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Hybridization of soft-computing algorithms with neural network for prediction obstructive sleep apnea using biomedical sensor measurements

Abstract

Sleep apnea (SA) is a common respiratory disorder, especially among obese people. It is caused by either the relaxation of the upper respiratory tract muscles or the failure of the neural signal to reach the muscles responsible for breathing, both of which interrupt the patient’s sleep–wake cycles. The traditional method for diagnosing this disorder, based on polysomnography, is complicated, vexing, expensive, time-consuming, and requires both sleep centers and specialized staff capable of connecting electrodes to the patient’s body. This paper proposes an SA prediction system based on merging five soft computing algorithms, specifically, combining the multi-verse optimizer (MVO) with an artificial neural network (ANN) to leverage measurements from heart rate, SpO2, and chest movement sensors. The most substantial novelty of this research is the hybridization of MVO and ANN (MVO-ANN), which improves the ANN performance by selecting the best learning rate and number of neurons in hidden ANN layers. This enables highly accurate prediction of sleep apnea events. This work’s experimental results reveal that the MVO-ANN performs better than other algorithms, with mean absolute errors of 0.042, 0.202, and 0.166 for training, testing, and validation of the ANN. In addition, the SA prediction system achieved an accuracy of 98.67%, a sensitivity of 96.71%, and a specificity of 99.24%. These results provide good evidence that the proposed method can reliably predict respiratory events in people suffering from SA.

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References

  1. Ferduła R, Walczak T, Cofta S (2019) The application of artificial neural network in diagnosis of sleep apnea syndrome. In: Advances in manufacturing II. Springer, pp 432–443

  2. Thorey V, Hernandez AB, Arnal PJ (2019) During EH AI vs humans for the diagnosis of sleep apnea. In: 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC), Berlin, Germany, Germany, 23–27. pp 1596–1600. https://doi.org/10.1109/EMBC.2019.8856877

  3. De Falco I, De Pietro G, Della Cioppa A, Sannino G, Scafuri U, Tarantino E (2019) Evolution-based configuration optimization of a deep neural network for the classification of obstructive sleep apnea episodes. Future Gener Comput Syst 98:377–391. https://doi.org/10.1016/j.future.2019.01.049

    Article  Google Scholar 

  4. Dong Z, Xu X, Wang C, Cartledge S, Maddison R, Islam SMS (2020) Association of overweight and obesity with obstructive sleep apnoea: a systematic review and meta-analysis. Obes Med 17:100185

    Article  Google Scholar 

  5. Chyad MH, Gharghan SK, Hamood HQ (2020) A survey on detection and prediction methods for sleep apnea. IOP Conf Ser: Mater Sci Eng 1:012102

    Article  Google Scholar 

  6. Badr MS, Javaheri S (2019) Central sleep apnea: a brief review. Curr Pulmonol Rep 8(1):14–21

    Article  Google Scholar 

  7. Collen J, Lettieri C, Wickwire E, Holley A (2020) Obstructive sleep apnea and cardiovascular disease, a story of confounders! Sleep Breath 1–15

  8. Yao X, Li M, Yao L, Shao L (2020) Obstructive sleep apnea and hypertension. In: Secondary hypertension. Springer, pp 461–488

  9. Qie R, Zhang D, Liu L, Ren Y, Zhao Y, Liu D, Liu F, Chen X, Cheng C, Guo C (2020) Obstructive sleep apnea and risk of type 2 diabetes mellitus: a systematic review and dose-response meta-analysis of cohort studies. J Diabetes 12(6):455–464

    Article  Google Scholar 

  10. Ruchała M, Bromińska B, Cyrańska-Chyrek E, Kuźnar-Kamińska B, Kostrzewska M, Batura-Gabryel H (2017) Obstructive sleep apnea and hormones–a novel insight. Arch Med Sci 13(4):875

    Article  Google Scholar 

  11. Kamble PG, Theorell-Haglöw J, Wiklund U, Franklin KA, Hammar U, Lindberg E, Eriksson JW (2020) Sleep apnea in men is associated with altered lipid metabolism, glucose tolerance, insulin sensitivity, and body fat percentage. Endocrine 1–10

  12. Zhou J, Wu X-m, Zeng W-j (2015) Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine. J Clin Monit Comput 29(6):767–772

    Article  Google Scholar 

  13. Almendros I, Martinez-Garcia MA, Farré R, Gozal D (2020) Obesity, sleep apnea, and cancer. Int J Obes 44:1653–1667

    Article  Google Scholar 

  14. McNab AA (2007) The eye and sleep apnea. Sleep Med Rev 11(4):269–276

    Article  Google Scholar 

  15. Mendonça F, Mostafa SS, Morgado-Dias F, Navarro-Mesa JL, Juliá-Serdá G, Ravelo-García AG (2018) A portable wireless device based on oximetry for sleep apnea detection. Computing 100(11):1203–1219

    Article  Google Scholar 

  16. Haidar R, Koprinska I, Jeffries B (2017) Sleep apnea event detection from nasal airflow using convolutional neural networks. In: International conference on neural information processing, Guangzhou, China, 14–18. Springer, pp 819–827

  17. Mendonca F, Mostafa SS, Ravelo-García AG, Morgado-Dias F, Penzel T (2018) A review of obstructive sleep apnea detection approaches. IEEE J Biomed Health Inform 23(2):825–837

    Article  Google Scholar 

  18. Haoyu L, Jianxing L, Arunkumar N, Hussein AF, Jaber MM (2019) An IoMT cloud-based real time sleep apnea detection scheme by using the SpO2 estimation supported by heart rate variability. Future Gener Comput Syst 98:69–77

    Article  Google Scholar 

  19. Hang L-W, Wang H-L, Chen J-H, Hsu J-C, Lin H-H, Chung W-S, Chen Y-F (2015) Validation of overnight oximetry to diagnose patients with moderate to severe obstructive sleep apnea. BMC Pulm Med 15(1):24

    Article  Google Scholar 

  20. Gutiérrez-Tobal GC, Kheirandish-Gozal L, Álvarez D, Crespo A, Philby MF, Mohammadi M, del Campo F, Gozal D, Hornero R (2015) Analysis and classification of oximetry recordings to predict obstructive sleep apnea severity in children. In: 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Milan, Italy, 25–29. IEEE, pp 4540–4543

  21. Sánchez-Morillo D, López-Gordo M, León A (2014) Novel multiclass classification for home-based diagnosis of sleep apnea hypopnea syndrome. Expert Syst Appl 41(4):1654–1662

    Article  Google Scholar 

  22. Marcos JV, Hornero R, Alvarez D, Aboy M, Del Campo F (2011) Automated prediction of the apnea-hypopnea index from nocturnal oximetry recordings. IEEE Trans Biomed Eng 59(1):141–149

    Article  Google Scholar 

  23. Oliver N, Flores-Mangas F (2007) Healthgear: Automatic sleep apnea detection and monitoring with a mobile phone. J Commun 2(2):1–9

    Article  Google Scholar 

  24. Chesson AL Jr, Berry RB, Pack A (2003) Practice parameters for the use of portable monitoring devices in the investigation of suspected obstructive sleep apnea in adults. Sleep 26(7):907–913

    Article  Google Scholar 

  25. Kalkbrenner C, Eichenlaub M, Rüdiger S, Kropf-Sanchen C, Rottbauer W, Brucher R (2018) Apnea and heart rate detection from tracheal body sounds for the diagnosis of sleep-related breathing disorders. Med Biol Eng Comput 56(4):671–681

    Article  Google Scholar 

  26. Yadollahi A, Giannouli E, Moussavi Z (2010) Sleep apnea monitoring and diagnosis based on pulse oximetery and tracheal sound signals. Med Biol Eng Comput 48(11):1087–1097

    Article  Google Scholar 

  27. Chen L, Zhang X, Wang H (2015) An obstructive sleep apnea detection approach using kernel density classification based on single-lead electrocardiogram. J Med Syst 39(5):47

    Article  Google Scholar 

  28. Almuhammadi WS, Aboalayon KA, Faezipour M (2015) Efficient obstructive sleep apnea classification based on EEG signals. In: Long Island systems, applications and technology, Farmingdale, NY, USA, 1–1. IEEE, pp 1–6

  29. Malaekah E, Patti CR, Cvetkovic D (2014) Automatic sleep-wake detection using electrooculogram signals. In: IEEE conference on biomedical engineering and sciences (IECBES), Kuala Lumpur, Malaysia, 8–10. IEEE, pp 724–728

  30. Kopaczka M, Oezkan O, Merhof D (2017) Face tracking and respiratory signal analysis for the detection of sleep apnea in thermal infrared videos with head movement. In: International conference on image analysis and processing, Catania-Italy, 11–15. Springer, pp 163–170

  31. Hung PD (2018) Central sleep apnea detection using an accelerometer. In: International conference on control and computer vision, Singapore, Singapore 15–18. ACM, pp 106–111

