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Eigenbehaviour as an Indicator of Cognitive Abilities

With growing use of machine learning algorithms and big data in health applications, digital measures, such as digital biomarkers, have become highly relevant in digital health. In this paper, we focus on one important use case, the long-term continuous monitoring of cognitive ability in older adult...

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Autores principales: Botros, Angela A., Schuetz, Narayan, Röcke, Christina, Weibel, Robert, Martin, Mike, Müri, René M., Nef, Tobias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003060/
https://www.ncbi.nlm.nih.gov/pubmed/35408381
http://dx.doi.org/10.3390/s22072769
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author Botros, Angela A.
Schuetz, Narayan
Röcke, Christina
Weibel, Robert
Martin, Mike
Müri, René M.
Nef, Tobias
author_facet Botros, Angela A.
Schuetz, Narayan
Röcke, Christina
Weibel, Robert
Martin, Mike
Müri, René M.
Nef, Tobias
author_sort Botros, Angela A.
collection PubMed
description With growing use of machine learning algorithms and big data in health applications, digital measures, such as digital biomarkers, have become highly relevant in digital health. In this paper, we focus on one important use case, the long-term continuous monitoring of cognitive ability in older adults. Cognitive ability is a factor both for long-term monitoring of people living alone as well as a relevant outcome in clinical studies. In this work, we propose a new potential 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 in turn 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 and older adults with mild cognitive impairment, using age and the reconstruction error, shows high discriminative performance with an ROC AUC of 0.94. This is an improvement of 0.08 as compared with a classification with age only. Due to the unobtrusive method of measurement, this potential digital biomarker of cognitive ability can be obtained entirely unobtrusively—it does not impose any patient burden. In conclusion, the usage of the reconstruction error is a strong potential digital biomarker for binary classification and, to a lesser extent, for more detailed prediction of inter-individual differences in cognition.
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spelling pubmed-90030602022-04-13 Eigenbehaviour as an Indicator of Cognitive Abilities Botros, Angela A. Schuetz, Narayan Röcke, Christina Weibel, Robert Martin, Mike Müri, René M. Nef, Tobias Sensors (Basel) Article With growing use of machine learning algorithms and big data in health applications, digital measures, such as digital biomarkers, have become highly relevant in digital health. In this paper, we focus on one important use case, the long-term continuous monitoring of cognitive ability in older adults. Cognitive ability is a factor both for long-term monitoring of people living alone as well as a relevant outcome in clinical studies. In this work, we propose a new potential 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 in turn 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 and older adults with mild cognitive impairment, using age and the reconstruction error, shows high discriminative performance with an ROC AUC of 0.94. This is an improvement of 0.08 as compared with a classification with age only. Due to the unobtrusive method of measurement, this potential digital biomarker of cognitive ability can be obtained entirely unobtrusively—it does not impose any patient burden. In conclusion, the usage of the reconstruction error is a strong potential digital biomarker for binary classification and, to a lesser extent, for more detailed prediction of inter-individual differences in cognition. MDPI 2022-04-04 /pmc/articles/PMC9003060/ /pubmed/35408381 http://dx.doi.org/10.3390/s22072769 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Botros, Angela A.
Schuetz, Narayan
Röcke, Christina
Weibel, Robert
Martin, Mike
Müri, René M.
Nef, Tobias
Eigenbehaviour as an Indicator of Cognitive Abilities
title Eigenbehaviour as an Indicator of Cognitive Abilities
title_full Eigenbehaviour as an Indicator of Cognitive Abilities
title_fullStr Eigenbehaviour as an Indicator of Cognitive Abilities
title_full_unstemmed Eigenbehaviour as an Indicator of Cognitive Abilities
title_short Eigenbehaviour as an Indicator of Cognitive Abilities
title_sort eigenbehaviour as an indicator of cognitive abilities
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003060/
https://www.ncbi.nlm.nih.gov/pubmed/35408381
http://dx.doi.org/10.3390/s22072769
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