Skip to main content

Advertisement

SpringerLink
Log in

Automated detection of cyclic alternating pattern and classification of sleep stages using deep neural network

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The visual sleep stages scoring by human experts is the current gold standard for sleep analysis. However, this method is tedious, time-consuming, prone to human errors, and unable to detect microstructure of sleep such as cyclic alternating pattern (CAP) which is an important diagnostic factor for the detection of sleep disorders such as insomnia and obstructive sleep apnea (OSA). The CAP is only observed as subtle changes in the electroencephalogram (EEG) signals during non-rapid eye movement (NREM) sleep, making it very difficult for human experts to discern. Hence, it is important to have an automated system developed using artificial intelligence for accurate and robust detection of CAP and sleep stages classification. In this study, a deep learning model based on 1-dimensional convolutional neural network (1D-CNN) is proposed for CAP detection and homogenous 3-class sleep stages classification, namely wakefulness (W), rapid eye movement (REM) and NREM sleep. The proposed model is developed using standardized EEG recordings. Our developed CNN network achieved good model performance for 3-class sleep stages classification with a classification accuracy of 90.46%. Our proposed model also yielded a classification accuracy of 73.64% using balanced CAP dataset, and sensitivity of 92.06% with unbalanced CAP dataset. Our proposed model correctly identified majority of A-phases which comprised of only 12.6% in the unbalanced dataset. The performance of the developed prototype is ready to be tested with more data before clinical application.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Cho JW, Duffy JF (2019) Sleep, sleep disorders, and sexual dysfunction. In world journal of men’s health. Korean Society for Sexual Medicine and Andrology 37(3):261–275. https://doi.org/10.5534/wjmh.180045

    Article  Google Scholar 

  2. Colten HR, Altevogt BM (2006) Institute of Medicine (US) committee on sleep medicine and research (ed) sleep disorders and sleep deprivation: an unmet public health problem. National Academies Press (US). https://doi.org/10.17226/11617

  3. Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM (2013) Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol 177(9):1006–1014. https://doi.org/10.1093/aje/kws342

    Article  Google Scholar 

  4. Roth T (2007) Insomnia: definition, prevalence, etiology, and consequences. J Clin Sleep Med 3(5 Suppl):S7–S10 http://www.ncbi.nlm.nih.gov/pubmed/17824495

  5. Redline S, Yenokyan G, Gottlieb DJ, Shahar E, O’Connor GT, Resnick HE, Diener-West M, Sanders MH, Wolf PA, Geraghty EM, Ali T, Lebowitz M, Punjabi NM (2010) Obstructive sleep apnea–hypopnea and incident stroke. Am J Respir Crit Care Med 182(2):269–277. https://doi.org/10.1164/rccm.200911-1746OC

    Article  Google Scholar 

  6. Khan MS, Aouad R (2017) The effects of insomnia and sleep loss on cardiovascular disease. Sleep Med Clin 12(2):167–177. https://doi.org/10.1016/j.jsmc.2017.01.005

    Article  Google Scholar 

  7. Hargens TA, Kaleth AS, Edwards ES, Butner KL (2013) Association between sleep disorders, obesity, and exercise: a review. Nat Sci Sleep 1(5):27–35. https://doi.org/10.2147/NSS.S34838

    Article  Google Scholar 

  8. Stranges S, Tigbe W, Gómez-Olivé FX, Thorogood M, Kandala NB (2012) Sleep problems: an emerging global epidemic? Findings from the INDEPTH WHO-SAGE study among more than 40,000 older adults from 8 countries across Africa and Asia. Sleep 35(8):1173–1181. https://doi.org/10.5665/sleep.2012

    Article  Google Scholar 

  9. Koyanagi A, Stickley A (2015) The association between sleep problems and psychotic symptoms in the general population: a global perspective. Sleep 38(12):1875–1885. https://doi.org/10.5665/sleep.5232

    Article  Google Scholar 

  10. Chattu VK, Manzar MD, Kumary S, Burman D, Spence DW, Pandi-Perumal SR (2018) The global problem of insufficient sleep and its serious public health implications. Healthcare 7(1):1. https://doi.org/10.3390/healthcare7010001

    Article  Google Scholar 

  11. Loh HW, Ooi CP, Vicnesh J, Oh SL, Faust O, Gertych A, Acharya UR (2020) Automated detection of sleep stages using deep learning techniques: a systematic review of the last decade (2010–2020). Appl Sci 10(24):8963. https://doi.org/10.3390/app10248963

