End-to end decision support system for sleep apnea detection and Apnea-Hypopnea Index calculation using hybrid feature vector and Machine learning

• Autorzy: Arslan Recep Sinan , Ulutas Hasan , Köksal Ahmet Sertol , Bakir Mehmet , Çiftçi Bülent

Identyfikatory

DOI

10.1016/j.bbe.2023.10.002

• Języki publikacji: EN

Abstrakty

EN

Sleep apnea is a disease that occurs due to the decrease in oxygen saturation in the blood and directly affects people’s lives. Detection of sleep apnea is crucial for assessing sleep quality. It is also an important parameter in the diagnosis of various other diseases (diabetes, chronic kidney disease, depression, and cardiological diseases). Recent studies show that detection of sleep apnea can be done via signal processing, especially EEG and ECG signals. However, the detection accuracy needs to be improved. In this paper, a ML model is used for the detection of sleep apnea using 19 static sensor data and 2 dynamic data (Sleep score and Arousal). The sensor data is recorded as a discrete signal and the sleep process is divided into 4.8 M segments. In this work, 19 different sensor data sets were recorded with polysomnography (PSG). These data sets have been used to perform sleep scoring. Then, arousal status marking is done. Model training was carried out with the feature vector consisting of 21 data obtained. Tests were performed with eight different machine learning techniques on a unique dataset consisting of 113 patients. After all, it was automatically determined whether people were diseased (a kind of apnea) or healthy. The proposed model had an average accuracy of 97.27%, while the recall, precision, and f-score values were 99.18%, 95.32%, and 97.20%, respectively. After all, the model that less feature engineering, less complex classification model, higher dataset usage, and higher classification performance has been revealed.

Słowa kluczowe

EN

sleep apnea hypopnea detection; machine learning; discrete signal processing; AHI

PL

bezdech senny; uczenie maszynowe; przetwarzanie sygnału; AHI

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 4
• Strony: 684--699
• Opis fizyczny: Bibliogr. 59 poz., rys., tab., wykr.

Twórcy

autor

Arslan Recep Sinan

• Kayseri University, Department of Computer Engineering, Talas, Kayseri, Turkey

autor

Ulutas Hasan

• Department of Computer Engineering, Faculty of Engineering and Architecture, Bozok University, Yozgat, Turkey

autor

Köksal Ahmet Sertol

• Department of Computer Engineering, Faculty of Engineering and Architecture, Bozok University, Yozgat, Turkey

autor

Bakir Mehmet

• Department of Computer Engineering, Faculty of Engineering and Architecture, Bozok University, Yozgat, Turkey

autor

Çiftçi Bülent

• Department of Chest Diseases, Faculty of Medicine, Yuksek Ihtisas University, Ankara, Turkey

Bibliografia

• [1] Guilleminault C, Sullivan SS, Huang YS. Sleep-disordered breathing, orofacial growth, and prevention of obstructive sleep apnea. Sleep Med Clin 2019;14(1):13-20.
• [2] Dheda K, Gumbo T, Maartens G, Dooley KE, Murray M, Furin J, et al. The Lancet Respiratory Medicine Commission: 2019 update: epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant and incurable tuberculosis. Lancet Respir Med 2019;7(9):820-6.
• [3] Nassi TE. Algorithms for automated scoring of respiratory events in sleep. University of Twente; 2021 [Master’s thesis].
• [4] Giannoni A, Gentile F, Sciarrone P, Borrelli C, Pasero G, Mirizzi G, et al. Upright Cheyne-Stokes respiration in patients with heart failure. J Am Coll Cardiol 2020;75(23):2934-46.
• [5] Malhotra A, Ayappa I, Ayas N, Collop N, Kirsch D, Mcardle N, et al. Metrics of sleep apnea severity: beyond the apneahypopnea index. Sleep 2021;44(7):zsab030.
• [6] Hilmisson H, Lange N, Duntley SP. Sleep apnea detection: accuracy of using automated ECG analysis compared to manually scored polysomnography (apnea hypopnea index). Sleep Breath 2019;23(1):125-33.
• [7] Matsumoto T, Chin K. Prevalence of sleep disturbances: sleep disordered breathing, short sleep duration, and non-restorative sleep. Respir Investig 2019;7(3):227-37.
• [8] Ravesloot MJL, Vonk PE, Maurer JT, Oksenberg A, De Vries N. Standardized framework to report on the role of sleeping position in sleep apnea patients. Sleep Breath 2021;25:1717-28.
• [9] Arslan RS, Ulutas H, Köksal AS, Bakır M, Çiftçi B. Tree-based machine learning techniques for automated human sleep stage classification. Traitement du Signal 2023;40(4):1385-400. https://doi.org/10.18280/ts.400408.
• [10] Sateia MJ. International classification of sleep disorders. Chest 2014;146(5):1387-94.
• [11] Bozkurt F, Uçar MK, Bozkurt MR, Bilgin C. Detection of abnormal respiratory events with single channel ECG and hybrid machine learning model in patients with obstructive sleep apnea. Irbm 2020;41(5):241-51.
• [12] Mousavi S, Afghah F, Acharya UR. SleepEEGNet: automated sleep stage scoring with sequence to sequence deep learning approach. PLoS One 2019;14(5):e0216456.
• [13] Fiorillo L, Puiatti A, Papandrea M, Ratti PL, Favaro P, Roth C, et al. Automated sleep scoring: a review of the latest approaches. Sleep Med Rev 2019;48 101204.
• [14] Budhiraja R, Thomas R, Kim M, Redline S. The role of big data in the management of sleep-disordered breathing. Sleep Med Clin 2016;11(2):241-55.
• [15] Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D. Machine learning in agriculture: a comprehensive updated review. Sensors 2021;21(11):3758.
• [16] Stephen M. Machine learning: an algorithmic perspective. 2nd ed. Routledge & CRC Press. https://www.routledge.com/ Machine-Learning-An-Algorithmic-Perspective-SecondEdition/Marsland/p/book/9781466583283 [visited on 12/04/2021].
• [17] Xu S, Faust O, Silvia S, Chakraborty S, Barua PD, Loh HW, et al. A review of automated sleep disorder detection. Comput Biol Med 2022;106100.
• [18] Mostafa SS, Mendonca F, Ravelo-Garcia AG, Juliá-Serdá GG, Morgado-Dias F. Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection. IEEE Access 2020;8:129586-99.
• [19] Salari N, Hosseinian-Far A, Mohammadi M, Ghasemi H, Khazaie H, Daneshkhah A. Ahmadi A Detection of sleep apnea using Machine learning algorithms based on ECG Signals: a comprehensive systematic review. Expert Syst Appl 2020;187 115950.
• [20] Berry RB, Brooks R, Gamaldo C, Harding SM, Lloyd RM, Quan SF, et al. AASM scoring manual updates for 2017 (version 2.4). J Clin Sleep Med 2017;13(5):665-6.
• [21] Zhongxu Z, Fengxia W, Xuan Y, Li Z, Chang H, Jing X, Changzhi L, Hong H. Accurate contactless sleep apnea detection framework with signal processing and machine learning methods. Methods 2022:167-78.
• [22] Cao K, Lv X. Multi-task feature fusion network for Obstructive Sleep Apnea detection using single-lead ECG signal. Measurement 2022;02 111787.
• [23] Tsouti V, Kanaris AI, Tsoutis K, Chatzandroulis S. Development of an automated system for obstructive sleep apnea treatment based on machine learning and breath effort monitoring. Microelectron Eng 2020;231 111376.
• [24] Satapathy SK, Loganathan D. A study of human sleep stage classification based on dual channels of EEG signal using machine learning techniques. SN Computer Sci 2021;2(3):1-16.
• [25] Alpaydin E. Machine learning. MIT Press; 2021.
• [26] Li K, Pan W, Li Y, Jiang Q, Liu G. A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal. Neurocomputing 2018;294:94-101.
• [27] Van Steenkiste T, Groenendaal W, Deschrijver D, Dhaene T. Automated sleep apnea detection in raw respiratory signals using long short-term memory neural networks. IEEE J Biomed Health Inform 2018;23(6):2354-64.
• [28] Erdenebayar U, Kim YJ, Park JU, Joo EY, Lee KJ. Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram. Comput Methods Programs Biomed 2019;180 105001.
• [29] Mencar C, Gallo C, Mantero M, Tarsia P, Carpagnano GE, Foschino Barbaro MP, et al. Application of machine learning to predict obstructive sleep apnea syndrome severity. Health Informatics J 2020;26(1):298-317.
• [30] Tuncer SA, Akılotu B, Toraman S. A deep learning-based decision support system for diagnosis of OSAS using PTT signals. Med Hypotheses 2019;127:15-22.
