Parkinson's disease (PD), a severe and progressive neurological illness,
affects millions of individuals worldwide. For effective treatment and
management of PD, an accurate and early diagnosis is crucial. This study
presents a deep learning-based model for the diagnosis of PD using resting
state electroencephalogram (EEG) signal. The objective of the study is to
develop an automated model that can extract complex hidden nonlinear features
from EEG and demonstrate its generalizability on unseen data. The model is
designed using a hybrid model, consists of convolutional neural network (CNN),
bidirectional gated recurrent unit (Bi-GRU), and attention mechanism. The
proposed method is evaluated on three public datasets (Uc San Diego Dataset,
PRED-CT, and University of Iowa (UI) dataset), with one dataset used for
training and the other two for evaluation. The results show that the proposed
model can accurately diagnose PD with high performance on both the training and
hold-out datasets. The model also performs well even when some part of the
input information is missing. The results of this work have significant
implications for patient treatment and for ongoing investigations into the
early detection of Parkinson's disease. The suggested model holds promise as a
non-invasive and reliable technique for PD early detection utilizing resting
state EEG.

该研究提出了一种基于深度学习模型的帕金森病诊断方法，利用静息状态脑电图信号，通过提取复杂的隐藏非线性特征并展示其在未见数据上的泛化能力，该模型由卷积神经网络、双向门控循环单元和注意力机制构成，并在三个公开数据集上进行评估，结果表明该模型在训练和测试数据集上均能准确诊断帕金森病，即使输入信息的部分缺失也能表现良好。该研究结果对患者治疗和帕金森病早期检测持有重要意义，该提出的模型有望成为一种非侵入性且可靠的利用静息状态脑电图进行帕金森病早期检测的技术。

一种基于混合深度时空注意力模型的帕金森病诊断方法，使用静息状态脑电信号

A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's  Disease Diagnosis Using Resting State EEG Signals

Obesity is a common issue in modern societies today that can lead to various
diseases and significantly reduced quality of life. Currently, research has
been conducted to investigate resting state EEG (electroencephalogram) signals
with an aim to identify possible neurological characteristics associated with
obesity. In this study, we propose a deep learning-based framework to extract
the resting state EEG features for obese and lean subject classification.
Specifically, a novel variational autoencoder framework is employed to extract
subject-invariant features from the raw EEG signals, which are then classified
by a 1-D convolutional neural network. Comparing with conventional machine
learning and deep learning methods, we demonstrate the superiority of using VAE
for feature extraction, as reflected by the significantly improved
classification accuracies, better visualizations and reduced impurity measures
in the feature representations. Future work can be directed to gaining an
in-depth understanding regarding the spatial patterns that have been learned by
the proposed model from a neurological view, as well as improving the
interpretability of the proposed model by allowing it to uncover any
temporal-related information.

本文提出了一种基于深度学习的框架来从放松状态脑电图信号中提取肥胖和健康人群的特征，并使用时空变分自动编码器和一维卷积神经网络分类。结果显示，该方法在分类精度、特征可视化和特征表示的杂质度方面具有优势。未来的方向包括在从神经学的角度深入了解模型所学习的空间模式以及允许发掘任何时间相关信息来提高模型的可解释性。