selection for ECG signal processing using improved genetic algorithm and empirical mode decomposition. Measure 94:372\u2013381","journal-title":"Measure"},{"key":"6013_CR34","doi-asserted-by":"crossref","unstructured":"Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Liu HH (1998) The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. The Royal Society 454(1971): 903-995","DOI":"10.1098\/rspa.1998.0193"},{"key":"6013_CR35","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.cam.2012.07.012","volume":"240","author":"B Huang","year":"2013","unstructured":"Huang B, Kunoth A (2013) An optimization based empirical mode decomposition scheme. J Comput Applied Math 240:174\u2013183","journal-title":"J Comput Applied Math"},{"issue":"6","key":"6013_CR36","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1016\/j.neucom.2010.07.030","volume":"74","author":"C Park","year":"2011","unstructured":"Park C, Looney D, Van Hulle MM, Mandic DP (2011) The complex local mean decomposition. Neurocomput 74(6):867\u2013875","journal-title":"Neurocomput"},{"key":"6013_CR37","doi-asserted-by":"crossref","unstructured":"Chen B, He Z, Chen X, Cao H, Cai G, Zi Y (2011) A demodulating approach based on local mean decomposition and its applications in mechanical fault diagnosis. Measure Sci Technol 22(5):055704","DOI":"10.1088\/0957-0233\/22\/5\/055704"},{"key":"6013_CR38","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.mechmachtheory.2015.08.001","volume":"94","author":"Y Li","year":"2015","unstructured":"Li Y, Xu M, Wei Y, Huang W (2015) Rotating machine fault diagnosis based on intrinsic characteristic-scale decomposition. Mech Mach Theory 94:9\u201327","journal-title":"Mech Mach Theory"},{"issue":"8","key":"6013_CR39","doi-asserted-by":"crossref","first-page":"3560","DOI":"10.1109\/TSP.2011.2143711","volume":"59","author":"I Selesnick","year":"2011","unstructured":"Selesnick I (2011) Wavelet transform with tunable Q-factor. IEEE Trans Signal Process 59(8):3560\u20133575","journal-title":"IEEE Trans Signal Process"},{"key":"6013_CR40","doi-asserted-by":"crossref","unstructured":"Nishad A, Pachori RB, Acharya UR (2018) Application of TQWT based filter-bank for sleep apnea screening using ECG signals. J Ambient Intell Human Comput, https:\/\/doi.org\/10.1007\/s12652-018-0867-3","DOI":"10.1007\/s12652-018-0867-3"},{"key":"6013_CR41","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.asoc.2016.11.002","volume":"50","author":"S Patidar","year":"2017","unstructured":"Patidar S, Pachori RB, Upadhyay A, Acharya UR (2017) An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism. Applied Soft Comput 50:71\u201378","journal-title":"Applied Soft Comput"},{"key":"6013_CR42","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.cmpb.2016.09.008","volume":"137","author":"AR Hassan","year":"2016","unstructured":"Hassan AR, Siuly S, Zhang Y (2016) Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput Method Program Biomed 137:247\u2013259","journal-title":"Comput Method Program Biomed"},{"issue":"3","key":"6013_CR43","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","volume":"62","author":"K Dragomiretskiy","year":"2014","unstructured":"Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531\u2013544","journal-title":"IEEE Trans Signal Process"},{"issue":"4","key":"6013_CR44","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1088\/0967-3334\/37\/4\/530","volume":"37","author":"A Mert","year":"2016","unstructured":"Mert A (2016) ECG feature extraction based on the bandwidth properties of variational mode decomposition. Physiologic Measure 37(4):530","journal-title":"Physiologic Measure"},{"issue":"8","key":"6013_CR45","doi-asserted-by":"crossref","first-page":"3245","DOI":"10.1007\/s00034-018-0804-x","volume":"37","author":"GJ Lal","year":"2018","unstructured":"Lal GJ, Gopalakrishnan EA, Govind D (2018) Epoch estimation from emotional speech signals using variational mode decomposition. Circuits, Syst, Signal Process 37(8):3245\u20133274","journal-title":"Circuits, Syst, Signal Process"},{"issue":"8","key":"6013_CR46","doi-asserted-by":"crossref","first-page":"3821","DOI":"10.1109\/JSTARS.2016.2529702","volume":"9","author":"YJ Xue","year":"2016","unstructured":"Xue YJ, Cao JX, Wang DX, Du HK, Yao Y (2016) Application of the variational-mode decomposition for seismic time-frequency analysis. IEEE J Selected Topics in Applied Earth Observ Remote Sens 9(8):3821\u20133831","journal-title":"IEEE J Selected Topics in Applied Earth Observ Remote Sens"},{"key":"6013_CR47","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.ymssp.2016.09.032","volume":"86","author":"Y Wang","year":"2017","unstructured":"Wang Y, Liu F, Jiang Z, He S, Mo Q (2017) Complex variational mode decomposition for signal processing applications. Mech Syst Signal Process 86:75\u201385","journal-title":"Mech Syst Signal Process"},{"key":"6013_CR48","first-page":"255","volume":"27","author":"T Penzel","year":"2000","unstructured":"Penzel T, Moody GB, Mark RG, Goldberger AL, Peter JH (2000) The apnea-ECG database. Comput Cardio 27:255\u2013258","journal-title":"Comput Cardio"},{"key":"6013_CR49","doi-asserted-by":"crossref","unstructured":"Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2003) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215\u2013e220","DOI":"10.1161\/01.CIR.101.23.e215"},{"issue":"1\u20133","key":"6013_CR50","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s10994-014-5460-1","volume":"101","author":"Y Sun","year":"2015","unstructured":"Sun Y, Li J, Liu J, Chow C, Sun B, Wang R (2015) Using causal discovery for feature selection in multivariate numerical time series. Mach Learn 101(1\u20133):377\u2013395","journal-title":"Mach Learn"},{"issue":"1\u20134","key":"6013_CR51","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0022-1694(01)00573-X","volume":"258","author":"B Sivakumar","year":"2002","unstructured":"Sivakumar B (2002) A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers. J Hydrology 258(1\u20134):149\u2013162","journal-title":"J Hydrology"},{"issue":"1","key":"6013_CR52","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.cmpb.2014.04.012","volume":"116","author":"SH Lee","year":"2014","unstructured":"Lee SH, Lim JS, Kim JK, Yang J, Lee Y (2014) Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance. Comput Method Program Biomed 116(1):10\u201325","journal-title":"Comput Method Program Biomed"},{"key":"6013_CR53","doi-asserted-by":"crossref","unstructured":"Takens F (1980) Detecting strange attractors in turbulence, in: Dynamical Systems and Turbulence, Warwick 1980, Springer, Berlin\/Heidelberg, 1981, pp 366-381","DOI":"10.1007\/BFb0091924"},{"key":"6013_CR54","doi-asserted-by":"crossref","unstructured":"Xu B, Jacquir S, Laurent G, Bilbault JM, Binczak S (2013) Phase space reconstruction of an experimental model of cardiac field potential in normal and arrhythmic conditions, In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 3274-3277","DOI":"10.1109\/EMBC.2013.6610240"},{"issue":"4","key":"6013_CR55","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1016\/j.bbe.2017.08.005","volume":"37","author":"RY Karimui","year":"2017","unstructured":"Karimui RY, Azadi S (2017) Cardiac arrhythmia classification using the phase space sorted by Poincare sections. Biocybern Biomed Eng 37(4):690\u2013700","journal-title":"Biocybern Biomed Eng"},{"issue":"8","key":"6013_CR56","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1007\/s40430-018-1313-3","volume":"40","author":"FR