ORIGINAL RESEARCH article
Front. Hum. Neurosci.
Sec. Brain-Computer Interfaces
Volume 19 - 2025 | doi: 10.3389/fnhum.2025.1530291
Ensemble learning techniques reveals multidimensional EEG feature alterations in pediatric schizophrenia
Provisionally accepted
- 1Shaoxing People's Hospital, Shaoxing, Zhejiang Province, China
- 2Zhejiang Normal University, Jinhua, China
- 3Department of Neurosurgery, Sir Run Run Shaw Hospital, Hangzhou, Jiangsu Province, China
- 4Zhejiang University, Hangzhou, Zhejiang Province, China
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Schizophrenia (SCZ) is a severe mental disorder that impairs brain function and daily life, while its early and objective diagnosis remains a major clinical challenge due to the reliance on subjective assessments. This study aims to develop a machine learning-based framework for the auxiliary diagnosis of SCZ using multi-dimensional electroencephalogram (EEG) features and to investigate the underlying neural alterations. Resting-state EEG data were obtained from 45 male patients with pediatric SCZ and 39 age-and gender-matched healthy controls. Three types of EEG features (relative power (RP), fuzzy entropy (FuzEn), and functional connectivity (FC)) were extracted under various time window lengths and fed into four ensemble learning models. A data-driven feature selection approach (Recursive Feature Elimination) was applied to identify the most informative features, resulting in 212 most discriminative features (48 RP, 40 FuzEn, and 124 FC) out of the initial 760. Leveraging the selected features, the Categorical Boosting model achieved the highest classification accuracy of 99.60% at the 4-second window.Further analysis of the discriminative features revealed that the altered EEG characteristics were mainly in the alpha, beta, and gamma bands. Particularly, altered FCs exhibited a frontoincrease-parieto-decrease pattern mainly in the right hemisphere along with spectral-dependent RP alterations and a universally reduced FuzEn in the pediatric SCZ group. In summary, this study not only showcases the potential of advanced ensemble learning algorithms in precisely identifying pediatric SCZ, but also provides new insights into the altered brain functions in pediatric SCZ patients, which may benefit the future development of automatic diagnosis systems.
Keywords: Pediatric schizophrenia, Electroencephalogram, ensemble learning, Feature Selection, brain function
Received: 20 Mar 2025; Accepted: 29 Jul 2025.
Copyright: © 2025 Mao, Wang, Wang, Wang, Li, Qi and SUN. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Gang Li, Zhejiang Normal University, Jinhua, China
Xuchen Qi, Zhejiang Normal University, Jinhua, China
Yu SUN, Zhejiang University, Hangzhou, 310058, Zhejiang Province, China
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