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        <title>Identifying robust markers of Parkinson’s disease in typing behaviour using a CNN-LSTM network</title>
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        <namePart type="given">Neil</namePart>
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        <namePart type="given">Mathias</namePart>
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    <name type="personal">
        <namePart type="given">Álvaro</namePart>
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    <name type="personal">
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.conll-1.47</identifier>
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%0 Conference Proceedings
%T Identifying robust markers of Parkinson’s disease in typing behaviour using a CNN-LSTM network
%A Dhir, Neil
%A Edman, Mathias
%A Sanchez Ferro, Álvaro
%A Stafford, Tom
%A Bannard, Colin
%S Proceedings of the 24th Conference on Computational Natural Language Learning
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F dhir-etal-2020-identifying
%X 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.
%R 10.18653/v1/2020.conll-1.47
%U https://aclanthology.org/2020.conll-1.47
%U https://doi.org/10.18653/v1/2020.conll-1.47
%P 578-595

Markdown (Informal)

[Identifying robust markers of Parkinson’s disease in typing behaviour using a CNN-LSTM network](https://aclanthology.org/2020.conll-1.47) (Dhir et al., CoNLL 2020)

• Identifying robust markers of Parkinson’s disease in typing behaviour using a CNN-LSTM network (Dhir et al., CoNLL 2020)

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.