Autonomous Unobtrusive Detection of Mild Cognitive Impairment in Older Adults

The current diagnosis process of dementia is resulting in a high percentage of cases with delayed detection. To address this problem, in this paper, we explore the feasibility of autonomously detecting mild cognitive impairment (MCI) in the older adult population. We implement a signal processing ap...

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Veröffentlicht in:IEEE transactions on biomedical engineering Jg. 62; H. 5; S. 1383 - 1394
Hauptverfasser: Akl, Ahmad, Taati, Babak, Mihailidis, Alex
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.05.2015
Schlagworte:
Aged
Aged, 80 and over
Algorithms
Artificial Intelligence
Biomedical measurement
Cognitive Dysfunction - diagnosis
Cognitive Dysfunction - physiopathology
Dementia
Feature extraction
Female
Home Activity
Humans
Legged locomotion
Machine Learning
Male
Mild Cognitive Impairment
Monitoring
Older Population
Remote Sensing Technology - instrumentation
Remote Sensing Technology - methods
ROC Curve
Sensors
Signal Processing
Signal Processing, Computer-Assisted
Smart Systems
Support Vector Machine
Unobtrusive Sensing Technologies
Vectors
Walking Speed
older population
Home activity
unobtrusive sensing technologies
signal processing
mild cognitive impairment (MCI)
smart systems
machine learning
walking speed
ISSN:0018-9294, 1558-2531, 1558-2531
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Zusammenfassung:The current diagnosis process of dementia is resulting in a high percentage of cases with delayed detection. To address this problem, in this paper, we explore the feasibility of autonomously detecting mild cognitive impairment (MCI) in the older adult population. We implement a signal processing approach equipped with a machine learning paradigm to process and analyze real-world data acquired using home-based unobtrusive sensing technologies. Using the sensor and clinical data pertaining to 97 subjects, acquired over an average period of three years, a number of measures associated with the subjects' walking speed and general activity in the home were calculated. Different time spans of these measures were used to generate feature vectors to train and test two machine learning algorithms namely support vector machines and random forests. We were able to autonomously detect MCI in older adults with an area under the ROC curve of 0.97 and an area under the precision-recall curve of 0.93 using a time window of 24 weeks. This study is of great significance since it can potentially assist in the early detection of cognitive impairment in older adults.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2015.2389149