Clinical Implication of Maumgyeol Basic Biotypes-Electroencephalography- and Photoplethysmogram-Based Bwave State...

Area of Science:

• Biomedical Engineering
• Neuroscience
• Psychiatry

Background:

• Individual differences in mental health are complex and not fully captured by subjective reports.
• Biomarker-based approaches offer objective measures for understanding mental health heterogeneity.
• Electroencephalography (EEG) and photoplethysmogram (PPG) are non-invasive physiological signals with potential for mental health assessment.

Purpose of the Study:

• To develop a clinically relevant subtype classification system for mental health using EEG and PPG.
• To investigate individual differences in mental health based on objective biomarkers.
• To explore the utility of combined EEG and PPG signals for subtype identification.

Main Methods:

• Recruited 100 healthy participants and 99 patients with psychiatric disorders.
• Established classification thresholds using EEG and PPG data from 2,278 individuals without mental disorders.
• Employed multivariate analysis of variance (MANOVA) and k-means clustering to analyze and verify subtypes.

Main Results:

• Identified distinct subtype distributions between healthy individuals and psychiatric patients.
• Demonstrated that cognitive abilities correlate with brain subtypes, while mind subtypes show variations in symptom severity, overall health, and cognitive stress.
• Confirmed comparability between theory-based and data-driven classification using k-means clustering.

Conclusions:

• The developed subtype classification system provides an objective measure of mental health.
• Utilizing EEG and PPG signals for subtype classification holds significant promise for the future of digital mental healthcare.
• This approach facilitates a deeper understanding of individual differences in mental health.