Deep Echo State Networks for Diagnosis of Parkinson's Disease
This work addresses a domain-specific problem for medical diagnosis, offering an incremental improvement in accuracy for PD identification.
The paper tackles Parkinson's Disease diagnosis by analyzing time-series data from spiral tests using deep Echo State Networks, achieving state-of-the-art results with significant improvement over shallow models.
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.