    Article  Google Scholar 

  12. Hori T, Sugita Y, Koga E, Shirakawa S, Inoue K, Uchida S, Kuwahara H, Kousaka M, Kobayashi T, Tsuji Y, Terashima M, Fukuda K, Fukuda N, Sleep Computing Committee of the Japanese Society of Sleep Research Society (2001) Proposed supplements and amendments to “a manual of standardized terminology, techniques and scoring system for sleep stages of human subjects”, the Rechtschaffen & Kales (1968) standard. Psychiatry Clin Neurosci 55(3):305–310. https://doi.org/10.1046/j.1440-1819.2001.00810.x

    Article  Google Scholar 

  13. Iber C, Ancoli-Israel S, Chesson AL, Quan SF (2007) The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specification. Darien, IL, USA, American Academy of Sleep Medicine

    Google Scholar 

  14. Carley DW, Farabi SS (2016) Physiology of sleep. Diabetes Spectr 29(1):5–9. https://doi.org/10.2337/diaspect.29.1.5

    Article  Google Scholar 

  15. Brain Basics: Understanding Sleep. National Institute of Neurological Disorders and Stroke. https://www.ninds.nih.gov/Disorders/patient-caregiver-education/Understanding-sleep. Accessed 17-Oct-2020

  16. Gais S, Mölle M, Helms K, Born J (2002) Learning-dependent increases in sleep spindle density. J Neurosci 22(15):6830–6834. https://doi.org/10.1523/JNEUROSCI.22-15-06830.2002

    Article  Google Scholar 

  17. Takahara M, Kanayama S, Nittono H, Hori T (2006) REM sleep EEG pattern: examination by a new EEG scoring system for REM sleep period. Sleep Biol Rhythms 4(2):105–110. https://doi.org/10.1111/j.1479-8425.2006.00201.x

    Article  Google Scholar 

  18. Schulz H (2008) Rethinking sleep analysis. J Clin Sleep Med 4(2):99–103

    Article  Google Scholar 

  19. Kemp B, Zwinderman AH, Tuk B, Kamphuisen HA, Oberyé JJ (2000) Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans Biomed Eng 47(9):1185–1194. https://doi.org/10.1109/10.867928

    Article  Google Scholar 

  20. Pardey J, Roberts S, Tarassenko L, Stradling J (1996) A new approach to the analysis of the human sleep/wakefulness continuum. J Sleep Res 5(4):201–210. https://doi.org/10.1111/j.1365-2869.1996.00201.x

    Article  Google Scholar 

  21. Halász P (1998) Hierarchy of micro-arousals and the microstructure of sleep. Neurophysiol Clin Neurophysiol 28(6):461–475. https://doi.org/10.1016/S0987-7053(99)80016-1

    Article  Google Scholar 

  22. Terzano MG, Mancia D, Salati MR, Costani G, Decembrino A, Parrino L (1985) The cyclic alternating pattern as a physiologic component of normal NREM sleep. Sleep 8(2):137–145. https://doi.org/10.1093/sleep/8.2.137

    Article  Google Scholar 

  23. Terzano MG, Parrino L, Sherieri A, Chervin R, Chokroverty S, Guilleminault C, Hirshkowitz M, Mahowald M, Moldofsky H, Rosa A, Thomas R, Walters A (2001) Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. Sleep Med 2(6):537–553. https://doi.org/10.1016/S1389-9457(01)00149-6

    Article  Google Scholar 

  24. Machado F, Sales F, Santos C, Dourado A, Teixeira CA (2018) A knowledge discovery methodology from EEG data for cyclic alternating pattern detection. Biomed Eng Online 17(1):185. https://doi.org/10.1186/s12938-018-0616-z

    Article  Google Scholar 

  25. Dhok S, Pimpalkhute V, Chandurkar A, Bhurane AA, Sharma M, Acharya UR (2020) Automated phase classification in cyclic alternating patterns in sleep stages using Wigner–Ville distribution based features. Comput Biol Med 119:103691. https://doi.org/10.1016/j.compbiomed.2020.103691

    Article  Google Scholar 

  26. Parrino L, Ferri R, Bruni O, Terzano MG (2012) Cyclic alternating pattern (CAP): the marker of sleep instability. Sleep Med Rev 16(1):27–45. https://doi.org/10.1016/j.smrv.2011.02.003

    Article  Google Scholar 

  27. Korkmaz S, Bilecenoglu NT, Aksu M, Yoldas TK (2018) Cyclic alternating pattern in obstructive sleep apnea patients with versus without excessive sleepiness. Sleep Disord 2018:1–7. https://doi.org/10.1155/2018/8713409

    Article  Google Scholar 

  28. Parrino L, Halasz P, Tassinari CA, Terzano MG (2006) CAP, epilepsy and motor events during sleep: the unifying role of arousal. Sleep Med Rev 10(4):267–285. https://doi.org/10.1016/j.smrv.2005.12.004

