Classification of Welch Periodograms Using Deep Learning Methods: A Case Study on the Prediction of Sleep Apnea

Year 2025, Volume: 5 Issue: 2, 33 - 48, 01.10.2025

Emre Turan , Orhan Er

Abstract

This study proposes a novel approach for the diagnosis of sleep apnea using deep learning analysis of Welch Pediograms derived from electrocardiogram (ECG) signals. Despite affecting between 3% and 17% of the general adult population, depending on diagnostic criteria used, sleep apnea remains underdiagnosed due to limitations in traditional diagnostic methods, particularly polysomnography, which is costly, requires specialized facilities, and may disrupt natural sleep patterns. Our research addresses these challenges by developing an automated diagnostic system that analyzes frequency spectrum variations in RR intervals through Welch Pediograms. These visualizations, derived from Holter monitor recordings, effectively capture the characteristic frequency-domain changes in heart rate variability associated with sleep apnea episodes. Using Convolutional Neural Networks (CNNs), our methodology processes these spectral representations to identify patterns indicative of sleep apnea. This approach offers significant advantages including non-invasive monitoring, home-based testing capability, and improved patient comfort. Our findings contribute to both the methodological development in sleep medicine and the practical implementation of accessible diagnostic tools for sleep apnea, potentially improving early detection rates and patient outcomes.

Keywords

Sleep apnea , Electrocardiogram (ECG) , Welch Pediogram , Heart rate variability , Deep learning , Convolutional Neural Networks , Frequency-domain analysis , Home-based diagnostics , Non-invasive monitoring , Holter recordings , Signal processing , Automated detection , Spectral analysis

Ethical Statement

There is no ethical violation.

Supporting Institution

İzmir Bakırçay University Artificial Intelligence in Health Application and Research Centre

Thanks

This study was developed under the roof of Izmir Bakırçay University Artificial Intelligence Studies in Health Application and Research Centre and Amatis Information Technologies.

