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.
@article{arxiv.1802.06708,
title = {Deep Echo State Networks for Diagnosis of Parkinson's Disease},
author = {Claudio Gallicchio and Alessio Micheli and Luca Pedrelli},
journal= {arXiv preprint arXiv:1802.06708},
year = {2018}
}
Comments
This is a pre-print of the paper submitted to the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018