Evaluation of Recurrent Neural Network Models for Parkinson's Disease Classification Using Drawing Data.

Area of Science:

• Neuroscience
• Artificial Intelligence
• Biomedical Engineering

Background:

• Parkinson's disease (PD) is a neurodegenerative disorder impacting motor function, speech, and cognition.
• Accurate PD diagnosis is challenging due to overlapping symptoms with natural aging.
• Deep learning offers potential breakthroughs for objective diagnostic tools.

Purpose of the Study:

• To investigate the utility of figure drawing data for Parkinson's disease diagnosis using deep learning.
• To compare the performance of Long Short-Term Memory (LSTM) and Echo State Networks (ESN) in analyzing time-series drawing data.
• To evaluate the advantages and disadvantages of LSTM and ESN architectures for PD detection.

Main Methods:

• Figure drawing data (coordinates, angles, pressure) were collected and treated as time-series signals.
• Recurrent neural network models, specifically LSTM and ESN, were trained on this data.
• The models were evaluated based on their ability to differentiate between PD patients and controls.

Main Results:

• Both LSTM and ESN models demonstrated potential in classifying Parkinson's disease based on drawing patterns.
• Comparative analysis highlighted specific strengths and weaknesses of each network architecture in capturing relevant temporal dynamics.
• Preliminary findings suggest figure drawing analysis via deep learning is a promising avenue for objective PD assessment.

Conclusions:

• Deep learning analysis of figure drawing time-series data shows promise for aiding Parkinson's disease diagnosis.
• Comparing LSTM and ESN provides insights into optimal recurrent network architectures for this application.
• Further research is warranted to refine these models and validate their clinical utility.