Abstract
Strokes are a cause of serious long-term disability and create an immense burden on healthcare. Among the sea of mobile applications for health, some target stroke patients, and most require active user cooperation. Our proposed application, collects data, without user intervention. We apply data mining methods to create personal feedback to the patient or doctor. We provide a survey of applications for mobile or wearables, specifically for stroke. We also survey papers that apply data mining to stroke. In addition to the survey, we present a feasibility study on using speech for classification of stroke patients. We created a new data set of unstructured speech recordings, increasing applicability. We present experimental results on classification of stroke patients. Our study provides promising insight to detecting stroke patients using a mobile application without requiring active user participation.
Original language | English |
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Title of host publication | ICT4AWE 2019 - Proceedings of the 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health |
Editors | Martina Ziefle, Leszek Maciaszek, Leszek Maciaszek |
Publisher | SciTePress |
Pages | 159-166 |
Number of pages | 8 |
ISBN (Electronic) | 9789897583681 |
DOIs | |
State | Published - 2019 |
Externally published | Yes |
Event | 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2019 - Heraklion, Crete, Greece Duration: 2 May 2019 → 4 May 2019 |
Publication series
Name | ICT4AWE 2019 - Proceedings of the 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health |
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Conference
Conference | 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2019 |
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Country/Territory | Greece |
City | Heraklion, Crete |
Period | 2/05/19 → 4/05/19 |
Bibliographical note
Publisher Copyright:Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
Keywords
- Cardiovascular
- Data Mining
- Digital Health
- M-Health
- Machine Learning
- Mobile
- Speech
- Stroke