Brain region localization: a rapid Parkinson's disease detection method based on EEG signals.

Journal: Medical & biological engineering & computing
Published Date: Jul 9, 2025

Abstract

Parkinson's disease (PD) is a prevalent neurodegenerative disorder worldwide, often progressing to mild cognitive impairment (MCI) and dementia. Clinical diagnosis of PD mainly depends on characteristic motor symptoms, which can lead to misdiagnosis, underscoring the need for reliable biomarkers. Early detection of PD and effective monitoring of disease progression are crucial for enhancing patient outcomes. Electroencephalogram (EEG) signals, as non-invasive neural recordings, show great promise as diagnostic biomarkers. In this study, we present a novel approach for PD diagnosis through the analysis of EEG signals from distinct brain regions. We used two publicly available EEG datasets and constructed three-dimensional (3D) time-frequency spectrograms for each brain region using the continuous wavelet transform (CWT). To improve feature representation, these spectrograms were encoded in the red-green-blue (RGB) color space. A ResNet18 model was trained separately on the spectrograms of each brain region, and its performance was assessed using the leave-one-subject-out cross-validation (LOSOCV) method. The proposed method achieved classification accuracies of 92.86% and 90.32% on the two datasets, respectively. The experimental results confirm the efficacy of our approach, highlighting its potential as a valuable tool to aid clinical diagnosis of PD.

Authors

  • Mingliang Zhang
    Fuzhou First General Hospital Affiliated with Fujian Medical University, Fuzhou, People's Republic of China.
  • Hang Liu
    Interventional Department, Changhai Hospital, Second Military Medical University, Shanghai 200433, China.
  • Zhenghao Guo
    Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China.
  • Cui Wang
    Fintech Innovation Center, Financial Intelligence and Financial Engineering Key Laboratory, Southwestern University of Finance and Economics (SWUFE), Chengdu, 611130, China.
  • Timo Hamalainen
    Faculty of Information Technology, Jyvaskyla, Finland.
  • Fengyu Cong
    School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024, Dalian, China; Faculty of Information Technology, University of Jyväskylä, 40014, Jyväskylä, Finland; School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024, Dalian, China; Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province. Dalian University of Technology, 116024, Dalian, China.

Keywords

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