A significantly enhanced neural network for handwriting assessment in Parkinson's disease detection

In recent years, machining learning aided diagnosis can provide non-invasive, low-cost tools to support clinicians and assist the diagnosis and monitoring of neurodegenerative disorders, in particular Parkinson's disease (PD). As an important motor symptom, disorder of the hand motion is usually used for diagnosis and evaluation of PD; moreover, majority of the patients with PD have handwriting abnormalities, which plays a special role in PD detection. In this paper, as an useful tool, we propose a novel hybrid model to learn the handwriting differences between PD patients and healthy controls, by learning and enhancing significant features from three handwriting exams, i.e., meander, circle and spiral. Based on a three-layer convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU), the proposed network can assess the potential of sequential information of handwriting in identifying Parkinsonian symptoms. Compared with several state of the art studies, the recognition rates of our proposed framework are 92.91%, 85.71% and 90.55% respectively in these three tests, which verifies the excellent classification effect.

Automated summary

In recent years, machining learning aided diagnosis has been used to support clinicians and assist in the diagnosis and monitoring of neurodegenerative disorders, particularly Parkinson's disease. This paper proposes a novel hybrid model that learns and enhances significant features from handwriting exams to detect Parkinson's disease. The proposed network, based on a CNN and BiGRU, effectively identifies Parkinsonian symptoms with recognition rates of 92.91%, 85.71%, and 90.55% in three tests.