Detection of Parkinson's disease from handwriting using deep learning: a comparative study

Degenerative disorders such as Parkinson's disease (PD) have an influence on daily activities due to rigidity of muscles, tremor or cognitive impairment. Micrographia, speech intensity, and deficient generation of voluntary saccadic eye movements (Pretegiani and Optican in Front Neurol 8:592, 2017) are manifestations of PD that can be used to devise noninvasive and low cost clinical tests. In this context, we have collected a multimodal dataset that we call Parkinson's disease Multi-Modal Collection (PDMultiMC), which includes online handwriting, speech signals, and eye movements recordings. We present here the handwriting dataset that we call HandPDMultiMC that will be made publicly available. The HandPDMultiMC dataset includes handwriting samples from 42 subjects (21 PD and 21 controls). In this work we investigate the application of various Deep learning architectures, namely the CNN and the CNN-BLSTM, to PD detection through time series classification. Various approaches such as Spectrograms have been applied to encode pen-based signals into images for the CNN model, while the raw time series are directly used in the CNN-BLSTM. In order to train these models for PD detection on large scale data, various data augmentation approaches for pen-based signals are proposed. Experimental results on our dataset show that the best performance for early PD detection (97.62% accuracy) is reached by a combination of CNN-BLSTM models trained with Jittering and Synthetic data augmentation approaches. We also illustrate that deep architectures can surpass the models trained on pre-engineered features even though the available data is small.