Poster No:
2591
Submission Type:
Abstract Submission
Authors:
Younghan Lee1,2, Youngseo Kim3, Hyeon Jin Kim4,5,6, Ho Bae7, Yunheung Paek1,2, Hyang Woon Lee3,5,6,8
Institutions:
1Department of Electrical and Computer Engineering (ECE),Seoul National University, Seoul, Korea, Republic of, 2Inter-University Semiconductor Research Center (ISRC), Seoul National University, Seoul, Korea, Republic of, 3Graduate Program of Artificial Intelligence Convergence, Ewha Womans University, Seoul, Korea, Republic of, 4Department of Neurology, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, 5Departments of Neurology Ewha Womans University School of Medicine and Ewha Medical Research, Seoul, Korea, Republic of, 6Departments of Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research, Seoul, Korea, Republic of, 7Department of Cyber Security, Ewha Womans University, Seoul, Korea, Republic of, 8Computational Medicine, System Health Science & Engineering, Graduate School of Ewha Womans University, Seoul, Korea, Republic of
First Author:
Younghan Lee, BEng
Department of Electrical and Computer Engineering (ECE),Seoul National University|Inter-University Semiconductor Research Center (ISRC), Seoul National University
Seoul, Korea, Republic of|Seoul, Korea, Republic of
Co-Author(s):
Youngseo Kim, MSc
Graduate Program of Artificial Intelligence Convergence, Ewha Womans University
Seoul, Korea, Republic of
Hyeon Jin Kim, MD
Department of Neurology, Korea University Ansan Hospital|Departments of Neurology Ewha Womans University School of Medicine and Ewha Medical Research|Departments of Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research
Ansan-si, Gyeonggi-do|Seoul, Korea, Republic of|Seoul, Korea, Republic of
Ho Bae, PhD
Department of Cyber Security, Ewha Womans University
Seoul, Korea, Republic of
Yunheung Paek, PhD
Department of Electrical and Computer Engineering (ECE),Seoul National University|Inter-University Semiconductor Research Center (ISRC), Seoul National University
Seoul, Korea, Republic of|Seoul, Korea, Republic of
Hyang Woon Lee, MD, PhD
Graduate Program of Artificial Intelligence Convergence, Ewha Womans University|Departments of Neurology Ewha Womans University School of Medicine and Ewha Medical Research|Departments of Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research|Computational Medicine, System Health Science & Engineering, Graduate School of Ewha Womans University
Seoul, Korea, Republic of|Seoul, Korea, Republic of|Seoul, Korea, Republic of|Seoul, Korea, Republic of
Introduction:
Among the myriad sleep disorders, Severe Obstructive Sleep Apnea (OSA) stands out as a prevalent and potentially debilitating condition, characterized by recurrent upper airway obstruction leading to disrupted breathing patterns during sleep. While the physiological impact of OSA on sleep architecture is well-established, the intricate neural dynamics underlying these disruptions remain an area of active investigation. In this study, we focus on unraveling the distinctions in neural activity during sleep between men and women with severe OSA in comparison to those with mild to moderate OSA and healthy controls without OSA. Through advanced methodologies using high-resolution neurodynamic analyses, we aimed to elucidate the differences of quantitative sleep characteristics in severe OSA patients in comparison to those in mild to moderate OSA and in control subjects without OSA. Additionally, we thrive to compare these sleep characteristics between male and female patients.
Methods:
A total of 852 anonymous polysomnography (PSG) datasets which were collected for an unrelated research initiative, having received approval from the Institutional Review Board (IRB) at Ewha Womans University Mokdong Hospital. Within this dataset, 284 individuals were identified with severe OSA, comprising 44 females and 240 males, with an average age of 52.54 years and 284 individuals with mild to moderate OSA with the same ratio of sex and the average age of 52.23 years. To ensure methodological robustness, we matched the sex and age parameters of the healthy control dataset without OSA preserving an average age of 51.78 years, with an equal distribution of 44 females and 240 males. The EEG data which is sampled at a rate of 200 Hz is used for spectral estimation through multitaper approach. We only focused on the C3 electrode and EEg spectrograms were drawn for each subject. Firstly, we extracted slow-oscillation (SO) power in a range of 0.5-2.0Hz. Subsequently carried out a calculation to generate histograms that indicates the average SO-power within the rage of 0.5-30 Hz. To facilitate mathematical analysis and identify characteristics of sleep neural dynamics in OSA, it is essential to standardize the vector of SO-power divided equally into 41 segments (0-39, respectively) across subjects. We employed the mean squared error (MSE) metric which aids in the precise evaluation of distinctions, contributing to a comprehensive understanding of the unique features of sleep neural dynamics in individuals with OSA.
Results:
We computed the average of SO-powers in each segment within three groups: those with severe OSA, those with mild to moderate OSA, and those without OSA. We utilized the mean squared error (MSE) for each SO-power segment to identify the differences between these three groups. Smaller MSE values indicate greater similarity in the characteristics of sleep neural dynamics. Our results revealed notable variations across segments between groups. In particular, the differences were significant in SO-powers with lower (segments 1-6) and higher (segments 43-40) ranges between the severe OSA and healthy control groups.
Conclusions:
Through the generation of sleep EEG spectrograms and computation of histograms for SO-power, we unveiled notable differences between OSA patients and controls, especially between severe OSA and control groups, particularly evident in lower and higher ranges. The potential clinical implications of our proposed method, combined with high-resolution neural dynamic analyses, include investigation of sleep quality in OSA patients with different disease severity as well as regular monitoring for the sleep improvement before and after therapeutic intervention.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 1
Keywords:
Electroencephaolography (EEG)
Sleep
Other - Obstructive Sleep Apnea (OSA), polysomnography (PSG), High-Resolution Neural Dynamics, Spectral analysis
1|2Indicates the priority used for review
Provide references using author date format
Malhotra, Atul, et al. "Metrics of sleep apnea severity: beyond the apnea-hypopnea index." Sleep 44.7 (2021): zsab030.
Prerau, Michael J., et al. "Sleep neurophysiological dynamics through the lens of multitaper spectral analysis." Physiology 32.1 (2017): 60-92.
H. J. Kim, S. Chen, U. T. Eden and M. J. Prerau, "A quantitative representation of continuous brain state during sleep," 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), Italy, 2021, pp. 103-106, doi: 10.1109/NER49283.2021.9441276.
Han, H., Oh, J. Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity. Sci Rep 13, 6379 (2023). https://doi.org/10.1038/s41598-023-33170-7
Acknowledgments
Supported by the National Research Foundation of Korea (NRF) (No. 2019M3C1B8090803, 2020R1A2C2013216, and RS-2023-00265524), Institute of Information & Communication Technology Planning & Evaluation (IITP) grant (No. RS-2022-00155966) by the Korea government (MSIT), and BK21-plus FOUR and Artificial Intelligence Convergence Innovation Human Resources Development programs of Ewha Womans University. to H.W.Lee. This study was supported by grants from the Korea University Ansan Hospital [No. K2316061 to H.J.Kim], the Korea Health Technology R & D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea [HI19C1065 to H.J.Kim]. This work was supported by the BK21 FOUR program of the Education and Research Program for Future ICT Pioneers, Seoul National University in 2023. Also, it was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (RS-2023-00277326).