Lopes","year":"2018","unstructured":"Lopes FR, de Gois JAM (2018) ECG model parameters optimization and space state reconstruction. J Brazilian Soc Mech Sci Eng 40(8):399","journal-title":"J Brazilian Soc Mech Sci Eng"},{"key":"6013_CR57","doi-asserted-by":"crossref","unstructured":"Sedgwick P (2012) Pearson\u2019s correlation coefficient. BMJ 345:e4483","DOI":"10.1136\/bmj.e4483"},{"issue":"6","key":"6013_CR58","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1002\/asi.10242","volume":"54","author":"P Ahlgren","year":"2003","unstructured":"Ahlgren P, Jarneving B, Rousseau R (2003) Requirements for a cocitation similarity measure, with special reference to Pearson\u2019s correlation coefficient. J American Soc Inf Sci Technol 54(6):550\u2013560","journal-title":"J American Soc Inf Sci Technol"},{"key":"6013_CR59","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.ins.2017.12.059","volume":"435","author":"Y Mu","year":"2018","unstructured":"Mu Y, Liu X, Wang L (2018) A Pearson\u2019s correlation coefficient based decision tree and its parallel implementation. Inf Sci 435:40\u201358","journal-title":"Inf Sci"},{"key":"6013_CR60","doi-asserted-by":"crossref","first-page":"736","DOI":"10.3389\/fnins.2019.00736","volume":"13","author":"N Moradi","year":"2019","unstructured":"Moradi N, Dousty M, Sotero RC (2019) Spatiotemporal empirical mode decomposition of resting-state fMRI signals: application to global signal regression. Frontier Neurosci 13:736","journal-title":"Frontier Neurosci"},{"key":"6013_CR61","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.isatra.2019.01.038","volume":"91","author":"Y Cheng","year":"2019","unstructured":"Cheng Y, Wang Z, Chen B, Zhang W, Huang G (2019) An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis. ISA Trans 91:218\u2013234","journal-title":"ISA Trans"},{"issue":"1","key":"6013_CR62","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3390\/s20010007","volume":"20","author":"L de Santiago","year":"2020","unstructured":"de Santiago L, Ortiz del Castillo M, Garcia-Martin E, Rodrigo MJ, Sanchez Morla EM, Cavaliere C, Boquete L (2020) Empirical mode decomposition-based filter applied to multifocal electroretinograms in multiple sclerosis diagnosis. Sensors 20(1):7","journal-title":"Sensors"},{"key":"6013_CR63","doi-asserted-by":"crossref","first-page":"669","DOI":"10.3389\/fbioe.2020.00669","volume":"8","author":"M Zhou","year":"2020","unstructured":"Zhou M, Bian K, Hu F, Lai W (2020) A new method based on CEEMD combined with iterative feature reduction for aided diagnosis of epileptic EEG. Frontier Bioeng Biotechnol 8:669","journal-title":"Frontier Bioeng Biotechnol"},{"issue":"03","key":"6013_CR64","first-page":"147","volume":"17","author":"CC Chiu","year":"2005","unstructured":"Chiu CC, Lin TH, Liau BY (2005) Using correlation coefficient in ECG waveform for arrhythmia detection. Biomed Eng: Appl, Basis Commu 17(03):147\u2013152","journal-title":"Biomed Eng: Appl, Basis Commu"},{"issue":"1","key":"6013_CR65","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1109\/TNN.2005.860843","volume":"17","author":"C Wang","year":"2006","unstructured":"Wang C, Hill DJ (2006) Learning from neural control. IEEE Trans Neural Netw 17(1):130\u2013146","journal-title":"IEEE Trans Neural Netw"},{"issue":"3","key":"6013_CR66","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1109\/TNN.2006.889496","volume":"18","author":"C Wang","year":"2007","unstructured":"Wang C, Hill DJ (2007) Deterministic learning and rapid dynamical pattern recognition. IEEE Trans Neural Netw 18(3):617\u2013630","journal-title":"IEEE Trans Neural Netw"},{"key":"6013_CR67","volume-title":"Deterministic Learning