Attention-Based Deep Learning Model for Early Detection of Parkinson's Disease

Parkinson's disease (PD), classified under the category of a neurological syndrome, affects the brain of a person which leads to the motor and non-motor symptoms. Among motor symptoms, one of the major disabling symptom is Freezing of Gait (FoG) that affects the daily standard of living of PD patients. Available treatments target to improve the symptoms of PD. Detection of PD at the early stages is an arduous task due to being indistinguishable from a healthy individual. This work proposed a novel attention-based model for the detection of FoG events and PD, and measuring the intensity of PD on the United Parkinson's Disease Rating Scale. Two separate datasets, that is, UCF Daphnet dataset for detection of Freezing of Gait Events and PhysioNet Gait in PD Dataset were used for training and validating on their respective problems. The results show a definite rise in the various performance metrics when compared to landmark models on these problems using these datasets. These results strongly suggest that the proposed state of the art attention-based deep learning model provide a consistent as well as an efficient solution to the selected problem. High values were obtained for various performance metrics like accuracy of 98.74% for detection FoG, 98.72% for detection of PD and 98.05% for measuring the intensity of PD on UPDRS. The model was also analyzed for robustness against noisy samples, where also model exhibited consistent performance. These results strongly suggest that the proposed model provides a better classification method for selected problem.