CLINICAL IMPLICATION OF MAUMGYEOL BASIC BIOTYPES – EEG- AND PPG-BASED BWAVE STATE INVENTORY (EBSI)

Abstract Background The development of individual subtypes based on biomarkers offers an intriguing and timely avenue for capturing individual factors pertaining to mental health independent from individuals’ insights. Aims & Objectives Incorporating 2-channel electroencephalography (EEG) and photoplethysmogram (PPG), we sought to develop subtype classification system with clinical relevance. Method One hundred healthy participants and 99 patients with psychiatric disorders were recruited. Classification thresholds were established using the EEG and PPG data from 2,278 individuals without mental disorders, which used to classify subtypes of 199 participants. MANOVA (Multiple Analysis of Variance) was applied to examine psychological differences between the subtypes. K-means clustering was employed to verify the classification system. Results The distribution of subtypes differed between healthy participants and those with psychiatric disorders. Cognitive ability differed depending on brain subtypes, whereas mind subtypes showed significant differences in symptom severity, overall health, and cognitive stress. K-means clustering revealed that the results of our theory-based classification and data-driven classification are comparable. Discussion & Conclusion The utilization of EEG and PPG signals for subtype classification offers potential for the future of digital mental healthcare.