Skip to main content

Advertisement

Automatic detection of obstructive sleep apnea through nonlinear dynamics of single-lead ECG signals

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Obstructive Sleep Apnea (OSA) is a sleep disorder where the brain and body receive insufficient oxygen during sleep. Traditional diagnosis involves Polysomnography (PSG), which is time-consuming, tedious, subjective, and costly in clinical settings. To address these drawbacks, computer-assisted diagnosis techniques have emerged, utilizing a single physiological signal. This study aims to introduce an innovative method for automatically detecting OSA based on the dynamics of the ECG system. The approach combines tunable quality factor (Q-factor) wavelet transform (TQWT), variational mode decomposition (VMD), and three-dimensional (3D) phase space for feature extraction, capturing clinically relevant information from OSA ECG recordings. Neural networks are employed to model and identify ECG system dynamics via deterministic learning theory, classifying normal and OSA ECG signals based on differences in dynamics using a bank of dynamical estimators. An assessment is conducted utilizing a 10-fold cross-validation methodology on a PhysioNet apnea-ECG dataset, which comprises 70 nocturnal recordings derived from an equal number of subjects. The empirical outcomes demonstrate that the introduced approach, which amalgamates a classifier based on neural network principles and the recommended attributes, attains superior accuracy (98.27\(\%\)), sensitivity (97.68\(\%\)), and specificity (98.63\(\%\)) in contrast to conventional PSG. The results corroborate the suggested technique as a viable substitute for automatic OSA detection in a clinical setting.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Availability of Data and Materials

All the datasets used in this manuscript are publicly available datasets (Physionet apnea-ECG database [48, 49] , already in the public domain).

References

  1. Xie B, Minn H (2012) Real-time sleep apnea detection by classifier combina- tion. IEEE Trans Inf Technol Biomed 16:469–477

    Google Scholar 

  2. 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):1829–1836

    Google Scholar 

  3. 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):2838–2850

    Google Scholar 

  4. 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

  5. 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 100:100–113

    MATH  Google Scholar 

  6. Lichstein KL, Perlis ML (eds.) (2003) Treating sleep disorders: Principles and practice of behavioral sleep medicine. John Wiley & Sons

  7. 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 294:94–101

    MATH  Google Scholar 

  8. Burgos A, Goni A, Illarramendi A, Bermudez J (2009) Real-time detection of apneas on a PDA. IEEE Trans Inf Technol Biomed 14(4):995–1002

    MATH  Google Scholar 

  9. 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):231–239

    MATH  Google Scholar 

  10. Almazaydeh L, Elleithy K, Faezipour M, Abushakra A (2013) Apnea detection based on respiratory signal classification. Procedia Comput Sci 21:310–316

    Google Scholar 

  11. 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

  12. 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):416–427

    Google Scholar 

  13. 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):125–133

    Google Scholar 

  14. 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):1011–1021

    MATH  Google Scholar 

  15. 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 20:12–17

    MATH  Google Scholar 

  16. 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 140:265–274

    MATH  Google Scholar 

  17. 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

  18. Hassan AR (2016) Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting. Biomed Signal Process Contr 29:22–30

    MATH  Google Scholar 

  19. Sharma H, Sharma KK (2016) An algorithm for sleep apnea detection from single-lead ECG using Hermite basis functions. Comput Bio Med 77:116–124

    MATH  Google Scholar 

  20. Garvey JL (2006) ECG techniques and technologies. Emergency Med Clinics 24(1):209–225

    MATH  Google Scholar 

  21. 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):277–296

    MATH  Google Scholar 

  22. 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):106–115

    MathSciNet  MATH  Google Scholar 

  23. Raj S, Ray KC (2017) ECG signal analysis using DCT-based DOST and PSO optimized SVM. IEEE Trans Instrument Measure 66(3):470–478

    MATH  Google Scholar 

  24. 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

    MATH  Google Scholar 

  25. 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):914–927

    Google Scholar 

  26. 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

  27. 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

  28. 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 156:141–149

    Google Scholar 

  29. 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

  30. Pearson M, Faust O (2019) Heart-rate based sleep apnea detection using Arduino. J Mech Med Bio 19(01):1940006

  31. Sivakumar S, Nedumaran D (2018) Discrete time-frequency signal analysis and processing techniques for non-stationary signals. J Applied Math Phys 6(09): 1916

  32. 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

  33. Lu L, Yan J, de Silva CW (2016) Feature selection for ECG signal processing using improved genetic algorithm and empirical mode decomposition. Measure 94:372–381

    MATH  Google Scholar 

  34. 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

  35. Huang B, Kunoth A (2013) An optimization based empirical mode decomposition scheme. J Comput Applied Math 240:174–183

    MathSciNet  MATH  Google Scholar 

  36. Park C, Looney D, Van Hulle MM, Mandic DP (2011) The complex local mean decomposition. Neurocomput 74(6):867–875

    MATH  Google Scholar 

  37. 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

  38. Li Y, Xu M, Wei Y, Huang W (2015) Rotating machine fault diagnosis based on intrinsic characteristic-scale decomposition. Mech Mach Theory 94:9–27

    MATH  Google Scholar 

  39. Selesnick I (2011) Wavelet transform with tunable Q-factor. IEEE Trans Signal Process 59(8):3560–3575

    MathSciNet  MATH  Google Scholar 

  40. 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

  41. 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 50:71–78

    MATH  Google Scholar 

  42. 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 137:247–259

    Google Scholar 

  43. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544

    MathSciNet  MATH  Google Scholar 

  44. Mert A (2016) ECG feature extraction based on the bandwidth properties of variational mode decomposition. Physiologic Measure 37(4):530

    MATH  Google Scholar 

  45. Lal GJ, Gopalakrishnan EA, Govind D (2018) Epoch estimation from emotional speech signals using variational mode decomposition. Circuits, Syst, Signal Process 37(8):3245–3274

    MathSciNet  MATH  Google Scholar 

  46. 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):3821–3831

    MATH  Google Scholar 

  47. Wang Y, Liu F, Jiang Z, He S, Mo Q (2017) Complex variational mode decomposition for signal processing applications. Mech Syst Signal Process 86:75–85

    MATH  Google Scholar 

  48. Penzel T, Moody GB, Mark RG, Goldberger AL, Peter JH (2000) The apnea-ECG database. Comput Cardio 27:255–258

    MATH  Google Scholar 

  49. 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–e220

  50. 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(1–3):377–395

    MathSciNet  MATH  Google Scholar 

  51. Sivakumar B (2002) A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers. J Hydrology 258(1–4):149–162

    MATH  Google Scholar 

  52. 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):10–25

    MATH  Google Scholar 

  53. Takens F (1980) Detecting strange attractors in turbulence, in: Dynamical Systems and Turbulence, Warwick 1980, Springer, Berlin/Heidelberg, 1981, pp 366-381

  54. 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

  55. Karimui RY, Azadi S (2017) Cardiac arrhythmia classification using the phase space sorted by Poincare sections. Biocybern Biomed Eng 37(4):690–700

    MATH  Google Scholar 

  56. Lopes FR, de Gois JAM (2018) ECG model parameters optimization and space state reconstruction. J Brazilian Soc Mech Sci Eng 40(8):399

    MATH  Google Scholar 

  57. Sedgwick P (2012) Pearson’s correlation coefficient. BMJ 345:e4483

  58. Ahlgren P, Jarneving B, Rousseau R (2003) Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient. J American Soc Inf Sci Technol 54(6):550–560

    MATH  Google Scholar 

  59. Mu Y, Liu X, Wang L (2018) A Pearson’s correlation coefficient based decision tree and its parallel implementation. Inf Sci 435:40–58

    MathSciNet  MATH  Google Scholar 

  60. 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

    Google Scholar 

  61. 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 91:218–234

    MATH  Google Scholar 

  62. 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

    Google Scholar 

  63. 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

    MATH  Google Scholar 

  64. Chiu CC, Lin TH, Liau BY (2005) Using correlation coefficient in ECG waveform for arrhythmia detection. Biomed Eng: Appl, Basis Commu 17(03):147–152

    MATH  Google Scholar 

  65. Wang C, Hill DJ (2006) Learning from neural control. IEEE Trans Neural Netw 17(1):130–146

    MATH  Google Scholar 

  66. Wang C, Hill DJ (2007) Deterministic learning and rapid dynamical pattern recognition. IEEE Trans Neural Netw 18(3):617–630

    MATH  Google Scholar 

  67. Wang C, Hill DJ (2009) Deterministic Learning Theory for Identification. CRC Press, Boca Raton, FL, Recognition and Control

    MATH  Google Scholar 

  68. Azar AT, El-Said SA (2014) Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl 24:1163–1177

    MATH  Google Scholar 

  69. Chu K (1999) An introduction to sensitivity, specificity, predictive values and likelihood ratios. Emerg Med Australasia 11(3):175–181

    MathSciNet  MATH  Google Scholar 

  70. 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

  71. Vapnik VN (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  72. 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 108:20–30

    Google Scholar 

  73. 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

    Google Scholar 

  74. 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):2269–2278

    Google Scholar 

  75. 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):3092–3099

    MATH  Google Scholar 

  76. Dey D, Chaudhuri S, Munshi S (2018) Obstructive sleep apnoea detection using convolutional neural network based deep learning framework. Biomed Eng Lett 8(1):95–100

    MATH  Google Scholar 

  77. Sharma H, Sharma KK (2020) Sleep apnea detection from ECG using variational mode decomposition. Biomed Phys & Eng Exp 6(1): 015026

  78. 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 70:1–13

    MATH  Google Scholar 

  79. 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

  80. 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

  81. 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):5281–5292

    MATH  Google Scholar 

  82. Venkataraman V, Turaga P (2016) Shape distributions of nonlinear dynamical systems for video-based inference. IEEE Trans Pattern Anal Mach Intell 38(12):2531–2543

  83. Som A, Krishnamurthi N, Venkataraman V, Turaga P (2016) Attractor-shape descriptors for balance impairment assessment in Parkinson’s disease. In: IEEE Conference on Engineering in Medicine and Biology Society, pp 3096-3100

  84. Sauer T, Yorke JA, Casdagli M (1991) Embedology. J Stat Phys 65(3–4):579–616

    MathSciNet  MATH  Google Scholar 

  85. 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):458–466

    MATH  Google Scholar 

  86. Michael S (2005) Applied nonlinear time series analysis: applications in physics, physiology and finance (Vol 52). World Sci

  87. 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

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of Fujian Province (Grant no. 2022J011146).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Zeng.

Ethics declarations

Conflicts of Interest

There is no conflict of interest.

Ethical Standards

There is no issue with Ethical approval and Informed consent.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, L., Liu, F., Wang, Y. et al. Automatic detection of obstructive sleep apnea through nonlinear dynamics of single-lead ECG signals. Appl Intell 55, 102 (2025). https://doi.org/10.1007/s10489-024-06013-9

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10489-024-06013-9

Keywords