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Year 2025, Early View, 1 - 1

İlhan Umut Hakan Üstünel Güven Çentik Erdem Uçar Levent Öztürk

https://doi.org/10.35378/gujs.1229166

Abstract

Keywords

Sleep apnea Digital signal processing Electroencephalography Sleep apnea Digital signal processing Digital signal processing Electroencephalography

References

  • [1] Gabran, S.R.I., Zhang, S., Salama M.M.A., and Mansour R.R., "Real-time automated neural-network sleep classifier using single channel EEG recording for detection of narcolepsy episodes", 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, (2008). DOI: https://doi.org/10.1109/IEMBS.2008.4649361
  • [2] Karakulak, E., "ARM MCU-Based Experimental EEG Signal Generator Using Internal DAC and PWM Outputs", Gazi University Journal of Science, 35(3): 886-894, (2022). DOI: https://doi.org/10.35378/gujs.860994
  • [3] Darroudi, A., Parchami, J., Razavi, M.K., and Sarbisheie, G., "EEG adaptive noise cancellation using information theoretic approach”, Bio-medical Materials and Engineering, 28(4): 325-338, (2017). DOI: https://doi.org/10.3233/BME-171680
  • [4] Malinowska, U., Durka, P.J., Blinowska, K., and Szelenberger, W., "Micro-and macrostructure of sleep EEG", IEEE Engineering in Medicine and Biology Magazine, 25(4): 26-31, (2006). DOI: https://doi.org/10.1109/MEMB.2006.1657784
  • [5] Rechtschaffen, A., Kales, A., “Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages in Human Subjects”, Public Health Service, U.S. Government Printing Office, Washington, DC, (1968).
  • [6] Ross, S.D., Allen, I.E., Harrison, K.J., Kvasz, M., Connelly, J., and Sheinhait, I.A., "Systematic review of the literature regarding the diagnosis of sleep apnea", Evidence Report/Technology Assessment (Summary), 1: 1-4, (1998). DOI: https://doi.org/10.1093/sleep/23.4.1f
  • [7] Young, T., Palta, M., Dempsey, J., Skatrud, J., Weber, S., and Badr, S., "The occurrence of sleep-disordered breathing among middle-aged adults", New England Journal of Medicine, 328(17): 1230-1235, (1993). DOI: https://doi.org/10.1056/NEJM199304293281704
  • [8] Kirby, S.D., Danter, W., George, C., Francovic, T., and Ferguson, K.A., "Neural network prediction of obstructive sleep apnea from clinical criteria", Chest, 116(2): 409-415, (1999). DOI: https://doi.org/10.1378/chest.116.2.409
  • [9] Guilleminault, C., Ara, T., and Dement, W.C., "The sleep apnea syndromes", Annual Review of Medicine, 27(1): 465-484, (1976). DOI: https://doi.org/10.1146/annurev.me.27.020176.002341
  • [10] Herer, B., Fuhrman, C., Roing, C., and Housset, B., "Prediction of obstructive sleep apnea by OxiFlow in overweight patients", Sleep Medicine, 3(5): 417-422, (2002). DOI: https://doi.org/10.1016/S1389-9457(02)00040-0
  • [11] Kiely, J.L., Delahunty, C., Matthews, S., and McNicholas, W.T., "Comparison of a limited computerized diagnostic system (ResCare Autoset) with polysomnography in the diagnosis of obstructive sleep apnoea syndrome", European Respiratory Journal, 9(11): 2360-2364, (1996). DOI: https://doi.org/10.1183/09031936.96.09112360
  • [12] Wang, Y., Xiao, Z., Fang, S., Li, W., Wang, J., and Zhao, X., "BI-Directional long short-term memory for automatic detection of sleep apnea events based on single channel EEG signal", Computers in Biology and Medicine, 142: 105211, (2022). DOI: https://doi.org/10.1016/j.compbiomed.2022.105211
  • [13] Lee, J.M., Kim, D.J., Kim, I.Y., Park, K.S., and Kim, S.I., "Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data", Computers in biology and medicine, 32(1): 37-47, (2002). DOI: https://doi.org/10.1016/S0010-4825(01)00031-2
  • [14] Herbert, J., "Report of the committee on methods of clinical examination in electroencephalography" Electroencephalography and Clinical Neurophysiology, 10: 370-375, (1958). DOI: https://doi.org/10.1016/0013-4694(58)90053-1
  • [15] Kemp, B., Jesus, O., "European data format ‘plus’(EDF+), an EDF alike standard format for the exchange of physiological data" Clinical neurophysiology, 114(9): 1755-1761, (2003). DOI: https://doi.org/10.1016/S1388-2457(03)00123-8
  • [16] Quan, S.F., Gillin, J.C., Littner, M.R., and Shepard J.W., "Sleep-related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research. editorials", Sleep (New York, NY), 22(5): 662-689, (1999).
  • [17] Erl, T., “Service-Oriented Architecture a Field Guide to İntegrating XML and Web Services”, Prentice Hall, USA, 2-4: 18-44, (2004).
  • [18] Dale, N.B., and John L., “Computer science illuminated”, Jones & Bartlett Learning, (2007).
  • [19] Ucar, E., Süt, N., Gülyaşar, T., Umut, I., and Öztürk, L., "Can obstructive apnea and hypopnea during sleep be differentiated by using electroencephalographic frequency bands? Statistical analysis of receiver-operator curve characteristics", Turkish Journal of Medical Sciences, 41(4): 571-580, (2011). DOI: https://doi.org/10.3906/sag-1007-967
  • [20] Torrence, C., and Gilbert P.C., "A practical guide to wavelet analysis", Bulletin of the American Meteorological Society, 79(1): 61-78, (1998). DOI: https://doi.org/10.1175/1520-0477(1998)079<0061: APGTWA>2.0.CO;2
  • [21] Smith, S.W., “The Scientist and Engineer's Guide to Digital Signal Processing”, second ed., California Technical Publishing, San Diego, California, (1996).
  • [22] Fliege, N.J., “Multirate Digital Signal Processing (Multirate Systems-Filter Banks-Wavelets)”, John Wiley & Sons, Chichester, (1996).
  • [23] Fawcett, T., “An Introduction to ROC analysis”, Pattern Recognation Letterrs, 27 (8): 861-874, (2005).
  • [24] Box, G.E.P., Hunter, J.S., and Hunter, W.G., “Statistics for Experimenters”, John Wiley & Sons, Inc., New Jersey, (2005).

