通过深度学习分析帕金森病患者脑电波，发现中央与顶区特定频段异常模式可作早期诊断依据。
Oscillatory Signatures of Parkinson's Disease: Central and Parietal EEG Alterations Across Multiple Frequency Bands
- 用小波变换处理电极对图像，结合卷积神经网络分类脑电信号。
- 中央区全频段分析达76%准确率，右顶区10秒窗口达74%准确率。
- 右半球在多个频段表现明显，且排除震颤干扰更可信。
本研究利用深度学习分析31名受试者（15名帕金森病患者，16名健康对照）的静息态脑电图，以探索其作为早期生物标志物的潜力。在去除震颤伪影后，采用基于小波变换的电极三元组图像输入卷积神经网络进行分类。跨脑区与频段分析揭示了帕金森病相关的神经振荡特征性空间-频谱模式：使用中央电极（C3, Cz, C4）进行全频段（0.4–62.4 Hz）分析时分类准确率达76%；右顶区（P8, CP6, P4）在10秒窗口下准确率为74%。双侧中央-顶区在theta频段（4.0–7.79 Hz）表现良好（67%），多区域在alpha频段（7.8–15.59 Hz）敏感度为65%。此外，γ频段（40–62.4 Hz）的改变特异性地集中在中央-顶区，且在不同时间窗下保持一致。尤其值得注意的是，多个频段均显示右侧半球显著参与。相较以往可能混入震颤信号的研究，本方法仅捕捉真实皮层神经活动变化，表明特定中央与顶区、多频段的脑电振荡模式或可提供帕金森病早期诊断信息，甚至早于运动症状出现。
原文摘要 · Abstract (English)
This study investigates EEG as a potential early biomarker by applying deep learning techniques to resting-state EEG recordings from 31 subjects (15 with PD and 16 healthy controls). EEG signals underwent preprocessing to remove tremor artifacts before classification with CNNs using wavelet-based electrode triplet images. Our analysis across different brain regions and frequency bands showed distinct spatial-spectral patterns of PD-related neural oscillations. We identified high classification accuracy (76%) using central electrodes (C3, Cz, C4) with full-spectrum 0.4-62.4 Hz analysis and 74% accuracy in right parietal regions (P8, CP6, P4) with 10-second windows. Bilateral centro-parietal regions showed strong performance (67%) in the theta band (4.0-7.79 Hz), while multiple areas demonstrated some sensitivity (65%) in the alpha band (7.8-15.59 Hz). We also observed a distinctive topographical pattern of gamma band (40-62.4 Hz) alterations specifically localized to central-parietal regions, which remained consistent across different temporal windows. In particular, we observed pronounced right-hemisphere involvement across several frequency bands. Unlike previous studies that achieved higher accuracies by potentially including tremor artifacts, our approach isolates genuine neurophysiological alterations in cortical activity. These findings suggest that specific EEG-based oscillatory patterns, especially in central and parietal regions and across multiple frequency bands, may provide diagnostic information for PD, potentially before the onset of motor symptoms.
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