    Article  Google Scholar 

  29. Thomas RJ (2003) Arousals in sleep-disordered breathing: patterns and implications. Sleep 26(8):1042–1047. https://doi.org/10.1093/sleep/26.8.1042

    Article  Google Scholar 

  30. Kassab MY, Farooq MU, Diaz-Arrastia R, Van Ness PC (2007) The clinical significance of EEG cyclic alternating pattern during coma. J Clin Neurophysiol 24(6):425–428. https://doi.org/10.1097/WNP.0b013e31815a028e

    Article  Google Scholar 

  31. Michielli N, Acharya UR, Molinari F (2019) Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Comput Biol Med 106:71–81. https://doi.org/10.1016/j.compbiomed.2019.01.013

    Article  Google Scholar 

  32. Tripathy RK, Acharya UR (2018) Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework. Biocybern Biomed Eng 38(4):890–902. https://doi.org/10.1016/j.bbe.2018.05.005

    Article  Google Scholar 

  33. Cimr D, Studnicka F, Fujita H, Cimler R, Slegr J (2021) Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements. Comput Methods Prog Biomed 106149:106149. https://doi.org/10.1016/j.cmpb.2021.106149

    Article  Google Scholar 

  34. Svetnik V, Ma J, Soper KA, Doran S, Renger JJ, Deacon S, Koblan KS (2007) Evaluation of automated and semi-automated scoring of Polysomnographic recordings from a clinical trial using Zolpidem in the treatment of insomnia. Sleep 30(11):1562–1574. https://doi.org/10.1093/sleep/30.11.1562

    Article  Google Scholar 

  35. Pittman SD, MacDonald MM, Fogel RB, Malhotra A, Todros K, Levy B, Geva AB, White DP (2004) Assessment of automated scoring of polysomnographic recordings in a population with suspected sleep-disordered breathing. Sleep 27(7):1394–1403. https://doi.org/10.1093/sleep/27.7.1394

    Article  Google Scholar 

  36. Anderer P, Gruber G, Parapatics S, Woertz M, Miazhynskaia T, Klosch G, Saletu B, Zeitlhofer J, Barbanoj MJ, Danker-Hopfe H, Himanen SL, Kemp B, Penzel T, Grozinger M, Kunz D, Rappelsberger P, Schlogl A, Dorffner G (2005) An E-health solution for automatic sleep classification according to Rechtschaffen and kales: validation study of the Somnolyzer 24 × 7 utilizing the siesta database. Neuropsychobiology 51(3):115–133. https://doi.org/10.1159/000085205

    Article  Google Scholar 

  37. Rahman MM, Bhuiyan MIH, Hassan AR (2018) Sleep stage classification using single-channel EOG. Comput Biol Med 102:211–220. https://doi.org/10.1016/j.compbiomed.2018.08.022

    Article  Google Scholar 

  38. Yildirim O, Baloglu UB, Acharya UR (2019) A deep learning model for automated sleep stages classification using PSG signals. Int J Environ Res Public Health 16(4). https://doi.org/10.3390/ijerph16040599

  39. Mendez MO, Chouvarda I, Alba A, Bianchi AM, Grassi A, Arce-Santana E, Milioli G, Terzano MG, Parrino L (2016) Analysis of A-phase transitions during the cyclic alternating pattern under normal sleep. Med Biol Eng Comput 54(1):133–148. https://doi.org/10.1007/s11517-015-1349-9

    Article  Google Scholar 

  40. Hartmann S, Baumert M (2019) Automatic A-phase detection of cyclic alternating patterns in sleep using dynamic temporal information. IEEE Trans Neural Syst Rehabil Eng 27(9):1695–1703. https://doi.org/10.1109/TNSRE.2019.2934828

    Article  Google Scholar 

  41. Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):E215–E220. https://doi.org/10.1161/01.cir.101.23.e215

    Article  Google Scholar 

  42. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427–437. https://doi.org/10.1016/j.ipm.2009.03.002

    Article  Google Scholar 

  43. Lipton ZC, Elkan C, Naryanaswamy B (2014) Optimal thresholding of classifiers to maximize F1 measure. Mach Learn Knowl Discov Databases 8725:225–239. https://doi.org/10.1007/978-3-662-44851-9_15

    Article  Google Scholar 

  44. da Silveira TL, Kozakevicius AJ, Rodrigues CR (2017) Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain. Med Biol Eng Comput 55(2):343–352. https://doi.org/10.1007/s11517-016-1519-4

    Article  Google Scholar 

  45. Hassan AR, Bhuiyan MIH (2017) Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting. Comput Methods Prog Biomed 140:201–210. https://doi.org/10.1016/j.cmpb.2016.12.015