Distinguishing Obstructive Sleep Apnea Using Electroencephalography Records

Year 2025, Early View, 1 - 1

İlhan Umut Hakan Üstünel Güven Çentik Erdem Uçar Levent Öztürk

https://doi.org/10.35378/gujs.1229166

Abstract

In this study, it was aimed to find out whether electroencephalographic (EEG) frequency bands can be used to distinguish people with obstructive sleep apnea (OSA) from those who do not have it. 11842 different cases taken from 121 patients suffering from OSA were combined with the case study of 30-person control group without sleep apnea. Apneas were highlighted at the respiration-record channels and EEG records which are concurrent with abnormal respiration cases were extracted from C4-A1 and C3-A2. Following that, they were examined with Fourier and Wavelet Transforms using a new software that was developed by us. The percentage values of Delta (0, 5-4 Hz), Theta (4-8 Hz), Alpha (8-13 Hz) and Beta (13-30 Hz) frequency bands were evaluated with the help of t-test and ROC Analysis to differentiate between apneas. The C3-A2 Beta (%) frequency level gave the highest distinguishing asset (AUC=0.662; p<0.001); however, the C3-A2 Alpha (%) level yielded the lowest distinguishing (AUC=0.536; p<0.001). Similarly, the C4-A1 Alpha (%) level produced the lowest distinguishing asset (AUC=0.536; p<0.001) whereas the C4-A1 Beta (%) frequency level gave the highest distinguishing asset (AUC=0.658; p<0.001). The chief finding of this study suggests that the EEG rates of patients with OSA differ from those of patients without OSA and following the changes at these channels may give rise to detection of apneas, and the Beta (%) yielded the most meaningful result among four different frequency bands in the study.

