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. 2015 May;62(5):1383-1394.
doi: 10.1109/TBME.2015.2389149. Epub 2015 Jan 9.

Autonomous unobtrusive detection of mild cognitive impairment in older adults

Affiliations

Autonomous unobtrusive detection of mild cognitive impairment in older adults

Ahmad Akl et al. IEEE Trans Biomed Eng. 2015 May.

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.

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Figures

Fig. 1
Fig. 1
Trajectories of weekly measures pertaining to subject i in the database. Each asterisk represents a weekly measure. Features are extracted using a window of size , that slides one week at a time.
Fig. 2
Fig. 2
General overview of the cognitive status recognition process.
Fig. 3
Fig. 3
A layout of the sensing technologies that were installed in the homes of the participating subjects [28].
Fig. 4
Fig. 4
Example of a subject who scored 0 on CDR scale on all administered annual assessments.
Fig. 5
Fig. 5
Example of a subject who scored 0.5 on CDR scale on the 2nd-year assessment onward.
Fig. 6
Fig. 6
Age probability densities corresponding to the dataset of 68 homes.
Fig. 7
Fig. 7
Age probability densities corresponding to the dataset of 97 homes.
Fig. 8
Fig. 8
ROC Curves generated by using SVM and = 24 weeks.
Fig. 9
Fig. 9
Precision-Recall Curves generated by using SVM and = 24 weeks.
Fig. 10
Fig. 10
ROC Curves corresponding to using SVM for three different cases: dataset of 68 homes, dataset of 97 homes, and dataset of 97 homes + best features.
Fig. 11
Fig. 11
Precision-Recall Curves corresponding to using SVM for three different cases: dataset of 68 homes, dataset of 97 homes, and dataset of 97 homes + best features.

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