Theory for Identification","author":"C Wang","year":"2009","unstructured":"Wang C, Hill DJ (2009) Deterministic Learning Theory for Identification. CRC Press, Boca Raton, FL, Recognition and Control"},{"key":"6013_CR68","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1007\/s00521-012-1324-4","volume":"24","author":"AT Azar","year":"2014","unstructured":"Azar AT, El-Said SA (2014) Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl 24:1163\u20131177","journal-title":"Neural Comput Appl"},{"issue":"3","key":"6013_CR69","first-page":"175","volume":"11","author":"K Chu","year":"1999","unstructured":"Chu K (1999) An introduction to sensitivity, specificity, predictive values and likelihood ratios. Emerg Med Australasia 11(3):175\u2013181","journal-title":"Emerg Med Australasia"},{"key":"6013_CR70","doi-asserted-by":"crossref","unstructured":"Yuan Q, Cai C, Xiao H, Liu X, Wen Y (2007) Diagnosis of breast tumours and evaluation of prognostic risk by using machine learning approaches. In D. S. Huang, L. Heutte, & M. Loog (eds.), Advanced intelligent computing theories and applications. With aspects of contemporary intelligent computing techniques (pp 1250-1260). Springer","DOI":"10.1007\/978-3-540-74282-1_141"},{"key":"6013_CR71","volume-title":"Statistical learning theory","author":"VN Vapnik","year":"1998","unstructured":"Vapnik VN (1998) Statistical learning theory. Wiley, New York"},{"key":"6013_CR72","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.compbiomed.2019.03.016","volume":"108","author":"CS Viswabhargav","year":"2019","unstructured":"Viswabhargav CS, Tripathy RK, Acharya UR (2019) Automated detection of sleep apnea using sparse residual entropy features with various dictionaries extracted from heart rate and EDR signals. Comput Biology and Med 108:20\u201330","journal-title":"Comput Biology and Med"},{"issue":"3","key":"6013_CR73","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1088\/0967-3334\/31\/3\/001","volume":"31","author":"MO Mendez","year":"2010","unstructured":"Mendez MO, Corthout J, Van Huffel S, Matteucci M, Penzel T, Cerutti S, Bianchi AM (2010) Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis. Physiologic Measure 31(3):273","journal-title":"Physiologic Measure"},{"issue":"9","key":"6013_CR74","doi-asserted-by":"crossref","first-page":"2269","DOI":"10.1109\/TBME.2015.2422378","volume":"62","author":"C Varon","year":"2015","unstructured":"Varon C, Caicedo A, Testelmans D, Buyse B, Van Huffel S (2015) A novel algorithm for the automatic detection of sleep apnea from single-lead ECG. IEEE Trans Biomed Eng 62(9):2269\u20132278","journal-title":"IEEE Trans Biomed Eng"},{"issue":"10","key":"6013_CR75","doi-asserted-by":"crossref","first-page":"3092","DOI":"10.1109\/JSEN.2017.2690805","volume":"17","author":"A Smruthy","year":"2017","unstructured":"Smruthy A, Suchetha M (2017) Real-time classification of healthy and apnea subjects using ECG signals with variational mode decomposition. IEEE Sens J 17(10):3092\u20133099","journal-title":"IEEE Sens J"},{"issue":"1","key":"6013_CR76","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s13534-017-0055-y","volume":"8","author":"D Dey","year":"2018","unstructured":"Dey D, Chaudhuri S, Munshi S (2018) Obstructive sleep apnoea detection using convolutional neural network based deep learning framework. Biomed Eng Lett 8(1):95\u2013100","journal-title":"Biomed Eng Lett"},{"key":"6013_CR77","doi-asserted-by":"crossref","unstructured":"Sharma H, Sharma KK (2020) Sleep apnea detection from ECG using variational mode decomposition. Biomed Phys & Eng Exp 6(1): 015026","DOI":"10.1088\/2057-1976\/ab68e9"},{"key":"6013_CR78","first-page":"1","volume":"70","author":"Q Shen","year":"2021","unstructured":"Shen