https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/432 Sat, 04 Jul 2026 00:56:27 GMT 2026-07-04T00:56:27Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/217879 Title: EEG-based unsupervised learning uncovers an insomnia subtype with sleep-state misperception and associated brain and mental health risks Authors: Yook, Soonhyun; Choi, Youngseok; Park, Hea Ree; Park, Gilsoon; Kang, Donghun; Kim, Joo Young; Lee, Jongshill; Joo, Eun Yeon; Kim, In Young; Kim, Hosung Abstract: Insomnia with sleep-state misperception (SSM), defined by a mismatch between subjective complaints and objective polysomnography, lacks a clear neurophysiological explanation despite its substantial clinical burden. Using an unsupervised autoencoder approach, we extracted latent EEG microstructure features and identified two reproducible insomnia subtypes across multiple datasets: an objective sleep disruption (OSD) phenotype marked by macrostructural abnormalities and an SSM phenotype presenting with near-normal polysomnography. Individuals with SSM showed reduced delta activity and elevated alpha activity during early N3 sleep, indicating shallow deep sleep and alpha intrusion. These microstructural alterations were strongly associated with clinically significant outcomes, including accelerated brain aging, impairments in attention and visual memory, and elevated depressive symptoms. Conventional SSM classifications based solely on subjective–objective discrepancy did not observe these pathophysiological abnormalities or their clinical consequences. Because consumer wearables quantify only macrostructural sleep metrics, they overlook these clinically relevant EEG features. Integrating microstructure-based analysis into portable sleep technologies may allow earlier identification of high-risk insomnia phenotypes that remain undetectable with standard approaches. Mon, 01 Jun 2026 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/217879 2026-06-01T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/213316 Title: Noninvasive Detection of Acute Hyperglycemia Using Signal from Wearable ECG Sensors Considering Individual HRV Response Delays to Glucose Authors: Ha, Jiho; Hwang, Ho Bin; Kim, Hayoung; Lee, Seungyeon; Lee, Jeyeon; Park, Jung Hwan; Lee, Jongshill; Kim, In Young Abstract: Noninvasive blood glucose monitoring is crucial for detecting early dysglycemia, yet continuous glucose monitors remain invasive and costly. Electrocardiogram (ECG) and its derived heart rate variability (HRV) measure may offer a noninvasive indicator of autonomic and cardiac responses associated with acute changes in glucose. In this study, 30 adults underwent a 75 g oral glucose tolerance test with concurrent ECG Holter and interstitial glucose monitoring. From these recordings, HRV and ECG features were extracted. A deep learning classifier with HRV and ECG was then trained to detect hyperglycemia (glucose ≥ 180 mg/dL). Cross-correlation analysis confirmed a significant association between HRV and glucose (Pearson r ~0.65, p < 0.05) when aligning each participant’s data according to individual response delays. The model achieved high classification performance under rigorous temporal validation (accuracy ~89%, area under the receiver operating characteristic curve ~0.89). Saliency analyses revealed that the classifier’s decisions focus on distinct ECG waveform transitions and key HRV features linked to glucose-induced autonomic changes. Overall, acute hyperglycemia elicited discernible changes in HRV and cardiac conduction, supporting the feasibility of this physiologically grounded approach for detecting the acute hyperglycemic phase under controlled conditions. This method holds promise for real-time implementation in wearable devices, enabling early diabetes risk screening. Wed, 01 Apr 2026 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/213316 2026-04-01T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/211418 Title: Cerebral Blood Flow Estimation Using NIRS in Cardiac Arrest Patients: Correlation with ROSC Outcomes Authors: Choi, Soo Hyun; Jang, Dong-Hyun; Kim, In Young; Kim, Do Gwon; Kim, Hee Eun; Kang, Jihoon; Park, Seungmin; Lee, Dong Keon; Lee, J. Eyeon Abstract: Aim: Out-of-hospital cardiac arrest (OHCA) is a critical emergency. Although elevated mean arterial pressure (MAP) would be expected to enhance cerebral blood flow (CBF) during cardiopulmonary resuscitation (CPR), direct clinical data remain limited. This study examined how CBF responds to varying MAP levels during CPR in OHCA patients. Methods: This retrospective observational study included adult patients (≥18 years) with OHCA who underwent CPR with both invasive arterial monitoring and near-infrared spectroscopy (NIRS) measurements to assess cerebral blood flow changes were included. Mean arterial pressure was categorized into 20 mmHg intervals (0-20, 20-40, 40-60, 60-80 mmHg). Pearson correlation and linear regression analysis compared patients achieving return of spontaneous circulation (ROSC) with those who did not. Results: Among the 74 patients analyzed, NIRS-estimated CBF showed minimal responsiveness to MAP changes below 60 mmHg in both groups. A significant positive correlation between MAP and CBF emerged in the 60-80 mmHg range specifically among patients achieving ROSC (p < 0.001), but not in non-ROSC patients. Linear regression revealed steeper CBF increases with higher MAP values in the ROSC group beyond 60 mmHg. Conclusions: The relationship between MAP and CBF during CPR varies by pressure range, with a positive correlation emerging at mean arterial pressure ≥ 60 mmHg, specifically among patients with better short-term outcomes. Maintaining mean arterial pressure ≥ 60 mmHg may be beneficial to optimizing cerebral blood flow during resuscitation. Sun, 01 Mar 2026 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/211418 2026-03-01T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/213235 Title: Tau PET overlap index correlation with neuropathological findings Authors: Lim, Seokbeen; Lee, Jeyeon; Min, Paul H.; Moloney, Christina M.; Mester, Carly T.; Ghatamaneni, Sujala; Senjem, Matthew L.; Botha, Hugo; Knopman, David S.; McCarter, Stuart J.; Ramanan, Vijay K.; Savica, Rodolfo; Fields, Julie A.; Machulda, Mary M.; Dickson, Dennis W.; Reichard, R. Ross; Nguyen, Aivi T.; Graff‐Radford, Jonathan; Schwarz, Christopher G.; Gunter, Jeffrey L.; Kantarci, Kejal; Boeve, Bradley; Vemuri, Prashanthi; Jones, David T.; Jack, Clifford R.; Petersen, Ronald C.; Murray, Melissa E.; Lowe, Val J. Abstract: Introduction: The tau positron emission tomography (PET) overlap index (OI) has shown promise in maximizing signal-to-noise for longitudinal tau PET imaging, particularly for early tau pathology, but requires validation against neuropathology. Methods: Fifty-seven participants who underwent serial tau PET imaging (flortaucipir) and subsequent autopsy were included. Tau PET OI and standardized uptake value ratios (SUVRs) were compared across neuropathological diagnoses. Results: Tau PET OI showed greater concordance with neurofibrillary tangle (NFT) severity in the entorhinal cortex (a key region for Alzheimer's disease [AD] tauopathy) than SUVR, particularly in early Braak tangle stages (positivity: 52.2% for OI vs. 13.0% for SUVR). OI detected overlapping tau voxels that exhibited spatial correspondence with immunohistochemical and autoradiography measures of tau deposition across both AD and non-AD tauopathies. Discussion: These findings demonstrate the enhanced capacity of OI in serial tau PET to robustly detect early and spatially localized tau pathology, supporting its application as a sensitive imaging metric in AD and select non-AD tauopathies. Sun, 01 Mar 2026 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/213235 2026-03-01T00:00:00Z