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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2020-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-020-78418-8 |
| _version_ | 1818428181141520384 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T14:57:32Z |
| format | Article |
| id | doaj.art-4e9104bc0664484dbf31156c7c675e0e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T14:57:32Z |
| publishDate | 2020-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4e9104bc0664484dbf31156c7c675e0e2022-12-21T22:56:58ZengNature PortfolioScientific Reports2045-23222020-12-0110111310.1038/s41598-020-78418-8Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniquesAlexandros Papadopoulos0Dimitrios Iakovakis1Lisa Klingelhoefer2Sevasti Bostantjopoulou3K. Ray Chaudhuri4Konstantinos Kyritsis5Stelios Hadjidimitriou6Vasileios Charisis7Leontios J. Hadjileontiadis8Anastasios Delopoulos9Multimedia Understanding Group, Information Processing Laboratory, Department of Electrical and Computer Engineering, Aristotle University of ThessalonikiSignal Processing and Biomedical Technology Unit, Telecommunications Laboratory, Department of Electrical and Computer Engineering, Aristotle University of ThessalonikiDepartment of Neurology, Technical University of DresdenThird Neurological Clinic, Papanikolaou HospitalInternational Parkinson Excellence Research Centre, King’s College Hospital NHS Foundation TrustMultimedia Understanding Group, Information Processing Laboratory, Department of Electrical and Computer Engineering, Aristotle University of ThessalonikiSignal Processing and Biomedical Technology Unit, Telecommunications Laboratory, Department of Electrical and Computer Engineering, Aristotle University of ThessalonikiSignal Processing and Biomedical Technology Unit, Telecommunications Laboratory, Department of Electrical and Computer Engineering, Aristotle University of ThessalonikiSignal Processing and Biomedical Technology Unit, Telecommunications Laboratory, Department of Electrical and Computer Engineering, Aristotle University of ThessalonikiMultimedia Understanding Group, Information Processing Laboratory, Department of Electrical and Computer Engineering, Aristotle University of ThessalonikiAbstract 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.https://doi.org/10.1038/s41598-020-78418-8 |
| spellingShingle | 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 Scientific Reports |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s41598-020-78418-8 |
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