PD-Net: Multi-Stream Hybrid Healthcare System for Parkinson's Disease Detection using Multi Learning Trick Approach

Abstract

Parkinson's disease is a neurodegenerative disorder that affects movement and muscle control and is caused by the loss of dopamine-producing neurons in the brain. The main symptoms of Parkinson's disease (PD) include tremors, rigidity, slowness of movement, imbalances, and linguistic impairment. One of the most pronounced clinical indicators is a change in the patient's voice, which can be used to assist in the diagnosis and evaluation of PD. An innovative method based on speech signals is proposed in this study to automatically identify PD by a sophisticated learning strategy to extract features via a parallel convolution-based network with an attention mechanism to preferentially focused on relevant PD cues. The proposed method utilized raw speech and i-vector as input tensors. We evaluated the method by different metrics including accuracy 98%, precision 0.99, recall 0.96, and f1-score 0.97 which shows the model's robustness.