Gubics, F and Nagy, Á and Dombi, J and Pálfi, A and Szabó, Z and Viharos, Zsolt János and Hoang, Anh Tuan and Bilicki, V and Szendi, I (2025) A Machine-Learning-Based Analysis of Resting Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students. DIAGNOSTICS, 15 (4). ISSN 2075-4418 10.3390/diagnostics15040454

Text Gubics_1_35760272_ny.pdf Download (2MB)

Abstract

Background: Early and accurate diagnosis is crucial for effective prevention and treatment of severe mental illnesses, such as schizophrenia and bipolar disorder. However, identifying these conditions in their early stages remains a significant challenge. Our goal was to develop a method capable of detecting latent disease liability in healthy volunteers. Methods: Using questionnaires examining affective temperament and schizotypal traits among voluntary, healthy university students (N = 710), we created three groups. These were a group characterized by an emphasis on positive schizotypal traits (N = 20), a group showing cyclothymic temperament traits (N = 17), and a control group showing no susceptibility in either direction (N = 21). We performed a resting-state EEG examination as part of a complex psychological, electrophysiological, psychophysiological, and laboratory battery, and we developed feature-selection machine-learning methods to differentiate the low-risk groups. Results: Both low-risk groups could be reliably (with 90% accuracy) separated from the control group. Conclusions: Models applied to the data allowed us to differentiate between healthy university students with latent schizotypal or bipolar tendencies. Our research may improve the sensitivity and specificity of risk-state identification, leading to more effective and safer secondary prevention strategies for individuals in the prodromal phases of these disorders.

Item Type:	Article
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Research Laboratory on Engineering & Management Intelligence
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	28 Feb 2025 09:09
Last Modified:	28 Feb 2025 09:09
URI:	https://eprints.sztaki.hu/id/eprint/10874

Update Item