Eigenbehaviour as an Indicator of Cognitive Abilities
With growing usage of machine learning algorithms and big data in health applications, digital biomarkers have become an important key feature to ensure the success of those applications. In this paper, we focus on one important use-case, the long-term continuous monitoring of the cognitive ability...
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Zusammenfassung: | With growing usage of machine learning algorithms and big data in health
applications, digital biomarkers have become an important key feature to ensure
the success of those applications. In this paper, we focus on one important
use-case, the long-term continuous monitoring of the cognitive ability of older
adults. The cognitive ability is a factor both for long-term monitoring of
people living alone as well as an outcome in clinical studies. In this work, we
propose a new digital biomarker for cognitive abilities based on location
eigenbehaviour obtained from contactless ambient sensors. Indoor location
information obtained from passive infrared sensors is used to build a location
matrix covering several weeks of measurement. Based on the eigenvectors of this
matrix, the reconstruction error is calculated for various numbers of used
eigenvectors. The reconstruction error is used to predict cognitive ability
scores collected at baseline, using linear regression. Additionally,
classification of normal versus pathological cognition level is performed using
a support-vector-machine. Prediction performance is strong for high levels of
cognitive ability, but grows weaker for low levels of cognitive ability.
Classification into normal versus pathological cognitive ability level reaches
high accuracy with a AUC = 0.94. Due to the unobtrusive method of measurement
based on contactless ambient sensors, this digital biomarker of cognitive
ability is easily obtainable. The usage of the reconstruction error is a strong
digital biomarker for the binary classification and, to a lesser extent, for
more detailed prediction of interindividual differences in cognition. |
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DOI: | 10.48550/arxiv.2110.09525 |