10.5281/zenodo.3675381
https://zenodo.org/records/3675381
oai:zenodo.org:3675381
Dimitrios Iakovakis
Dimitrios Iakovakis
0000-0002-6854-5942
Aristotle University of Thessaloniki
Stelios Hadjidimitriou
Stelios Hadjidimitriou
Aristotle University of Thessaloniki
Vasileios Charisis
Vasileios Charisis
Aristotle University of Thessaloniki
Sevasti Bostanjopoulou
Sevasti Bostanjopoulou
Department of Neurology, Hippokration Hospital
Zoe Katsarou
Zoe Katsarou
Department of Neurology, Hippokration Hospital Thessaloniki
Lisa Klingelhoefer
Lisa Klingelhoefer
Department of Neurology Technical University Dresden
Simone Mayer
Simone Mayer
Department of Neurology Technical University Dresden
Heinz Reichmann
Heinz Reichmann
Department of Neurology Technical University Dresden
Sofia B. Dias
Sofia B. Dias
Faculdade de Motricidade Humana Universidade de Lisboa
José A. Diniz
José A. Diniz
Faculdade de Motricidade Humana Universidade de Lisboa
Dhaval Trivedi
Dhaval Trivedi
International Parkinson Excellence Research Centre, King's College Hospital NHS Foundation Trust, London, United Kingdom
Ray K. Chaudhuri
Ray K. Chaudhuri
International Parkinson Excellence Research Centre, King's College Hospital NHS Foundation Trust, London, United Kingdom
Leontios J. Hadjileontiadis
Leontios J. Hadjileontiadis
Aristotle University of Thessaloniki
Early Parkinson's Disease Detection via Touchscreen Typing Analysis using Convolutional Neural Networks
Zenodo
2019
2019-07-28
10.5281/zenodo.3675380
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
Parkinson’s Disease (PD) is the second most common neurodegenerative disorder worldwide, causing both motor and non-motor symptoms. In the early stages, symptoms are mild and patients may ignore their existence. As a result, they do not undergo any related clinical examination; hence delaying their PD diagnosis. In an effort to remedy such delay, analysis of data passively captured from user’s interaction with consumer technologies has been recently explored towards remote screening of early PD motor signs. In the current study, a smartphone-based method analyzing subjects’ finger interaction with the smartphone screen is developed for the quantification of fine-motor skills decline in early PD using Convolutional Neural Networks. Experimental results from the analysis of keystroke typing in-the-clinic data from 18 early PD patients and 15 healthy controls have shown a classification
performance of 0.89 Area Under the Curve (AUC) with 0.79/0.79 sensitivity/specificity, respectively. Evaluation of the generalization ability of the proposed approach was made by its application on typing data arising from a separate self-reported cohort of 27 PD patients’ and 84 healthy controls’ daily usage with their personal smartphones (data in-the-wild), achieving 0.79 AUC with 0.74/0.78 sensitivity/specificity, respectively. The results show the potentiality of the proposed approach to process keystroke dynamics arising from users’ natural typing activity to detect PD, which contributes to the development of digital tools for remote pathological symptom screening.
European Commission
10.13039/501100000780
690494
Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS