Application of Deep Learning in the Classification of EEG Signals of Parkinson's Disease Patients

Abstract

This article summarizes research on classifying electroencephalogram (EEG) signals of Parkinson's disease (PD) patients using deep learning technologies. Parkinson's disease is a common neurodegenerative disorder that severely affects quality of life. EEG signals provide valuable information about brain activity. Therefore, analyzing and classifying the EEG signals of PD patients can aid in early diagnosis and treatment. The paper outlines the assessment metrics and cross-validation methods in the diagnosis of Parkinson's disease, highlighting the effectiveness of deep learning in diagnosis. The key technologies reviewed in the article include Wavelet Packet Transform (WPT) and Deep Residual Neural Networks (DRSN), with the WPT-DRSN method achieving up to 99.92% prediction accuracy in the binary classification task of PD patients. Furthermore, this method has also been applied to more complex classweweification tasks, such as categorizing PD patients, REM sleep behavior disorder patients, and healthy controls, maintaining high accuracy. When analyzing Parkinson's disease EEG with the CRNN model, the accuracy is high, but the sample size is small and the model interpretability is limited, necessitating further validation and improvement. For EEG-based PD diagnosis and classification methods, high accuracy was achieved through ICA preprocessing, CSP feature extraction, and ensemble learning classifiers. Future research should consider the integration of multimodal data fusion, deep learning, and NLP technologies, as well as the development of personalized models to enhance the accuracy of PD diagnosis.