As the second most common neurodegenerative disease, Parkinson's disease has
caused serious problems worldwide. However, the cause and mechanism of PD are
not clear, and no systematic early diagnosis and treatment of PD have been
established. Many patients with PD have not been diagnosed or misdiagnosed. In
this paper, we proposed an EEG-based approach to diagnosing Parkinson's
disease. It mapped the frequency band energy of electroencephalogram(EEG)
signals to 2-dimensional images using the interpolation method and identified
classification using capsule network(CapsNet) and achieved 89.34%
classification accuracy for short-term EEG sections. A comparison of separate
classification accuracy across different EEG bands revealed the highest
accuracy in the gamma bands, suggesting that we need to pay more attention to
the changes in gamma band changes in the early stages of PD.

本文提出了一种基于脑电图的方法来诊断帕金森病，通过使用插值方法将脑电图信号的频带能量映射到二维图像中，并使用胶囊网络（CapsNet）进行分类，短期 EEG 部分的正确率达到 89.34％。通过在不同 EEG 带上分类的比较，我们发现在伽马波段的分类准确性最高，暗示我们需要更加关注早期 PD 患者的伽马波段的变化。