Poster No:
2585
Submission Type:
Abstract Submission
Authors:
Younghan Lee1,2, Youngseo Kim3, Hyeon Jin Kim4,5,6, Ho Bae7, Yunheung Paek1,2, Hyang Woon Lee3,8,5,6
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|Computational Medicine, System Health Science & Engineering, Graduate School of 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
Seoul, Korea, Republic of|Seoul, Korea, Republic of|Seoul, Korea, Republic of|Seoul, Korea, Republic of
Introduction:
The diagnosis of REM sleep behavior disorder (RBD) relies upon the meticulous analysis of polysomnography (PSG), wherein recurrent instances of sleep-related vocalization and physically aggressive behaviors during dream enactment are observed. Confirmation of this diagnosis is achieved through the identification of REM sleep without atonia in the PSG recordings. Such complex cognitive processes occurring during sleep are intricately tied to the interactions of neural activities. Consequently, many studies have delved into the examination of specific frequency components within electroencephalography (EEG), exploring their correlation with diverse neuropsychological outcomes. In response to the current limitations in research methodology, predominantly characterized by a low-resolution framework, our endeavor is to represent personal brain activity during sleep independently of the temporal evolution of sleep architecture. In this research initiative, we aim to discern distinctive individualized neural dynamics between cohorts of patients with and without RBD.
Methods:
A total of 220 anonymous polysomnography (PSG) datasets from subjects were analyzed in the course of unrelated research, following approval from the Institutional Review Board (IRB) of Ewha Womans University Mokdong Hospital. Within this cohort, exactly 50% (110 subjects) were diagnosed with RBD, comprising 49 females and 61 males, with an average age of 62.22 years. To uphold methodological integrity, the sex and age parameters of the control dataset were matched, maintaining an average age of 62.24 years, with an equal distribution of 49 females and 61 males.
The EEG data, sampled at 200 Hz, underwent analysis focusing on the C3-A1 electrode derivation. Employing the multitaper approach for spectral estimation, sleep EEG spectrograms were computed for each subject. The relative slow-oscillation (SO, 0.5-2.0 Hz) power, serving as a quantitative surrogate indicator of sleep depth, was extracted. Subsequently, the average spectral power (0.5-30 Hz) corresponding to the SO-power was computed to generate histograms. To facilitate inter-subject or intra-subject comparison, standardized segments were established by fixing the size of SO-power ranges into 41 sections (segment 0~40, respectively).
Results:
The mean value of each segment within the two groups (those with and without RBD) exhibited a noticeable distinction. To quantify this difference, we employed the mean squared error (MSE), a metric that gauges the average of the squared differences between two vectors. The extent of difference varied across SO-power segments. Notably, segments with both lower (segment 1-6) and higher (segment 33-40) SO-power ranges exhibited more pronounced differences between the two groups. This suggests that, for discerning subjects with RBD based on neural dynamics, attention should be focused on specific regions. For instance, segment 16, representing the middle section of the histogram, demonstrated visually no difference between the two groups.
Conclusions:
In conclusion, our study explored the feasibility of differentiating RBD by analyzing neural dynamics during sleep. Our approach utilized EEG to examine SO-power, aiming to overcome methodological limitations. Analyzing 220 subjects, half diagnosed with RBD, we observed significant differences in SO-power segments particularly with both lower and higher ranges. MSE metric was employed to quantify these distinctions, emphasizing the importance of specific SO-power ranges in the differentiation process.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 1
Keywords:
Degenerative Disease
Electroencephaolography (EEG)
Sleep
Other - REM sleep behavior disorder (RBD), polysomnography (PSG), Computational Neuroscience, Spectral analysis
1|2Indicates the priority used for review
Provide references using author date format
American Academy of Sleep Medicine. International Classification of Sleep Disorders, 3rd ed.; American Academy of Sleep Medicine: Darien, IL, USA, 2014.
Choi, Gyeong Seon, et al. "Can corticomuscular coherence differentiate between rem sleep behavior disorder with or without parkinsonism?." Journal of Clinical Medicine 10.23 (2021): 5585.
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.
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).