    Article  Google Scholar 

  46. Hassan AR, Bhuiyan MIH (2016) Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomed Signal Process Control 24:1–10. https://doi.org/10.1016/j.bspc.2015.09.002

    Article  Google Scholar 

  47. Zhu G, Li Y, Wen P (2014) Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal. IEEE J Biomed Health Inform 18(6):1813–1821. https://doi.org/10.1109/JBHI.2014.2303991

    Article  Google Scholar 

  48. Sharma M, Goyal D, Achuth PV, Acharya UR (2018) An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank. Comput Biol Med 98:58–75. https://doi.org/10.1016/j.compbiomed.2018.04.025

    Article  Google Scholar 

  49. Navona C, Barcaro U, Bonanni E, Di Martino F, Maestri M, Murri L (2002) An automatic method for the recognition and classification of the A-phases of the cyclic alternating pattern. Clin Neurophysiol 113(11):1826–1831. https://doi.org/10.1016/S1388-2457(02)00284-5

    Article  Google Scholar 

  50. Mariani S, Grassi A, Mendez MO, Milioli G, Parrino L, Terzano MG, Bianchi AM (2013) EEG segmentation for improving automatic CAP detection. Clin Neurophysiol 124(9):1815–1823. https://doi.org/10.1016/j.clinph.2013.04.005

    Article  Google Scholar 

  51. Mariani S, Manfredini E, Rosso V, Grassi A, Mendez MO, Alba A, Matteucci M, Parrino L, Terzano MG, Cerutti S, Bianchi AM (2012) Efficient automatic classifiers for the detection of a phases of the cyclic alternating pattern in sleep. Med Biol Eng Comput 50(4):359–372. https://doi.org/10.1007/s11517-012-0881-0

    Article  Google Scholar 

  52. Mendonça F, Fred A, Mostafa SS, Morgado-Dias F, Ravelo-García AG (2018) Automatic detection of cyclic alternating pattern. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3474-5

  53. Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Prog Biomed 161:1–13. https://doi.org/10.1016/j.cmpb.2018.04.005

    Article  Google Scholar 

  54. Faust O, Razaghi H, Barika R, Ciaccio EJ, Acharya UR (2019) A review of automated sleep stage scoring based on physiological signals for the new millennia. Comput Methods Prog Biomed 176:81–91. https://doi.org/10.1016/j.cmpb.2019.04.032

    Article  Google Scholar 

  55. Mirza B, Wang W, Wang J, Choi H, Chung NC, Ping P (2019) Machine learning and integrative analysis of biomedical big data. Genes (Basel) 10(2):87. https://doi.org/10.3390/genes10020087

    Article  Google Scholar 

  56. Shoeibi A, Ghassemi N, Khodatars M, Jafari M, Hussain S, Alizadehsani R, Moridian P, Khosravi A, Hosseini-Nejad H, Rouhani M, Zare A, Khadem A, Nahavandi S, Atiya AF, Acharya UR (2020) Epileptic seizure detection using deep learning techniques: A Review. http://arxiv.org/abs/2007.01276

Download references

Author information

Authors and Affiliations

  1. School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore

    Hui Wen Loh, Chui Ping Ooi & U. Rajendra Acharya

  2. Department of Electronics and Communication Engineering, Indian Institute of Information Technology Nagpur (IIITN), Nagpur, India

    Shivani G. Dhok

  3. Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India

    Manish Sharma

  4. Department of Electronics and Communication, Visvesvaraya National Institute of Technology (VNIT), Nagpur, India

    Ankit A. Bhurane

  5. School of Engineering, Ngee Ann Polytechnic, Ngee Ann, Singapore

    U. Rajendra Acharya

  6. Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan

    U. Rajendra Acharya

  7. International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan

    U. Rajendra Acharya

  8. School of Management and Enterprise, University of Southern Queensland, Toowoomba, Australia

    U. Rajendra Acharya

Authors
  1. Hui Wen Loh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chui Ping Ooi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shivani G. Dhok
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manish Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ankit A. Bhurane
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. U. Rajendra Acharya
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to this article. The idea for the article was provided by HWL and URA. SGD provided MATLAB code for data preparation. HWL developed and the model and drafted the first manuscript. Subsequently, CPO, SGD, MS, AAB, and URA edited the manuscript and provided suggestions to improve the manuscript.

Corresponding author

Correspondence to U. Rajendra Acharya.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Loh, H.W., Ooi, C.P., Dhok, S.G. et al. Automated detection of cyclic alternating pattern and classification of sleep stages using deep neural network. Appl Intell 52, 2903–2917 (2022). https://doi.org/10.1007/s10489-021-02597-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02597-8

Keywords

Search

Navigation