https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/431 Fri, 24 Jul 2026 08:07:48 GMT 2026-07-24T08:07:48Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/218433 Title: Topological Prior Vector for quantifying PPG waveform morphology: Metrological characteristics and a hemodynamic state monitoring demonstration Authors: Yi, Myung-Kyu; Lee, Jongshill; Lee, Jeyeon; Kim, In Young Abstract: Photoplethysmography (PPG) waveform analysis for wearable monitoring remains challenging because waveform quantification is easily affected by noise, sampling variability, fiducial-point uncertainty, and strong inter-subject differences. To address this problem, we propose the Topological Prior Vector (TPV), a deterministic 33-dimensional descriptor that transforms persistent-homology-derived topology into an explicitly defined and reproducible statistical representation of global PPG morphology. Unlike rhythm-oriented variability metrics or fiducial-dependent waveform indices, TPV summarizes the structural organization of delay-embedded pulse trajectories through fixed statistical summaries of birth, death, lifetime, dispersion, complexity, and entropy. We evaluate TPV from a measurement-oriented perspective by examining repeatability, perturbation robustness, and statistical sensitivity to waveform variation on two public datasets. The results show that TPV preserves stable within-subject morphology profiles and remains consistent under controlled signal degradation. In addition, TPV exhibits state-dependent statistical associations across blood-pressure-defined groups, with clearer monotonic tendencies in normotensive conditions and attenuated relationships in hypertensive conditions, suggesting that waveform-topology coupling is regime-dependent rather than uniformly linear. As an initial downstream validation for normotensive versus hypertensive state discrimination, TPV was further evaluated using conventional tree-based classifiers. In intra-subject and inter-subject settings, Random Forest achieved AUCs of 0.83 and 0.75, respectively, while LightGBM yielded AUCs of 0.80 and 0.779, supporting the representational utility of TPV across different classifier choices. These findings suggest that TPV is best understood not as a direct physiological surrogate, but as a compact, reproducible, and measurement-oriented statistical descriptor for profiling global waveform structure under wearable sensing uncertainty. Tue, 01 Sep 2026 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/218433 2026-09-01T00:00:00Z 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