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<p>Schizophrenia (SCZ) is a severe psychotic disorder characterized by cognitive and social deficits. Its diagnosis traditionally relies on subjective clinical assessments, which can be time-consuming and inconsistent. This study introduces a newly curated resting-state electroencephalogram (EEG) dataset comprising SCZ patients and healthy controls. A deep learning-driven analysis is performed to identify significant EEG frequency bands and brain regions that can serve as reliable biomarkers for accurate SCZ detection. We propose a novel two-stage investigation framework utilizing spectrograms generated via short-term Fourier transform (STFT). In the first stage, the most discriminative EEG frequency bands are identified across the entire brain. In the second stage, these bands are mapped to specific brain regions to localize the most diagnostically relevant signals. For classification, a deep learning-based ResNet-18 model is employed to analyze the EEG spectrograms. The findings reveal that delta band activity in the posterior brain region serves as the most effective biomarker, achieving a classification accuracy of 91.3% and an AUC of 0.97. This dual-layered framework not only pinpoints the most informative spectral and spatial EEG features but also underscores the potential of resting-state EEG and deep learning as powerful, objective tools for the clinical diagnosis of schizophrenia.</p>

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Deep learning-based identification of EEG biomarkers for schizophrenia using a two-stage spectral-spatial framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月11日

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  • Hardik Thakkar,
  • Soumya Jain,
  • Bikesh Kumar Singh,
  • Saurabh Gupta,
  • Sai Krishna Tikka,
  • Lokesh Kumar Singh

摘要

Schizophrenia (SCZ) is a severe psychotic disorder characterized by cognitive and social deficits. Its diagnosis traditionally relies on subjective clinical assessments, which can be time-consuming and inconsistent. This study introduces a newly curated resting-state electroencephalogram (EEG) dataset comprising SCZ patients and healthy controls. A deep learning-driven analysis is performed to identify significant EEG frequency bands and brain regions that can serve as reliable biomarkers for accurate SCZ detection. We propose a novel two-stage investigation framework utilizing spectrograms generated via short-term Fourier transform (STFT). In the first stage, the most discriminative EEG frequency bands are identified across the entire brain. In the second stage, these bands are mapped to specific brain regions to localize the most diagnostically relevant signals. For classification, a deep learning-based ResNet-18 model is employed to analyze the EEG spectrograms. The findings reveal that delta band activity in the posterior brain region serves as the most effective biomarker, achieving a classification accuracy of 91.3% and an AUC of 0.97. This dual-layered framework not only pinpoints the most informative spectral and spatial EEG features but also underscores the potential of resting-state EEG and deep learning as powerful, objective tools for the clinical diagnosis of schizophrenia.