# Deep Echo State Networks for Diagnosis of Parkinson's Disease

arXiv: https://arxiv.org/abs/1802.06708 · HTML: https://mlsanity.com/paper/1802.06708/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Exploiting Multiple Timescales in Hierarchical Echo State Networks](https://mlsanity.com/paper/2101.04223/) (2021)

## Abstract (arXiv)

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.