Keywords

Sleep apnea Digital signal processing Electroencephalography

References

  • [1] Gabran, S.R.I., Zhang, S., Salama M.M.A., and Mansour R.R., "Real-time automated neural-network sleep classifier using single channel EEG recording for detection of narcolepsy episodes", 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, (2008). DOI: https://doi.org/10.1109/IEMBS.2008.4649361
  • [2] Karakulak, E., "ARM MCU-Based Experimental EEG Signal Generator Using Internal DAC and PWM Outputs", Gazi University Journal of Science, 35(3): 886-894, (2022). DOI: https://doi.org/10.35378/gujs.860994
  • [3] Darroudi, A., Parchami, J., Razavi, M.K., and Sarbisheie, G., "EEG adaptive noise cancellation using information theoretic approach”, Bio-medical Materials and Engineering, 28(4): 325-338, (2017). DOI: https://doi.org/10.3233/BME-171680
  • [4] Malinowska, U., Durka, P.J., Blinowska, K., and Szelenberger, W., "Micro-and macrostructure of sleep EEG", IEEE Engineering in Medicine and Biology Magazine, 25(4): 26-31, (2006). DOI: https://doi.org/10.1109/MEMB.2006.1657784
  • [5] Rechtschaffen, A., Kales, A., “Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages in Human Subjects”, Public Health Service, U.S. Government Printing Office, Washington, DC, (1968).
  • [6] Ross, S.D., Allen, I.E., Harrison, K.J., Kvasz, M., Connelly, J., and Sheinhait, I.A., "Systematic review of the literature regarding the diagnosis of sleep apnea", Evidence Report/Technology Assessment (Summary), 1: 1-4, (1998). DOI: https://doi.org/10.1093/sleep/23.4.1f
  • [7] Young, T., Palta, M., Dempsey, J., Skatrud, J., Weber, S., and Badr, S., "The occurrence of sleep-disordered breathing among middle-aged adults", New England Journal of Medicine, 328(17): 1230-1235, (1993). DOI: https://doi.org/10.1056/NEJM199304293281704
  • [8] Kirby, S.D., Danter, W., George, C., Francovic, T., and Ferguson, K.A., "Neural network prediction of obstructive sleep apnea from clinical criteria", Chest, 116(2): 409-415, (1999). DOI: https://doi.org/10.1378/chest.116.2.409
  • [9] Guilleminault, C., Ara, T., and Dement, W.C., "The sleep apnea syndromes", Annual Review of Medicine, 27(1): 465-484, (1976). DOI: https://doi.org/10.1146/annurev.me.27.020176.002341
  • [10] Herer, B., Fuhrman, C., Roing, C., and Housset, B., "Prediction of obstructive sleep apnea by OxiFlow in overweight patients", Sleep Medicine, 3(5): 417-422, (2002). DOI: https://doi.org/10.1016/S1389-9457(02)00040-0
  • [11] Kiely, J.L., Delahunty, C., Matthews, S., and McNicholas, W.T., "Comparison of a limited computerized diagnostic system (ResCare Autoset) with polysomnography in the diagnosis of obstructive sleep apnoea syndrome", European Respiratory Journal, 9(11): 2360-2364, (1996). DOI: https://doi.org/10.1183/09031936.96.09112360
  • [12] Wang, Y., Xiao, Z., Fang, S., Li, W., Wang, J., and Zhao, X., "BI-Directional long short-term memory for automatic detection of sleep apnea events based on single channel EEG signal", Computers in Biology and Medicine, 142: 105211, (2022). DOI: https://doi.org/10.1016/j.compbiomed.2022.105211
  • [13] Lee, J.M., Kim, D.J., Kim, I.Y., Park, K.S., and Kim, S.I., "Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data", Computers in biology and medicine, 32(1): 37-47, (2002). DOI: https://doi.org/10.1016/S0010-4825(01)00031-2
  • [14] Herbert, J., "Report of the committee on methods of clinical examination in electroencephalography" Electroencephalography and Clinical Neurophysiology, 10: 370-375, (1958). DOI: https://doi.org/10.1016/0013-4694(58)90053-1
  • [15] Kemp, B., Jesus, O., "European data format ‘plus’(EDF+), an EDF alike standard format for the exchange of physiological data" Clinical neurophysiology, 114(9): 1755-1761, (2003). DOI: https://doi.org/10.1016/S1388-2457(03)00123-8
  • [16] Quan, S.F., Gillin, J.C., Littner, M.R., and Shepard J.W., "Sleep-related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research. editorials", Sleep (New York, NY), 22(5): 662-689, (1999).
  • [17] Erl, T., “Service-Oriented Architecture a Field Guide to İntegrating XML and Web Services”, Prentice Hall, USA, 2-4: 18-44, (2004).
  • [18] Dale, N.B., and John L., “Computer science illuminated”, Jones & Bartlett Learning, (2007).
  • [19] Ucar, E., Süt, N., Gülyaşar, T., Umut, I., and Öztürk, L., "Can obstructive apnea and hypopnea during sleep be differentiated by using electroencephalographic frequency bands? Statistical analysis of receiver-operator curve characteristics", Turkish Journal of Medical Sciences, 41(4): 571-580, (2011). DOI: https://doi.org/10.3906/sag-1007-967
  • [20] Torrence, C., and Gilbert P.C., "A practical guide to wavelet analysis", Bulletin of the American Meteorological Society, 79(1): 61-78, (1998). DOI: https://doi.org/10.1175/1520-0477(1998)079<0061: APGTWA>2.0.CO;2
  • [21] Smith, S.W., “The Scientist and Engineer's Guide to Digital Signal Processing”, second ed., California Technical Publishing, San Diego, California, (1996).
  • [22] Fliege, N.J., “Multirate Digital Signal Processing (Multirate Systems-Filter Banks-Wavelets)”, John Wiley & Sons, Chichester, (1996).
  • [23] Fawcett, T., “An Introduction to ROC analysis”, Pattern Recognation Letterrs, 27 (8): 861-874, (2005).
  • [24] Box, G.E.P., Hunter, J.S., and Hunter, W.G., “Statistics for Experimenters”, John Wiley & Sons, Inc., New Jersey, (2005).
There are 24 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

