Handwriting dynamics assessment using deep neural network for early identification of Parkinson's disease

The etiology of Parkinson's disease (PD) remains unclear. Symptoms usually appear after approximately 70% of dopamine-producing cells have stopped working normally. PD cannot be cured, but its symptoms can be managed to delay its progression. Evidence suggests that early diagnosis is important in establishing an effective pathway for management of symptoms. However, PD diagnosis is challenging, particularly in the early stages of the disease. In this paper, we present a method for early diagnosis of PD using patients' handwriting samples. To improve performance, we combined multiple PD handwriting datasets and used deep transfer learning-based algorithms to overcome the challenge of high variability in the handwritten material. Our approach achieved excellent PD identification performance with 99.22% accuracy on illuminated task of combined HandPD, NewHandPD and Parkinson's Drawing datasets, demonstrating the superiority of our approach over current state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved.