Identifying robust markers of Parkinson’s disease in typing behaviour using a CNN-LSTM network

Neil Dhir, Mathias Edman, Álvaro Sanchez Ferro, Tom Stafford, Colin Bannard


Abstract
There is urgent need for non-intrusive tests that can detect early signs of Parkinson’s disease (PD), a debilitating neurodegenerative disorder that affects motor control. Recent promising research has focused on disease markers evident in the fine-motor behaviour of typing. Most work to date has focused solely on the timing of keypresses without reference to the linguistic content. In this paper we argue that the identity of the key combinations being produced should impact how they are handled by people with PD, and provide evidence that natural language processing methods can thus be of help in identifying signs of disease. We test the performance of a bi-directional LSTM with convolutional features in distinguishing people with PD from age-matched controls typing in English and Spanish, both in clinics and online.
Anthology ID:
2020.conll-1.47
Volume:
Proceedings of the 24th Conference on Computational Natural Language Learning
Month:
November
Year:
2020
Address:
Online
Editors:
Raquel Fernández, Tal Linzen
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
578–595
Language:
URL:
https://aclanthology.org/2020.conll-1.47
DOI:
10.18653/v1/2020.conll-1.47
Bibkey:
Cite (ACL):
Neil Dhir, Mathias Edman, Álvaro Sanchez Ferro, Tom Stafford, and Colin Bannard. 2020. Identifying robust markers of Parkinson’s disease in typing behaviour using a CNN-LSTM network. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 578–595, Online. Association for Computational Linguistics.
Cite (Informal):
Identifying robust markers of Parkinson’s disease in typing behaviour using a CNN-LSTM network (Dhir et al., CoNLL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.conll-1.47.pdf