Q, Qin H, Wei K, Liu G (2021) Multiscale deep neural network for obstructive sleep apnea detection using RR interval from single-lead ECG signal. IEEE Trans Inst Measure 70:1\u201313","journal-title":"IEEE Trans Inst Measure"},{"key":"6013_CR79","doi-asserted-by":"crossref","unstructured":"Zarei A, Beheshti H, Asl BM (2022) Detection of sleep apnea using deep neural networks and single-lead ECG signals. Biomed Signal Process Contr 71: 103125","DOI":"10.1016\/j.bspc.2021.103125"},{"key":"6013_CR80","doi-asserted-by":"crossref","unstructured":"Yang Q, Zou L, Wei K, Liu G (2022) Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network. Comput Bio Med 140:105124","DOI":"10.1016\/j.compbiomed.2021.105124"},{"issue":"11","key":"6013_CR81","doi-asserted-by":"crossref","first-page":"5281","DOI":"10.1109\/JBHI.2023.3304299","volume":"27","author":"S Hu","year":"2023","unstructured":"Hu S, Liu J, Yang C, Wang A, Li K, Liu W (2023) Semi-supervised learning for low-cost personalized obstructive sleep apnea detection using unsupervised deep learning and single-lead electrocardiogram. IEEE J Biomed Health Inf 27(11):5281\u20135292","journal-title":"IEEE J Biomed Health Inf"},{"key":"6013_CR82","doi-asserted-by":"crossref","unstructured":"Venkataraman V, Turaga P (2016) Shape distributions of nonlinear dynamical systems for video-based inference. IEEE Trans Pattern Anal Mach Intell 38(12):2531\u20132543","DOI":"10.1109\/TPAMI.2016.2533388"},{"key":"6013_CR83","doi-asserted-by":"crossref","unstructured":"Som A, Krishnamurthi N, Venkataraman V, Turaga P (2016) Attractor-shape descriptors for balance impairment assessment in Parkinson\u2019s disease. In: IEEE Conference on Engineering in Medicine and Biology Society, pp 3096-3100","DOI":"10.1109\/EMBC.2016.7591384"},{"issue":"3\u20134","key":"6013_CR84","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1007\/BF01053745","volume":"65","author":"T Sauer","year":"1991","unstructured":"Sauer T, Yorke JA, Casdagli M (1991) Embedology. J Stat Phys 65(3\u20134):579\u2013616","journal-title":"J Stat Phys"},{"issue":"4","key":"6013_CR85","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1109\/TSA.2005.848885","volume":"13","author":"MT Johnson","year":"2005","unstructured":"Johnson MT, Povinelli RJ, Lindgren AC, Ye J, Liu X, Indrebo KM (2005) Time-domain isolated phoneme classification using reconstructed phase spaces. IEEE Trans Speech and Audio Process 13(4):458\u2013466","journal-title":"IEEE Trans Speech and Audio Process"},{"key":"6013_CR86","unstructured":"Michael S (2005) Applied nonlinear time series analysis: applications in physics, physiology and finance (Vol 52). World Sci"},{"key":"6013_CR87","doi-asserted-by":"crossref","unstructured":"Zhang X, Yao L, Wang X, Monaghan J, Mcalpine D, Zhang Y (2021) A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. J Neural Eng 18(3):031002","DOI":"10.1088\/1741-2552\/abc902"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06013-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-06013-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06013-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T15:06:43Z","timestamp":1737385603000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-06013-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,9]]},"references-count":87,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6013"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-06013-9","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,9]]},"assertion":[{"value":"24 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"There is no issue with Ethical approval and Informed consent.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Standards"}}],"article-number":"102"}}