Vascular Dementia is often Clinically diagnosed once the effects of the disease are prevalent in a person's daily living routines. However, previous research has shown various behavioral and physiological changes linked to the development of Vascular Dementia, with these changes beginning to present earlier than clinical diagnosis is currently possible. In this review, works focused on these early signs of Vascular Dementia are highlighted. However, recognizing these changes is difficult. Many computational systems have been proposed for the evaluation these early signs of Vascular Dementia. The chosen works have largely focused on utilizing sensors systems or algorithmic evaluation can be incorporated into a person's environment to measure behavioral, and phycological metrics. This raw data can then be computationally analyzed to draw conclusions about the patterns of change surrounding the onset of Vascular Dementia. This compilation of works presents current a framework for investigating the various behavioral and physiological metrics as well as potential avenues for further investigating of sensor system and algorithmic design with the goal of enabling earlier Vascular Dementia detection.
|Original language||English (US)|
|Number of pages||10|
|Journal||International Journal of Advanced Computer Science and Applications|
|State||Published - 2019|
Bibliographical notePublisher Copyright:
© 2013 The Science and Information (SAI) Organization.
Copyright 2020 Elsevier B.V., All rights reserved.
- Algorithmic disease detection
- Artificial intelligence
- Machine learning
- Pattern recognition
- Vascular dementia
- Vascular dementia onset