A Hybrid Model based on Convolutional Neural Networks and Long Short-term Memory for Rest Tremor Classification

Parkinson's disease is a neurodegenerative disease, in which tremor is the main symptom. Deep brain stimulation can help manage a broad range of neurological ailments such as Parkinson's disease. It involves electrical impulses delivered to specific targets in the brain, with the purpose of altering or modulating neural functioning. Security is playing a vital role in protecting healthcare gadgets from unauthorized access or modification. Our purpose is to adopt deep learning methodologies to classify resting tremors. To achieve this purpose, a novel approach for resting tremor classification in patients with Parkinson's disease using a hybrid model based on convolutional neural networks and long short-term memory is proposed. This research exploits the high-level feature extraction of the convolutional neural network model and the potential capacity to capture long-term dependencies of the long short-term memory model. The performed experiments demonstrate that our proposed approach outperforms the best result for other state-of-the-art methods.