EEG and ECG Microstructure Markers During Apnea Transition Phases in Central and Obstructive Sleep Apnea

Abstract

This study aimed to characterize EEG- and ECG-derived microstructural markers during apnea transitions in central sleep apnea (CSA) and obstructive sleep apnea (OSA), and to examine how these transient dynamics vary across sleep stages and apnea severity. Polysomnography data from 32 male patients (16 CSA, 16 OSA) were retrospectively analyzed. EEG sub-band energies (delta, theta, alpha, beta), pulse transit time (PTT), Hjorth parameters, and frequency-domain HRV (LF/HF) were computed for pre-apnea, intra-apnea, and post-apnea epochs. Patients were additionally stratified by AHI. Group comparisons were performed using parametric and non-parametric tests with Bonferroni adjustment. OSA and CSA demonstrated distinct electrophysiological patterns. CSA showed increasing EEG sub-band power and decreasing PTT from pre- to intra-apnea, indicating rapid autonomic adjustment with minimal sleep architecture disruption. OSA exhibited reduced EEG power, lengthened PTT, and greater beta activation during apnea, consistent with arousal-like intrusions. Stage-specific analysis revealed that REM sleep displayed the most pronounced transitions, marked by delta–theta enhancement and alpha–beta suppression. In N2 and N3, delta activity showed characteristic divergence across apnea intervals, highlighting unique microstructural adaptations within each stage. High-resolution EEG and ECG features provide complementary insights into the physiological mechanisms distinguishing OSA from CSA. Event-level EEG microstructures captured transient cortical dynamics not visible in conventional scoring, suggesting their potential value as early biomarkers of respiratory instability. These findings may support improved diagnostic accuracy and individualized management strategies in sleep apnea.

Keywords

• Sleep apnea
• EEG
• ECG
• microstructure
• HRV
• pulse transit time
• apnea transitions
• spectral analysis

Merkezi ve Obstrüktif Uyku Apnesinde Apne Geçiş Evrelerinde EEG ve EKG Mikroyapı Belirteçleri

Abstract

Bu çalışma, santral uyku apnesi (CSA) ve obstrüktif uyku apnesi (OSA) sırasında meydana gelen apne geçişlerinde EEG ve EKG’den türetilen mikroyapı belirteçleri tanımlamayı ve bu geçici dinamiklerin uyku evreleri ile apne şiddetine göre nasıl değiştiğini incelemeyi amaçlamıştır. Toplam 32 erkek hastaya ait tam gece polisomnografi kayıtları (16 CSA, 16 OSA) geriye dönük olarak analiz edilmiştir. Pre-apne, intra-apne ve post-apne epokları için EEG alt bant enerjileri (delta, teta, alfa, beta), nabız geçiş zamanı (PTT), Hjorth parametreleri ve frekans alanı HRV (LF/HF) değerleri hesaplanmıştır. Hastalar ayrıca AHI gruplarına göre sınıflandırılmıştır. Grup karşılaştırmaları parametrik ve parametrik olmayan testlerle, Bonferroni düzeltmesi kullanılarak yapılmıştır. OSA ve CSA arasında belirgin elektrofizyolojik farklılıklar gözlenmiştir. CSA’da pre-apneden intra-apneye geçişte EEG alt bant güçleri artarken, PTT azalmış; bu durum minimal uyku mimarisi bozulmasıyla birlikte hızlı otonom yanıtları göstermiştir. OSA’da ise EEG gücü azalmış, PTT uzamış ve apne sırasında beta aktivitesi artmış olup, bu durum uyanıklık benzeri geçişlerle uyumludur. Evreye özgü analizler, özellikle REM uykusunda delta–teta artışı ve alfa–beta baskılanmasıyla belirgin geçişler olduğunu ortaya koymuştur. N2 ve N3 evrelerinde delta aktivitesinin apne dönemlerine göre karakteristik değişimleri, evreye özgü mikroyapı adaptasyonları göstermiştir. Yüksek çözünürlüklü EEG ve EKG özellikleri, CSA ve OSA’yı ayırt eden fizyolojik mekanizmalar hakkında tamamlayıcı bilgiler sunmaktadır. Olay düzeyindeki EEG mikroyapıları geleneksel skorlamada görünmeyen geçici kortikal dinamikleri yakalayarak solunumsal instabilitenin erken belirteçleri olabilir. Bu bulgular, uyku apnesinin tanısal doğruluğunu artırmaya ve bireyselleştirilmiş tedavi stratejilerini desteklemeye katkı sağlayabilir.

