Different Classification Approaches for Early Detection of Parkinson’s Disease


Nikita Aggarwal
Department of ECE, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, India
Jasleen Saini
Dept of CSE UIET, Panjab University Chandigarh, India
B.S. Saini
Department of ECE Dr. B.R. Ambedkar National Institute of Technology Jalandhar, India
Savita Gupta
Dept of CSE UIET, Panjab University Chandigarh, India


Parkinson’s disease is perhaps the most well-known neurodegenerative disorder that mainly occurs due to the loss of dopamine-producing neurons and consists of motor/non-motor symptoms. The progression of the symptoms is often varying from one person to another to the diversity of the disease. The condition causes a huge burden both on those affected, as well as their families. Accurate diagnosis is critical and challenging but still, no specific diagnostic process is available. The computer-aided diagnosis techniques of signalling and imaging processing are very helpful in the prediction and classification of PD. This review gives a brief description of different methods of classification for early detection and also highlights the most profitable research directions by focusing on continuous monitoring patterns of daily activities, interactions, and routine that may provide the data on status changes, clinical management, and controlling self-correction.

September 22, 2021
Online ISSN