İlhan Umut 0000-0002-5269-1128

Hakan Üstünel 0000-0001-9903-593X

Güven Çentik 0000-0001-9034-1586

Erdem Uçar 0000-0002-0039-9619

Levent Öztürk 0000-0002-0182-3960

Early Pub Date August 14, 2023
Publication Date
Published in Issue Year 2025 Early View

Cite

APA Umut, İ., Üstünel, H., Çentik, G., Uçar, E., et al. (2023). Distinguishing Obstructive Sleep Apnea Using Electroencephalography Records. Gazi University Journal of Science1-1. https://doi.org/10.35378/gujs.1229166
AMA Umut İ, Üstünel H, Çentik G, Uçar E, Öztürk L. Distinguishing Obstructive Sleep Apnea Using Electroencephalography Records. Gazi University Journal of Science. Published online August 1, 2023:1-1. doi:10.35378/gujs.1229166
Chicago Umut, İlhan, Hakan Üstünel, Güven Çentik, Erdem Uçar, and Levent Öztürk. “Distinguishing Obstructive Sleep Apnea Using Electroencephalography Records”. Gazi University Journal of Science, August (August 2023), 1-1. https://doi.org/10.35378/gujs.1229166.
EndNote Umut İ, Üstünel H, Çentik G, Uçar E, Öztürk L (August 1, 2023) Distinguishing Obstructive Sleep Apnea Using Electroencephalography Records. Gazi University Journal of Science 1–1.
IEEE İ. Umut, H. Üstünel, G. Çentik, E. Uçar, and L. Öztürk, “Distinguishing Obstructive Sleep Apnea Using Electroencephalography Records”, Gazi University Journal of Science, pp. 1–1, August 2023, doi: 10.35378/gujs.1229166.
ISNAD Umut, İlhan et al. “Distinguishing Obstructive Sleep Apnea Using Electroencephalography Records”. Gazi University Journal of Science. August 2023. 1-1. https://doi.org/10.35378/gujs.1229166.
JAMA Umut İ, Üstünel H, Çentik G, Uçar E, Öztürk L. Distinguishing Obstructive Sleep Apnea Using Electroencephalography Records. Gazi University Journal of Science. 2023;:1–1.
MLA Umut, İlhan et al. “Distinguishing Obstructive Sleep Apnea Using Electroencephalography Records”. Gazi University Journal of Science, 2023, pp. 1-1, doi:10.35378/gujs.1229166.
Vancouver Umut İ, Üstünel H, Çentik G, Uçar E, Öztürk L. Distinguishing Obstructive Sleep Apnea Using Electroencephalography Records. Gazi University Journal of Science. 2023:1-.