• [31] Feng K, Qin H, Wu S, Pan W, Liu G. A sleep apnea detection method based on unsupervised feature learning and singlelead electrocardiogram. IEEE Trans Instrum Meas 2020;70:1-12.
• [32] Chang HY, Yeh CY, Lee CT, Lin CC. A sleep apnea detection system based on a one-dimensional deep convolution neural network model using single-lead electrocardiogram. Sensors 2020;20(15):4157.
• [33] ElMoaqet H, Kim J, Tilbury D, Ramachandran SK, Ryalat M, Chu CH. Gaussian mixture models for detecting sleep apnea events using single oronasal airflow record. Appl Sci 2020;10 (21):7889.
• [34] Mukherjee D, Dhar K, Schwenker F, Sarkar R. Ensemble of deep learning models for sleep apnea detection: an experimental study. Sensors 2021;21(16):5425.
• [35] Ramesh J, Keeran N, Sagahyroon A, Aloul F. Towards validating the effectiveness of obstructive sleep apnea classification from electronic health records using machine learning no. 11. In: Healthcare. MDPI; 2021. p. 1450.
• [36] Bricout A, Fontecave-Jallon J, Pépin JL, Guméry PY. Accelerometry-derived respiratory index estimating apnea-hypopnea index for sleep apnea screening. Comput Methods Programs Biomed 2021;207 106209.
• [37] Arslan RS, Ulutas H, Köksal AS, Bakır M, Çiftçi B. Automated sleep scoring system using multi-channel data and machine learning. Comput Biol Med 2022;146 105653.
• [38] Yang Q, Zou L, Wei K, Liu G. Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network. Comput Biol Med 2022;140 105124.
• [39] Jiménez-García J, García M, Gutiérrez-Tobal GC, KheirandishGozal L, Vaquerizo-Villar F, Álvarez D, et al. A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry. Comput Biol Med 2022;147 105784.
• [40] Arslan RS, Ulutas H, Köksal AS, Bakir M, Çiftçi B. Sensitive deep learning application on sleep stage scoring by using all PSG data. Neural Comput & Applic 2023;35(10):7495-508.
• [41] Han H, Oh J. Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity. Sci Rep 2023;13(1):6379.
• [42] Hu S, Liu J, Yang C, Wang A, Li K, Liu W. Semi-supervised learning for low-cost personalized obstructive sleep apnea detection using unsupervised deep learning and single-lead electrocardiogram. IEEE J Biomed Health Inform 2023.
• [43] Maniaci A, Riela PM, Iannella G, Lechien JR, La Mantia I, De Vincentiis M, et al. Machine learning identification of obstructive sleep apnea severity through the patient clinical features: a retrospective study. Life 2023;13(3):702.
• [44] Tyagi PK, Agrawal D. Automatic detection of sleep apnea from single-lead ECG signal using enhanced-deep belief network model. Biomed Signal Process Control 2023;80 104401.
• [45] Cheng L, Luo S, Li B, Liu R, Zhang Y, Zhang H. Multiple-instance learning for EEG based OSA event detection. Biomed Signal Process Control 2023;80 104358.
• [46] Pang B, Doshi S, Roy B, Lai M, Ehlert L, Aysola RS, et al. Machine learning approach for obstructive sleep apnea screening using brain diffusion tensor imaging. J Sleep Res 2023;32(1):e13729.
• [47] Strumpf Z, Gu W, Tsai CW, Chen PL, Yeh E, Leung L, et al. Belun Ring (Belun Sleep System BLS-100): deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea. Sleep. Health 2023.
• [48] Abdulla S, Diykh M, Siuly S, Ali M. An intelligent model involving multi-channels spectrum patterns based features for automatic sleep stage classification. Int J Med Inf 2023;171 105001.
• [49] Hemrajani P, Dhaka VS, Rani G, Shukla P, Bavirisetti DP. Efficient deep learning based hybrid model to detect obstructive sleep apnea. Sensors 2023;23(10):4692.
• [50] Huo J, Quan SF, Roveda J, Li A. BASH-GN: a new machine learning-derived questionnaire for screening obstructive sleep apnea. Sleep Breath 2023;27(2):449-57.
• [51] Wang S, Xuan W, Chen D, Gu Y, Liu F, Chen J, et al. Machine learning assisted wearable wireless device for sleep apnea syndrome diagnosis. Biosensors 2023;13(4):483.
• [52] Mlynczak M, Valdez TA, Kukwa W. Comparing sleep studies in terms of the apnea-hypopnea index using the dedicated Shiny web application. Biomed Signal Process Control 2021;68:1-7.
• [53] Ruehland WR, Rochford PD, O’Donoghue FJ, Pierce RJ, Singh P, Thornton AT. The new AASM criteria for scoring hypopneas: impact on the apnea hypopnea index. Sleep 2009;32(2):150-7.
• [54] Dong X, Yu Z, Cao W, Shi Y, Ma Q. A survey on Ensemble Learning. Front Comp Sci 2019;14(2):241-58.
• [55] Géron A. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media, Inc.; 2022.
• [56] Arslan RS. Sleep disorder and apnea events detection framework with high performance using two-tier learning model design. PeerJ Computer Science 2023;9:e1554. https:// doi.org/10.7717/peerj-cs.1554.
• [57] Zhou Y, Shu D, Xu H, Qiu Y, Zhou P, Ruan W, et al. Validation of novel automatic ultra-wideband radar for sleep apnea detection. J Thorac Dis 2020;12(4):1286.
• [58] Lai DKH, Yu ZH, Leung TYN, Lim HJ, Tam AYC, So BPH, et al. Vision transformers (ViT) for blanket-penetrating sleep posture recognition using a triple ultra-wideband (UWB) radar system. Sensors 2023;23(5):2475.
• [59] Lee WH, Kim SH, Na JY, Lim YH, Cho SH, Cho SH, et al. Noncontact sleep/wake monitoring using impulse-radio ultra wideband radar in neonates. Front Pediatr 2021;9 782623.