Evaluation of recurrent neural network models for Parkinson’s disease classification using drawing data

Parkinson’s disease is a disorder that affects the neurons in the human brain. It is characterized by various symptoms such as slowness of motor functions (bradykinesia), motor instability, speech impairment and, in some cases, psychiatric effects such as hallucinations. Most of the symptoms mentioned here, however, are also common side effects of normal aging. This makes an accurate diagnosis of Parkinson’s disease a challenging task. Some breakthroughs have been made in recent years with the help of deep learning. This work aims at considering figure drawing data as a time series of coordinates, angles and pressure readings to train recurrent neural network models. In addition, the work compares two recurrent network models, Long Short-Term Memory and Echo State Networks, to explore the advantages and disadvantages of both architectures.