Keywords

• Uyku apnesi
• EEG
• EKG
• mikroyapı KHD (kalp hızı değişkenliği)
• nabız geçiş zamanı
• apne geçişleri
• spektral analiz

References

1. [1] Redline S. Sleep and cardiovascular health: An introduction to the series. Circulation Research. 2025; 137: 705–708.
2. [2] Vitale GJ, Capp K, Ethridge K, Lorenzetti MS, Jeffrey M. Sleep apnea and the brain: Neurocognitive and emotional considerations. Journal of Sleep Disorders and Management. 2016; 2: 8–12.
3. [3] Kim DH, Kim B, Han K, Kim SW. The relationship between metabolic syndrome and obstructive sleep apnea syndrome: A nationwide population-based study. Scientific Reports. 2021; 11: 8751.
4. [4] Alterki A, Abu-Farha M, Al Shawaf E, Al-Mulla F, Abubaker J. Investigating the relationship between obstructive sleep apnoea, inflammation and cardio-metabolic diseases. International Journal of Molecular Sciences. 2023; 24: 6807.
5. [5] Javaheri S, Barbe F, Campos-Rodriguez F, Dempsey JA, Khayat R, Javaheri S, et al. Sleep apnea: Types, mechanisms, and clinical cardiovascular consequences. Journal of the American College of Cardiology. 2017; 69: 841–858.
6. [6] Guilleminault C, Tilkian A, Dement WC. The sleep apnea syndromes. Annual Review of Medicine. 1976; 27: 465–484.
7. [7] Fathima S, Ahmed M. Sleep apnea detection using EEG: A systematic review of datasets, methods, challenges, and future directions. Annals of Biomedical Engineering. 2025; 53: 1043–1067.
8. [8] Khandoker AH, Karmakar CK, Palaniswami M. Comparison of pulse rate variability with heart rate variability during obstructive sleep apnea. Medical Engineering & Physics. 2011; 33: 204–209.

