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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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
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
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
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
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
Badr MS, Javaheri S (2019) Central sleep apnea: a brief review. Curr Pulmonol Rep 8(1):14–21
Collen J, Lettieri C, Wickwire E, Holley A (2020) Obstructive sleep apnea and cardiovascular disease, a story of confounders! Sleep Breath 1–15
Yao X, Li M, Yao L, Shao L (2020) Obstructive sleep apnea and hypertension. In: Secondary hypertension. Springer, pp 461–488
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
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
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
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
Almendros I, Martinez-Garcia MA, Farré R, Gozal D (2020) Obesity, sleep apnea, and cancer. Int J Obes 44:1653–1667
McNab AA (2007) The eye and sleep apnea. Sleep Med Rev 11(4):269–276
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
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
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
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
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
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
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
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
Oliver N, Flores-Mangas F (2007) Healthgear: Automatic sleep apnea detection and monitoring with a mobile phone. J Commun 2(2):1–9
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
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
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
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
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
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
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
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
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
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
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
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
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
Vimala V, Ramar K, Ettappan M (2019) An intelligent sleep apnea classification system based on EEG signals. J Med Syst 43(2):36
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Alemu HZ, Wu W, Zhao J (2018) Feedforward neural networks with a hidden layer regularization method. Symmetry 10(10):525
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
Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513
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
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
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
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
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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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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DOI: https://doi.org/10.1007/s00521-022-06919-w