  32. Gharghan SK, Nordin R, Ismail M, Ali JA (2015) Accurate wireless sensor localization technique based on hybrid PSO-ANN algorithm for indoor and outdoor track cycling. IEEE Sens J 16(2):529–541

    Article  Google Scholar 

  33. Erdenebayar U, Kim YJ, Park J-U, Joo EY, Lee K-J (2019) Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram. Comput Methods Programs Biomed 180:105001. https://doi.org/10.1016/j.cmpb.2019.105001

    Article  Google Scholar 

  34. Hafezi M, Montazeri N, Saha S, Zhu K, Gavrilovic B, Yadollahi A, Taati B (2020) Sleep apnea severity estimation from tracheal movements using a deep learning model. IEEE Access 8:22641–22649

    Article  Google Scholar 

  35. Mahmud T, Khan IA, Mahmud TI, Fattah SA, Zhu W-P, Ahmad MO (2020) Sleep apnea event detection from sub-frame based feature variation in EEG signal using deep convolutional neural network. In: 42nd Annual international conference of the IEEE engineering in medicine & biology society (EMBC), Montreal, QC, Canada, 20–24. IEEE, pp 5580–5583

  36. Sankar AB, Selvi JAV, Kumar D, Lakshmi KS (2013) Effective enhancement of classification of respiratory states using feed forward back propagation neural networks. Sadhana 38(3):377–395

    Article  Google Scholar 

  37. Vimala V, Ramar K, Ettappan M (2019) An intelligent sleep apnea classification system based on EEG signals. J Med Syst 43(2):36

    Article  Google Scholar 

  38. Wang T, Lu C, Shen G (2019) Detection of sleep apnea from single-lead ECG signal using a time window artificial neural network. Biomed Res Int. https://doi.org/10.1155/2019/9768072

    Article  Google Scholar 

  39. Van Steenkiste T, Groenendaal W, Dreesen P, Lee S, Klerkx S, De Francisco R, Deschrijver D, Dhaene T (2020) Portable detection of apnea and hypopnea events using bio-impedance of the chest and deep learning. IEEE J Biomed Health Inform 24(9):2589–2598

    Article  Google Scholar 

  40. Hassan O, Parvin D, Kamrul S (2020) Machine learning model based digital hardware system design for detection of sleep apnea among neonatal infants. In: 63rd international midwest symposium on circuits and systems (MWSCAS), Springfield, MA, USA, 9–12. IEEE, pp 607–610

  41. Liang X, Qiao X, Li Y (2019) Obstructive sleep apnea detection using combination of CNN and LSTM techniques. In: 8th Joint international information technology and artificial intelligence conference (ITAIC), Chongqing, China, 24–26. IEEE, pp 1733–1736

  42. Toften S, Kjellstadli JT, Tyvold SS, Moxness MHS (2021) A pilot study of detecting individual sleep apnea events using noncontact radar technology, pulse oximetry, and machine learning. J Sens 2021:2998202. https://doi.org/10.1155/2021/2998202

    Article  Google Scholar 

  43. Alvarez D, Hornero R, Marcos JV, del Campo F (2010) Multivariate analysis of blood oxygen saturation recordings in obstructive sleep apnea diagnosis. IEEE Trans Biomed Eng 57(12):2816–2824

    Article  Google Scholar 

  44. Lin SH, Branson C, Park L, Leung J, Doshi N, Auerbach SH (2018) Oximetry as an accurate tool for identifying moderate to severe sleep apnea in patients with acute stroke. J Clin Sleep Med 14(12):2065–2073

    Article  Google Scholar 

  45. Nigro CA, Dibur E, Rhodius E (2011) Pulse oximetry for the detection of obstructive sleep apnea syndrome: can the memory capacity of oxygen saturation influence their diagnostic accuracy? Sleep Disorders 2011:427028. https://doi.org/10.1155/2011/427028

    Article  Google Scholar 

  46. Garde A, Dehkordi P, Wensley D, Ansermino JM, Dumont GA (2015) Pulse oximetry recorded from the Phone Oximeter for detection of obstructive sleep apnea events with and without oxygen desaturation in children. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), MiCo - Milano Conference Center - Milan, Italy, August 25–29. IEEE, pp 7692–7695

  47. Zubaidi SL, Ortega-Martorell S, Al-Bugharbee H, Olier I, Hashim KS, Gharghan SK, Kot P, Al-Khaddar R (2020) Urban water demand prediction for a city that suffers from climate change and population growth: Gauteng province case study. Water 12(7):1885

