  In this paper, we introduce a novel approach for diagnosis of Parkinson's
Disease (PD) based on deep Echo State Networks (ESNs). The identification of PD
is performed by analyzing the whole time-series collected from a tablet device
during the sketching of spiral tests, without the need for feature extraction
and data preprocessing. We evaluated the proposed approach on a public dataset
of spiral tests. The results of experimental analysis show that DeepESNs
perform significantly better than shallow ESN model. Overall, the proposed
approach obtains state-of-the-art results in the identification of PD on this
kind of temporal data.
