Next Article in Journal
Unplanned Excision in Soft Tissue Sarcoma: Current Knowledge and Remaining Gaps
Previous Article in Journal
Automatic Evaluation of Bone Age Using Hand Radiographs and Pancorporal Radiographs in Adolescent Idiopathic Scoliosis
Previous Article in Special Issue
Explainable AI for Bipolar Disorder Diagnosis Using Hjorth Parameters
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 15
Issue 4
10.3390/diagnostics15040454
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students

by
Flórián Gubics
1,
Ádám Nagy
2,*,
József Dombi
3,4,
Antónia Pálfi
2,
Zoltán Szabó
2,
Zsolt János Viharos
5,6,
Anh Tuan Hoang
5,
Vilmos Bilicki
2 and
István Szendi
7,8,9
1
Department of Medical Genetics, Doctoral School of Interdisciplinary Medicine, University of Szeged, 6720 Szeged, Hungary
2
Department of Software Engineering, University of Szeged, 6720 Szeged, Hungary
3
Department of Computer Algorithms and Artificial Intelligence, University of Szeged, Árpád Square 2, 6720 Szeged, Hungary
4
HUN-REN-SZTE Research Group on Artificial Intelligence, Institute of Informatics, University of Szeged, Tisza Lajos Boulevard 103, 6725 Szeged, Hungary
5
HUN-REN Institute for Computer Science and Control (SZTAKI), Center of Excellence in Production Informatics and Control, Centre of Excellence of the Hungarian Academy of Sciences (MTA), Kende Street 13-17, H-1111 Budapest, Hungary
6
Faculty of Economics and Business, John von Neumann University, Izsák Street 10, 6400 Kecskemét, Hungary
7
Department of Psychiatry, Kiskunhalas Semmelweis Hospital, Dr. Monszpart László Street 1, 6400 Kiskunhalas, Hungary
8
Department of Clinical- and Health Psychology, Institute of Psychology, University of Szeged, Egyetem Street 2, 6720 Szeged, Hungary
9
Centre of Excellence for Interdisciplinary Research, Development and Innovation, University of Szeged, Dugonics Square 13, 6720 Szeged, Hungary
*
Author to whom correspondence should be addressed.
Diagnostics 2025, 15(4), 454; https://doi.org/10.3390/diagnostics15040454
Submission received: 26 December 2024 / Revised: 2 February 2025 / Accepted: 7 February 2025 / Published: 13 February 2025
(This article belongs to the Special Issue A New Era in Diagnosis: From Biomarkers to Artificial Intelligence)
Browse Figures
Versions Notes

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.
Keywords: psychology; signal processing; analysis; bipolar depression; schizophrenia; early detection psychology; signal processing; analysis; bipolar depression; schizophrenia; early detection

Share and Cite

MDPI and ACS Style

Gubics, F.; Nagy, Á.; Dombi, J.; Pálfi, A.; Szabó, Z.; Viharos, Z.J.; Hoang, A.T.; Bilicki, V.; Szendi, I. A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students. Diagnostics 2025, 15, 454. https://doi.org/10.3390/diagnostics15040454

AMA Style

Gubics F, Nagy Á, Dombi J, Pálfi A, Szabó Z, Viharos ZJ, Hoang AT, Bilicki V, Szendi I. A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students. Diagnostics. 2025; 15(4):454. https://doi.org/10.3390/diagnostics15040454

Chicago/Turabian Style

Gubics, Flórián, Ádám Nagy, József Dombi, Antónia Pálfi, Zoltán Szabó, Zsolt János Viharos, Anh Tuan Hoang, Vilmos Bilicki, and István Szendi. 2025. "A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students" Diagnostics 15, no. 4: 454. https://doi.org/10.3390/diagnostics15040454

APA Style

Gubics, F., Nagy, Á., Dombi, J., Pálfi, A., Szabó, Z., Viharos, Z. J., Hoang, A. T., Bilicki, V., & Szendi, I. (2025). A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students. Diagnostics, 15(4), 454. https://doi.org/10.3390/diagnostics15040454

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop