Generalizable electroencephalographic classification of Parkinson's disease using deep learning

Growing interest surrounds the use of electroencephalography (EEG) and deep learning for diagnosing neurological conditions like Parkinson's Disease (PD). Despite the existing proof-of-concept literature demonstrating the potential of deep learning in classifying PD from EEG data, neurologists have been slow to adopt these tools due to insufficient evidence of their real-world diagnostic generalizable performance. Our study aimed to evaluate the potential of deep learning for inter-subject PD classification using a conservative training approach and testing on an external independent dataset. Specifically, we utilized publicly available resting-state EEG data from PD patients at two separate centers, the University of New Mexico (n = 54) and the University of Iowa (n = 28), as our training and testing sets, respectively. Each of these recordings had a minimum of 2 minutes of data. We implemented a channel-wise convolutional neural network, tuning it with a leave-one-subject-out cross-validation approach. Our approach achieved a patient-level accuracy of 80.4% (epoch-level accuracy = 72.7%), which remained consistent when tested on the external dataset (patient-level accuracy = 82.8%, epoch-level accuracy = 75.7%). Our model performs equal-or-better than other standard classification models and our approach compares favourably to similar works. Our publicly available code serves as a foundation for future research exploring different deep learning architectures, investigating other pathologies, and involving larger datasets with the hope of accelerating the adoption of objective computational approaches for the diagnosis and monitoring of neurological disorders