论文标题
使用胶囊网络的帕金森氏病诊断的基于脑电图的方法
An EEG-based approach for Parkinson's disease diagnosis using Capsule network
论文作者
论文摘要
作为第二常见的神经退行性疾病，帕金森氏病在全球范围内引起了严重的问题。但是，PD的原因和机制尚不清楚，并且尚未确定PD的系统性早期诊断和治疗。许多PD患者尚未被诊断或误诊。在本文中，我们提出了一种基于脑电图的方法来诊断帕金森氏病。它使用插值方法将脑电图（EEG）信号的频带能量映射到二维图像，并使用胶囊网络（CAPSNET）确定了分类，并实现了短期EEG部分的89.34％分类精度。对不同EEG频段的单独分类精度的比较表明，伽马频段的精度最高，这表明我们需要更加注意PD早期伽马频段变化的变化。
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.