  48. Zubaidi SL, Abdulkareem IH, Hashim KS, Al-Bugharbee H, Ridha HM, Gharghan SK, Al-Qaim FF, Muradov M, Kot P, Al-Khaddar R (2020) Hybridised artificial neural network model with slime mould algorithm: a novel methodology for prediction of urban stochastic water demand. Water 12(10):2692

    Article  Google Scholar 

  49. Munadhil Z, Gharghan SK, Mutlag AH, Al-Naji A, Chahl J (2020) Neural network-based Alzheimer’s patient localization for wireless sensor network in an indoor environment. IEEE Access 8:150527–150538

    Article  Google Scholar 

  50. Gohari M, Rahman RA, Raja RI, Tahmasebi M (2012) A novel artificial neural network biodynamic model for prediction seated human body head acceleration in vertical direction. J Low Freq Noise Vib Act Control 31(3):205–216

    Article  Google Scholar 

  51. Gohari M, Rahman R, Tahmasebi M, Nejat P (2014) Off-road vehicle seat suspension optimisation, part I: derivation of an artificial neural network model to predict seated human spine acceleration in vertical vibration. J Low Freq Noise Vib Act Control 33(4):429–441

    Article  Google Scholar 

  52. Henríquez PA, Ruz GA (2018) A non-iterative method for pruning hidden neurons in neural networks with random weights. Appl Soft Comput 70:1109–1121

    Article  Google Scholar 

  53. Motahar S, Jahangiri M (2020) Transient heat transfer analysis of a phase change material heat sink using experimental data and artificial neural network. Appl Therm Eng 167:114817

    Article  Google Scholar 

  54. Ang ZH, Ang CK, Lim WH, Yu LJ, Solihin MI (2020) Development of an artificial intelligent approach in adapting the characteristic of polynomial trajectory planning for robot manipulator. Int J Mech Eng Robot Res 9(3):408–414

    Article  Google Scholar 

  55. Mahdi SQ, Gharghan SK, Hasan MA (2021) FPGA-Based neural network for accurate distance estimation of elderly falls using WSN in an indoor environment. Measurement 167:108276

    Article  Google Scholar 

  56. Kapanova KG, Dimov I, Sellier JM (2018) A genetic approach to automatic neural network architecture optimization. Neural Comput Appl 29(5):1481–1492. https://doi.org/10.1007/s00521-016-2510-6

    Article  Google Scholar 

  57. Alemu HZ, Wu W, Zhao J (2018) Feedforward neural networks with a hidden layer regularization method. Symmetry 10(10):525

    Article  Google Scholar 

  58. Zubaidi SL, Hashim K, Ethaib S, Al-Bdairi NSS, Al-Bugharbee H, Gharghan SK (2020) A novel methodology to predict monthly municipal water demand based on weather variables scenario. J King Saud Univ-Eng Sci. https://doi.org/10.1016/j.jksues.2020.09.011

    Article  Google Scholar 

  59. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Article  Google Scholar 

  60. Fathy A, Rezk H (2018) Multi-verse optimizer for identifying the optimal parameters of PEMFC model. Energy 143:634–644. https://doi.org/10.1016/j.energy.2017.11.014

    Article  Google Scholar 

  61. Shukri SE, Al-Sayyed R, Hudaib A, Mirjalili S (2021) Enhanced multi-verse optimizer for task scheduling in cloud computing environments. Expert Syst Appl 168:114230. https://doi.org/10.1016/j.eswa.2020.114230

    Article  Google Scholar 

  62. Tabrizchi H, Tabrizchi M, Tabrizchi H (2020) Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree. SN Appl Sci 2(4):752. https://doi.org/10.1007/s42452-020-2575-9

    Article  Google Scholar 

  63. Tuncer SA, Akılotu B, Toraman S (2019) A deep learning-based decision support system for diagnosis of OSAS using PTT signals. Med Hypotheses 127:15–22

    Article  Google Scholar 

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Acknowledgements

The author would like to thank the staff of the Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University, and Al-Kafeel Super Specialty Hospital in Karbala for their support during this study.

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Correspondence to Sadik Kamel Gharghan.

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Chyad, M.H., Gharghan, S.K., Hamood, H.Q. et al. Hybridization of soft-computing algorithms with neural network for prediction obstructive sleep apnea using biomedical sensor measurements. Neural Comput & Applic 34, 8933–8957 (2022). https://doi.org/10.1007/s00521-022-06919-w

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Keywords

  • Artificial neural network
  • Heart rate
  • Prediction
  • SpO2
  • Sleep apnea
  • Soft computing algorithm