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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Vydáno v:	IEEE transactions on biomedical engineering Ročník 62; číslo 5; s. 1383 - 1394
Hlavní autoři:	Akl, Ahmad, Taati, Babak, Mihailidis, Alex
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.05.2015
Témata:	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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Abstract	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.
AbstractList	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.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. 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 3 years, a number of measures associated with the subjects' walking speeds 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 work is of great significance since it can potentially assist in the early detection of 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 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.
Author	Taati, Babak Akl, Ahmad Mihailidis, Alex
Author_xml	– sequence: 1 givenname: Ahmad surname: Akl fullname: Akl, Ahmad email: ahmad.akl@utoronto.ca organization: Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada – sequence: 2 givenname: Babak surname: Taati fullname: Taati, Babak organization: Toronto Rehabilitation Institute – sequence: 3 givenname: Alex surname: Mihailidis fullname: Mihailidis, Alex organization: Institute of Biomaterials and Biomedical Engineering, University of Toronto
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Snippet	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 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...
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SubjectTerms	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
Title	Autonomous Unobtrusive Detection of Mild Cognitive Impairment in Older Adults
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