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

Proceedings of the 24th Conference on Computational Natural Language Learning, pages 578–595 Online, November 19-20, 2020. c⃝2020 Association for Computational Linguistics https://doi.org/10.18653/v1/P17 578 Identifying Robust Markers of Parkinson’s Disease in Typing Behaviour Using a CNN-LSTM Network Neil Dhir12†, Mathias Edman1†, ´Alvaro S´anchez Ferro3, Tom Stafford4, Colin Bannard5 1Kamin AI, 2Alan Turing Institute, 3HM CINAC, 4University of Sheffield, 5University of Liverpool {neil,mathias}@kamin.ai, alvarosferro@hotmail.com t.stafford@sheffield.ac.uk, cbannard@liverpool.ac.uk Abstract There is urgent need for non-intrusive tests that can detect early signs of Parkinson’s dis- ease (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 ar- gue that the identity of the key combinations being produced should impact how they are handled by people with PD, and provide evi- dence that natural language processing meth- ods 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 Span- ish, both in clinics and online.1 1 Introduction Parkinson’s disease is a neurodegenerative disease that affects approximately 1% of people over the age of 60 (De Lau and Breteler, 2006). Its cardinal manifestations include bradykinesia (slowness of movement), tremor and rigidity. These result from the degeneration of dopaminergic neurons in the basal ganglia (an area of the brain responsible for action selection). A particular challenge in the treat- ment of PD is that by the time such motor signs are present, over 50% of neurons in the affected area of the basal ganglia (the substantia nigra) have been lost (Fearnley and Lees, 1991). While neuroimag- ing can pick up on these changes (Barber et al., 2017), such procedures are prohibitively expensive and cannot be performed on whole populations. There is thus an urgent need for cheap and easy- †Equal contribution. 1Code, models and data used in this paper can be found at: http://typingresearch.com/conll2020/ to-administer measures that can be used for the identification of at-risk individuals. A long-used simple motor test for PD is the al- ternating finger tapping test (Burns and DeJong, 1960). This test involves asking a person to alter- nately tap an index finger in two locations a set distance apart on a surface or on a keyboard (Gio- vannoni et al., 1999). People with PD are typically able to perform fewer taps over a 30 second pe- riod than people with no diagnosis. While such measures have proved useful, they suffer from a clear lack of specificity – slowing of movement is also a strong predictor of other neurodegenera- tive disorders, such as Alzeimer’s disease (Roalf et al., 2018). Furthermore, neural degeneration is unlikely to be detected by as coarse-grained a mea- sure as tapping rate until the disease is relatively advanced. If specificity and earlier detection is to be achieved more targeted …

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