Visual Representation of Online Handwriting Time Series for Deep Learning Parkinson's Disease Detection

Abstract

Parkinson's disease (PD) is a neurological disorder associated with a progressive decline in motor skills, speech, and cognitive processes. Since the diagnosis of Parkinson's disease is difficult, researchers have worked to develop a support tool based on algorithms to separate healthy controls from PD patients. Online handwriting dynamic signals can provide more detailed and complex information for PD detection task. Existing techniques often depended on hand-crafted features that required expert knowledge of the field. In this paper, it is suggested to learn pen-based features by means of deep learning for automatic classification of PD. For this purpose, a visual representation of the time series can be computed and used at the input of a convolutional neural network (CNN) as in [4]. Classically, the time series is transformed into a fixed dimension image applying normalization on the time dimension. In this work we have experimented several visual representations, including the spectrogram where normalization of the time scale is applied after short term information has been extracted locally. We have been able to show that considering the local short term information allows the deep learning models to provide better classification results compared to a globally normalized fixed dimension visual representation. For validation purpose, a CNN-BLSTM was directly applied on the time series, without any normalization of the time scale which led to best performance equivalent to the one obtained on spectrogram representation.