References

• [1] T. Young, P. Peppard, and D. Gottlieb, "Epidemiology of obstructive sleep apnea: a population health perspective," American Journal of Respiratory and Critical Care Medicine, vol. 165, no. 9, pp. 1217-1239, 2002, doi: 10.1164/rccm.2109080.
• [2] N. M. Punjabi, "The epidemiology of adult obstructive sleep apnea," Proceedings of the American Thoracic Society, vol. 5, no. 2, pp. 136-143, 2008, doi: 10.1513/pats.200709-155MG.
• [3] V. K. Somers, D. P. White, R. Amin, W. T. Abraham, F. Costa, A. Culebras, S. Daniels, J. S. Floras, C. E. Hunt, L. J. Olson, T. G. Pickering, R. Russell, M. Woo, and T. Young, "Sleep apnea and cardiovascular disease: an American Heart Association/American College of Cardiology Foundation Scientific Statement," Circulation, vol. 118, no. 10, pp. 1080-1111, 2008, doi: 10.1161/CIRCULATIONAHA.107.189375.
• [4] P. Lévy, M. Kohler, W. T. McNicholas, F. Barbé, R. D. McEvoy, V. K. Somers, L. Lavie, and J. L. Pépin, "Obstructive sleep apnoea syndrome," Nature Reviews Disease Primers, vol. 1, no. 1, pp. 1-21, 2015, doi: 10.1038/nrdp.2015.15.
• [5] Senaratna, Chamara V., et al. "Prevalence of obstructive sleep apnea in the general population: a systematic review." Sleep medicine reviews 34 (2017): 70-81.K. A. Franklin and E. Lindberg, "Obstructive sleep apnea is a common disorder in the population—a review on the epidemiology of sleep apnea," Journal of Thoracic Disease, vol. 7, no. 8, pp. 1311-1322, 2015, doi: 10.3978/j.issn.2072-1439.2015.06.11.
• [6] V. K. Kapur, D. H. Auckley, S. Chowdhuri, D. C. Kuhlmann, R. Mehra, K. Ramar, and C. G. Harrod, "Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: an American Academy of Sleep Medicine clinical practice guideline," Journal of Clinical Sleep Medicine, vol. 13, no. 3, pp. 479-504, 2017, doi: 10.5664/jcsm.6506.
• [7] N. A. Collop, W. M. Anderson, B. Boehlecke, D. Claman, R. Goldberg, D. J. Gottlieb, D. Hudgel, M. Sateia, and R. Schwab, "Clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients," Journal of Clinical Sleep Medicine, vol. 3, no. 7, pp. 737-747, 2007, doi: 10.5664/jcsm.27032.
• [8] C. A. Kushida, M. R. Littner, T. Morgenthaler, C. A. Alessi, D. Bailey, J. Coleman Jr, L. Friedman, M. Hirshkowitz, S. Kapen, M. Kramer, T. Lee-Chiong, D. L. Loube, J. Owens, J. P. Pancer, and M. Wise, "Practice parameters for the indications for polysomnography and related procedures: an update for 2005," Sleep, vol. 28, no. 4, pp. 499-521, 2005, doi: 10.1093/sleep/28.4.499.
• [9] R. B. Berry, R. Budhiraja, D. J. Gottlieb, D. Gozal, C. Iber, V. K. Kapur, C. L. Marcus, R. Mehra, S. Parthasarathy, S. F. Quan, S. Redline, K. P. Strohl, S. L. Davidson Ward, and M. M. Tangredi, "Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events," Journal of Clinical Sleep Medicine, vol. 8, no. 5, pp. 597-619, 2012, doi: 10.5664/jcsm.2172.
• [10] N. J. Douglas, S. Thomas, and M. A. Jan, "Clinical value of polysomnography," The Lancet, vol. 339, no. 8789, pp. 347-350, 1992, doi: 10.1016/0140-6736(92)91660-Z.
• [11] L. Almazaydeh, K. Elleithy, and M. Faezipour, "Obstructive sleep apnea detection using SVM-based classification of ECG signal features," Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4938-4941, 2012, doi: 10.1109/EMBC.2012.6347100.
• [12] A. H. Khandoker, J. Gubbi, and M. Palaniswami, "Automated scoring of obstructive sleep apnea and hypopnea events using short-term electrocardiogram recordings," IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 6, pp. 1057-1067, 2009, doi: 10.1109/TITB.2009.2031639.
• [13] T. Penzel, J. W. Kantelhardt, L. Grote, J. H. Peter, and A. Bunde, "Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea," IEEE Transactions on Biomedical Engineering, vol. 50, no. 10, pp. 1143-1151, 2003, doi: 10.1109/TBME.2003.817636.
• [14] P. Welch, "The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms," IEEE Transactions on Audio and Electroacoustics, vol. 15, no. 2, pp. 70-73, 1967, doi: 10.1109/TAU.1967.1161901.
• [15] G. D. Clifford, "Signal processing methods for heart rate variability," Ph.D. dissertation, Department of Engineering Science, University of Oxford, Oxford, UK, 2002.
