帕金森病
脑电图
卷积神经网络
人工智能
帕金森病
模式识别(心理学)
黑质
计算机科学
神经科学
医学
疾病
心理学
病理
作者
Hui Wen Loh,Chui Ping Ooi,Elizabeth E. Palmer,Prabal Datta Barua,Şengül Doğan,Türker Tuncer,Mehmet Bayğın,U. Rajendra Acharya
出处
期刊:Electronics
[MDPI AG]
日期:2021-07-20
卷期号:10 (14): 1740-1740
被引量:81
标识
DOI:10.3390/electronics10141740
摘要
Parkinson’s disease (PD) is globally the most common neurodegenerative movement disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the brain. However, current methods to diagnose PD on the basis of clinical features of Parkinsonism may lead to misdiagnoses. Hence, noninvasive methods such as electroencephalographic (EEG) recordings of PD patients can be an alternative biomarker. In this study, a deep-learning model is proposed for automated PD diagnosis. EEG recordings of 16 healthy controls and 15 PD patients were used for analysis. Using Gabor transform, EEG recordings were converted into spectrograms, which were used to train the proposed two-dimensional convolutional neural network (2D-CNN) model. As a result, the proposed model achieved high classification accuracy of 99.46% (±0.73) for 3-class classification (healthy controls, and PD patients with and without medication) using tenfold cross-validation. This indicates the potential of proposed model to simultaneously automatically detect PD patients and their medication status. The proposed model is ready to be validated with a larger database before implementation as a computer-aided diagnostic (CAD) tool for clinical-decision support.
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