Classification of Parkinson's disease EEG signals using 2D-MDAGTS model and multi-scale fuzzy entropy

Parkinson's disease (PD) is a neurodegenerative disorder that causes changes in neurons, behavior, and physiological structures. However, these changes are very subtle in the early stages of PD, making diagnosis and treatment challenging. To overcome this challenge, we propose a multi-scale fuzzy entropy (MSFEn) fusion method. MSFEn can be used as an EEG feature to quantify the complexity and irregularity of the EEG signal. We also propose a two-dimensional multiple dual attention gated temporal-separable (2D-MDAGTS) model for PD automatic detection. This model integrates temporal separable convolution (TSCN), gated recurrent unit (GRU), and dual attention network (DANet) to improve PD detection performance. TSCN can mine multi-level information in timing sequence and output processed features. Then the GRU is operated in parallel with the DANet to further integrate feature information. GRU can capture long -term dependencies of time series data and DANet can adjust the weight of features through the attention mechanism to better focus on the features related to PD. Two datasets were used to evaluate the proposed methods. In the classification of healthy subjects and drug-free PD patients, our results achieved an accuracy of 98.68% on the San Diego dataset and 99.30% on the UNM dataset. In the classification of healthy subjects and PD patients on medication, our results achieved an accuracy of 99.01% on the San Diego dataset and 99.31% on the UNM dataset. The results showed that this method is superior in the diagnosis of PD patients. The application of this method is expected to provide support for early diagnosis and disease monitoring.