• [16] W. W. Flemons, N. J. Douglas, S. T. Kuna, D. O. Rodenstein, and J. Wheatley, "Access to diagnosis and treatment of patients with suspected sleep apnea," American Journal of Respiratory and Critical Care Medicine, vol. 169, no. 6, pp. 668-672, 2004, doi: 10.1164/rccm.200308-1124PP.
• [17] S. Setiawan and Y. Lin, "Sleep apnea detection from ECG signal: a comprehensive review and future direction," IEEE Access, vol. 9, pp. 47239-47261, 2021, doi: 10.1109/ACCESS.2021.3068053.
• [18] T. Rahman, Z. Yin, M. A. Hasan, Y. K. Vo, and X. Yuan, "Obstructive sleep apnea diagnosis using spectro-temporal analysis and convolutional neural networks from single-lead ECG," IEEE Transactions on Biomedical Engineering, vol. 66, no. 12, pp. 3322-3333, 2019, doi: 10.1109/TBME.2019.2902650.
• [19] T. Penzel, J. W. Kantelhardt, C. Lo, K. Voigt, and C. Vogelmeier, "Dynamics of heart rate and sleep stages in normals and patients with sleep apnea," Neuropsychopharmacology, vol. 28, no. 1, pp. 48-53, 2000, doi: 10.1016/S0893-133X(03)00116-8.
• [20] F. Roche, J. M. Gaspoz, I. Court-Fortune, P. Minini, V. Pichot, D. Duverney, F. Costes, J. R. Lacour, and J. C. Barthélémy, "Screening of obstructive sleep apnea syndrome by heart rate variability analysis," Circulation, vol. 100, no. 13, pp. 1411-1415, 1999, doi: 10.1161/01.cir.100.13.1411.
• [21] B. Bahrami and M. H. Forouzanfar, "Detection of obstructive sleep apnea from single-lead ECG signals using deep learning approach," Biomedical Signal Processing and Control, vol. 71, p. 103119, 2022, doi: 10.1016/j.bspc.2021.103119.
• [22] X. Chen, R. Wang, H. Chen, and Y. Chen, "A spatiotemporal deep learning approach for automatic detection of sleep apnea from single-lead ECG signals," IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 10, pp. 4968-4979, 2022, doi: 10.1109/JBHI.2022.3178882.
• [23] A. Martín-Montero, N. Paniagua-Sánchez, P. Casaseca-de-la-Higuera, M. Martínez-Zarzuela, and F. J. Díaz-Pernas, "Single-channel ECG classification for obstructive sleep apnea detection based on feature engineering and deep learning," Expert Systems with Applications, vol. 162, p. 113672, 2020, doi: 10.1016/j.eswa.2020.113672.
• [24] C. Mermigkis, I. Bouloukaki, and N. M. Siafakas, "Heart rate variability in chronic obstructive pulmonary disease associated with sleep apnea syndrome," COPD: Journal of Chronic Obstructive Pulmonary Disease, vol. 6, no. 6, pp. 456-461, 2009, doi: 10.3109/15412550903341828.
• [25] H. Qin, H. W. Kang, Y. Li, Z. Lei, J. Xu, and G. Liu, "Detection of obstructive sleep apnea based on heart rate variability analysis with CNN-LSTM model," IEEE Access, vol. 9, pp. 25939-25950, 2021, doi: 10.1109/ACCESS.2021.058015.
• [26] G. Bazoukis, S. Stavrakis, A. Yeung, H. Liu, K. Masetti, L. Tse, and T. Liu, "Diagnostic and prognostic value of artificial intelligence in electrocardiogram interpretation: a comprehensive review," Cardiovascular Research, vol. 119, no. 5, pp. 1203-1217, 2023, doi: 10.1093/cvr/cvac141.
• [27] A. Y. Hannun, P. Rajpurkar, M. Haghpanahi, G. H. Tison, C. Bourn, M. P. Turakhia, and A. Y. Ng, "Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network," Nature Medicine, vol. 25, no. 1, pp. 65-69, 2019, doi: 10.1038/s41591-018-0268-3.
• [28] R. Paul, V. K. Bajaj, and N. Kumar, "ECG arrhythmia classification using CNN with focal loss and class-weighted cross-entropy," Biomedical Signal Processing and Control, vol. 79, p. 104221, 2024, doi: 10.1016/j.bspc.2023.104221.
• [29] A. A. Abd-alrazaq, A. Alhuwail, M. Househ, and M. M. Hamdi, "Artificial intelligence in the diagnosis and management of sleep disorders: a systematic review and meta-analysis," Journal of Medical Internet Research, vol. 26, no. 1, p. e47596, 2024, doi: 10.2196/47596.
• [30] S. Aiyer, Y. Guo, H. Chen, Y. Wang, P. Xu, X. Wan, and G. Liu, "Automated detection of obstructive sleep apnea using wearable technologies: a systematic review," Sleep Medicine Reviews, vol. 62, p. 101593, 2022, doi: 10.1016/j.smrv.2022.101593.