Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Australia; Centre for Health Research, University of Southern Queensland, Toowoomba, Australia.
Mærsk Mc-Kinney Møller Institute, Faculty of Engineering, University of Southern Denmark, Denmark.
Comput Biol Med. 2024 May;174:108462. doi: 10.1016/j.compbiomed.2024.108462. Epub 2024 Apr 9.
Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life.
帕金森病（PD）是一种进行性神经退行性疾病，影响着全球超过 1000 万人的生活质量。早期诊断对于及时干预和改善患者预后至关重要。脑电图（EEG）信号由于其在监测疾病进展方面的潜力，通常用于早期 PD 诊断。但是，传统的基于 EEG 的方法缺乏对提供 PD 相关重要信息的大脑区域的探索，其性能无法满足实时应用的要求。为了解决这些限制，本研究提出了一种新的方法，使用基于时频表示（TFR）的 AlexNet 卷积神经网络（CNN）模型来探索基于 EEG 通道的分析，并从 EEG 数据中有效地识别出关键的大脑区域来诊断 PD。小波散射变换（WST）用于捕获独特的时频特征，而 AlexNet CNN 用于检测不同尺度下的复杂空间模式，准确识别与 PD 相关的复杂 EEG 模式。在两个实时 EEG PD 数据集上的实验结果：圣地亚哥数据集和爱荷华州数据集表明，与其他区域相比，额叶和中央脑区域，包括 AF4 和 AFz 电极，为 PD 检测提供了更具代表性的特征。所提出的架构在圣地亚哥数据集上达到了 99.84%的惊人准确率，在爱荷华州数据集上达到了 95.79%的准确率，优于现有的基于 EEG 的 PD 检测方法。这项研究的发现将有助于为高效 PD 诊断创建必要的技术，从而提高患者护理和生活质量。