9. [9] Osa-Sanchez A, Ramos-Martinez-de-Soria J, Mendez-Zorrilla A, Ruiz IO, Garcia-Zapirain B. Wearable sensors and artificial intelligence for sleep apnea detection: A systematic review. Journal of Medical Systems. 2025; 49: 66.
10. [10] Malhotra RK. AASM scoring manual 3: A step forward for advancing sleep care. Journal of Clinical Sleep Medicine. 2024; 20: 835–836.
11. [11] Lin WC, Hsu TW, Hsu CH, Lu CH, Chen HL. Alterations in sympathetic and parasympathetic brain networks in obstructive sleep apnea. Sleep Medicine. 2020; 73: 135–142.
12. [12] Bhongade A, Gandhi TK. Automatic identification of obstructive sleep apnea using multimodal features. Biomedical Signal Processing and Control. 2025; 105: 107609.
13. [13] Janbakhshi P, Shamsollahi MB. Sleep apnea detection from single-lead ECG using EDR-based features. IRBM. 2018; 39: 206–218.
14. [14] Yu X, Guan L, Su P, Zhang Q, Guo X, Li T, et al. OSA screening in elderly hypertensive patients using single-lead wearable ECG. Sleep and Breathing. 2024; 28: 2445–2456.
15. [15] Wiemers MC, Laufs H, von Wegner F. Frequency analysis of EEG microstate sequences in wakefulness and NREM sleep. Brain Topography. 2024; 37: 312–328.
16. [16] Dingli K, Assimakopoulos T, Fietze I, Witt C, Wraith PK, Douglas NJ. Electroencephalographic spectral analysis in sleep apnea patients. European Respiratory Journal. 2002; 20: 1246–1253.
17. [17] Gutiérrez-Tobal GC, Gomez-Pilar J, Kheirandish-Gozal L, Martín-Montero A, Poza J, Álvarez D, et al. Pediatric sleep apnea: Overnight EEG as a phenotypic biomarker. Frontiers in Neuroscience. 2021; 15: 644-697.
18. [18] Attar ET. Detailed evaluation of sleep apnea using HRV with ML/statistical methods. Frontiers in Neurology. 2025; 16: 1636983.
19. [19] Han H, Seong MJ, Hyeon J, Joo E, Oh J. Classification and automatic scoring of arousal intensity using ML. Scientific Reports. 2024; 14: 5983.
20. [20] Malinowska U, Durka PJ, Blinowska KJ, Szelenberger W, Wakarow A. Micro- and macrostructure of sleep EEG. IEEE Engineering in Medicine and Biology Magazine. 2006; 25: 26–31.
21. [21] Agarwal R. Automatic detection of micro-arousals. Proc. IEEE EMBC. 2006; 1158–1161.
22. [22] Ferini-Strambi L, Bianchi A, Zucconi M, Oldani A, Castronovo V, Smirne S. CAP impact on HRV in young healthy adults. Clinical Neurophysiology. 2000; 111: 99–101.
23. [23] Chouvarda I, Mendez MO, Rosso V, Bianchi AM, Parrino L, Grassi A, et al. Predicting EEG complexity from sleep structure. Physiological Measurement. 2011; 32: 1083.
24. [24] Mullins AE, Jong W, Kim J, Keith W, Wong KH, Bartlett DJ, et al. Sleep EEG microstructure and neurobehavioral impairment in OSA. Sleep and Breathing. 2021; 25: 347–354.
25. [25] D’Rozario AL, Cross NE, Vakulin A. Quantitative EEG in adult OSA. Sleep Medicine Reviews. 2017; 36: 29–42.
26. [26] Appleton SL, Vakulin A, D’Rozario A. Quantitative EEG in REM/NREM associated with AHI. Sleep. 2019; 42: 6.
27. [27] Guzik P, Piskorski J, Awan K. Heart rate asymmetry microstructure in OSA. Clinical Autonomic Research. 2013; 23: 91–100.
28. [28] Li N, Wang J, Wang D, Wang Q, Han F, Jyothi K, et al. Sleep microstructure correlation with cognitive function in OSA. European Archives of Oto-Rhino-Laryngology. 2019; 276: 3525–3532.
29. [29] Bahr-Hamm K, Koirala N, Hanif M, Gouveris H, Muthuraman M. Sensorimotor cortical activity during arousals in OSA. International Journal of Molecular Sciences. 2023; 24: 47.
30. [30] Sharma M, Yadav A, Tiwari J, Karabatak M, Yildirim O, Acharya UR. Wavelet-based sleep scoring model using EEG/EMG/EOG. International Journal of Environmental Research and Public Health. 2022; 19: 7176.
31. [31] Šušmáková K. Human sleep and EEG. Measurement Science Review. 2004; 4: 59–74.
32. [32] Khandoker AH, Gubbi J, Palaniswami M. Automated scoring of OSA events using ECG. IEEE Transactions on Information Technology in Biomedicine. 2009; 13: 1057–1067.
33. [33] Berry RB, Brooks R, Gamaldo CE, Harding SM, Marcus C, Vaughn BV. AASM Manual for Scoring of Sleep and Associated Events. American Academy of Sleep Medicine. 2012.
34. [34] Chen SW, Chen HC, Chan HL. Real-time QRS detection with moving average and wavelet. Computer Methods and Programs in Biomedicine. 2006; 82: 187–195.
35. [35] Kadbi MH, Hashemi J, Mohseni HR, Maghsoudi A. Classification of ECG arrhythmias. Proc. MEDSIP. 2006; 398–400.
36. [36] Pagani J. Detection of OSA in children using pulse transit time. Computers in Cardiology. 2002; 29: 529–532.
37. [37] Pitson DJ, Sandell A, Van Den Hout R, Stradling JR. Pulse transit time as inspiratory effort index in OSA. European Respiratory Journal. 1995; 8: 1669–1674.
38. [38] Rangayyan RM. Biomedical Signal Analysis. John Wiley & Sons. 2015.
39. [39] Hamila R, Astola J, Alaya CF, Gabbouj M, Renfors M. Teager energy and ambiguity function. IEEE Transactions on Signal Processing. 1999; 47: 260–262.
40. [40] Arzeno NM, Deng ZD, Poon CS. First-derivative QRS detection algorithms analysis. IEEE Transactions on Biomedical Engineering. 2008; 55: 478–484.
41. [41] Seena V, Yomas J. Feature extraction and denoising of ECG using wavelet transform. Proc. IEEE Int. Caracas Conf. 2014; 1–6.
42. [42] Takalo R, Hytti H, Ihalainen H. Tutorial on univariate autoregressive spectral analysis. Journal of Clinical Monitoring and Computing. 2006; 20: 379.
43. [43] Hjorth B. EEG analysis based on time domain properties. Electroencephalography and Clinical Neurophysiology. 1970; 29: 306–310.
44. [44] Hjorth B. Physical significance of time domain EEG descriptors. Electroencephalography and Clinical Neurophysiology. 1973; 34: 321–325.
45. [45] Pal S, Mitra M. MI detection via supervised classification. Proc. ACT Int. Conf. 2009; 398–400.
46. [46] Binnie CD. Computer-assisted interpretation of EEGs. Electroencephalography and Clinical Neurophysiology. 1978; 45: 575–585.
47. [47] Subha DP, Joseph PK, Acharya R, Lim CM. EEG signal analysis: A survey. Journal of Medical Systems. 2010; 34: 195–212.
48. [48] Motamedi-Fakhr S, Moshrefi-Torbati M, Hill M, Hill CM, White PR. Signal processing techniques for sleep EEG. Biomedical Signal Processing and Control. 2014; 10: 21–33.
49. [49] Garg G, Behl S, Singh V. PSD estimation for seizure detection. Journal of Computers. 2011; 6: 160–163.
50. [50] Welch PD. FFT for power spectra estimation. Digital Signal Processing. 1975; 1: 532–574.
51. [51] Alkan A, Yilmaz AS. Frequency domain analysis using Welch and Yule–Walker AR methods. Energy Conversion and Management. 2007; 48: 2129–2135.
52. [52] Collomb C. Burg’s method: Algorithm and recursion. Computer Science Technical Report. 2009.
53. [53] Dijk DJ, Brunner DP, Beersma DGM, Borbély AA. EEG power density and slow-wave sleep vs circadian phase. Sleep. 1990; 13: 430–440.
54. [54] Kocak O, Ficici C, Firat H, Telatar Z. Structural EEG signal analysis for sleep apnea classification. Biomedical Engineering/Biomedizinische Technik. 2024; 69: 419-430.