Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques

Abstract Parkinson’s Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both motor and non-motor symptoms of escalating severity as it progresses. Since it cannot be cured, treatment options focus on the improvement of...

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Autores principales:	Alexandros Papadopoulos, Dimitrios Iakovakis, Lisa Klingelhoefer, Sevasti Bostantjopoulou, K. Ray Chaudhuri, Konstantinos Kyritsis, Stelios Hadjidimitriou, Vasileios Charisis, Leontios J. Hadjileontiadis, Anastasios Delopoulos
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4e9104bc0664484dbf31156c7c675e0e
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spelling	oai:doaj.org-article:4e9104bc0664484dbf31156c7c675e0e2021-12-02T11:41:18ZUnobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques10.1038/s41598-020-78418-82045-2322https://doaj.org/article/4e9104bc0664484dbf31156c7c675e0e2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78418-8https://doaj.org/toc/2045-2322Abstract Parkinson’s Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both motor and non-motor symptoms of escalating severity as it progresses. Since it cannot be cured, treatment options focus on the improvement of PD symptoms. In fact, evidence suggests that early PD intervention has the potential to slow down symptom progression and improve the general quality of life in the long term. However, the initial motor symptoms are usually very subtle and, as a result, patients seek medical assistance only when their condition has substantially deteriorated; thus, missing the opportunity for an improved clinical outcome. This situation highlights the need for accessible tools that can screen for early motor PD symptoms and alert individuals to act accordingly. Here we show that PD and its motor symptoms can unobtrusively be detected from the combination of accelerometer and touchscreen typing data that are passively captured during natural user-smartphone interaction. To this end, we introduce a deep learning framework that analyses such data to simultaneously predict tremor, fine-motor impairment and PD. In a validation dataset from 22 clinically-assessed subjects (8 Healthy Controls (HC)/14 PD patients with a total data contribution of 18.305 accelerometer and 2.922 typing sessions), the proposed approach achieved 0.86/0.93 sensitivity/specificity for the binary classification task of HC versus PD. Additional validation on data from 157 subjects (131 HC/26 PD with a total contribution of 76.528 accelerometer and 18.069 typing sessions) with self-reported health status (HC or PD), resulted in area under curve of 0.87, with sensitivity/specificity of 0.92/0.69 and 0.60/0.92 at the operating points of highest sensitivity or specificity, respectively. Our findings suggest that the proposed method can be used as a stepping stone towards the development of an accessible PD screening tool that will passively monitor the subject-smartphone interaction for signs of PD and which could be used to reduce the critical gap between disease onset and start of treatment.Alexandros PapadopoulosDimitrios IakovakisLisa KlingelhoeferSevasti BostantjopoulouK. Ray ChaudhuriKonstantinos KyritsisStelios HadjidimitriouVasileios CharisisLeontios J. HadjileontiadisAnastasios DelopoulosNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alexandros Papadopoulos Dimitrios Iakovakis Lisa Klingelhoefer Sevasti Bostantjopoulou K. Ray Chaudhuri Konstantinos Kyritsis Stelios Hadjidimitriou Vasileios Charisis Leontios J. Hadjileontiadis Anastasios Delopoulos Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques
description	Abstract Parkinson’s Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both motor and non-motor symptoms of escalating severity as it progresses. Since it cannot be cured, treatment options focus on the improvement of PD symptoms. In fact, evidence suggests that early PD intervention has the potential to slow down symptom progression and improve the general quality of life in the long term. However, the initial motor symptoms are usually very subtle and, as a result, patients seek medical assistance only when their condition has substantially deteriorated; thus, missing the opportunity for an improved clinical outcome. This situation highlights the need for accessible tools that can screen for early motor PD symptoms and alert individuals to act accordingly. Here we show that PD and its motor symptoms can unobtrusively be detected from the combination of accelerometer and touchscreen typing data that are passively captured during natural user-smartphone interaction. To this end, we introduce a deep learning framework that analyses such data to simultaneously predict tremor, fine-motor impairment and PD. In a validation dataset from 22 clinically-assessed subjects (8 Healthy Controls (HC)/14 PD patients with a total data contribution of 18.305 accelerometer and 2.922 typing sessions), the proposed approach achieved 0.86/0.93 sensitivity/specificity for the binary classification task of HC versus PD. Additional validation on data from 157 subjects (131 HC/26 PD with a total contribution of 76.528 accelerometer and 18.069 typing sessions) with self-reported health status (HC or PD), resulted in area under curve of 0.87, with sensitivity/specificity of 0.92/0.69 and 0.60/0.92 at the operating points of highest sensitivity or specificity, respectively. Our findings suggest that the proposed method can be used as a stepping stone towards the development of an accessible PD screening tool that will passively monitor the subject-smartphone interaction for signs of PD and which could be used to reduce the critical gap between disease onset and start of treatment.
format	article
author	Alexandros Papadopoulos Dimitrios Iakovakis Lisa Klingelhoefer Sevasti Bostantjopoulou K. Ray Chaudhuri Konstantinos Kyritsis Stelios Hadjidimitriou Vasileios Charisis Leontios J. Hadjileontiadis Anastasios Delopoulos
author_facet	Alexandros Papadopoulos Dimitrios Iakovakis Lisa Klingelhoefer Sevasti Bostantjopoulou K. Ray Chaudhuri Konstantinos Kyritsis Stelios Hadjidimitriou Vasileios Charisis Leontios J. Hadjileontiadis Anastasios Delopoulos
author_sort	Alexandros Papadopoulos
title	Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques
title_short	Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques
title_full	Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques
title_fullStr	Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques
title_full_unstemmed	Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques
title_sort	unobtrusive detection of parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/4e9104bc0664484dbf31156c7c675e0e
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Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques

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