Predicting MMSE Score from Finger-Tapping Measurement

Dementia is a leading cause of diseases for the elderly. Early diagnosis is very important for the elderly living with dementias. In this paper, we propose a method for dementia diagnosis by predicting MMSE score from finger-tapping measurement with machine learning pipeline. Based on measurement of...

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Bibliographic Details
Published in:arXiv.org
Main Author: Ma, Jian
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 16.11.2021
Subjects:
Biomarkers
Dementia
Diagnosis
Entropy
Machine learning
Older people
Prediction models
Predictions
ISSN:2331-8422
Online Access:Get full text
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Summary:Dementia is a leading cause of diseases for the elderly. Early diagnosis is very important for the elderly living with dementias. In this paper, we propose a method for dementia diagnosis by predicting MMSE score from finger-tapping measurement with machine learning pipeline. Based on measurement of finger tapping movement, the pipeline is first to select finger-tapping attributes with copula entropy and then to predict MMSE score from the selected attributes with predictive models. Experiments on real world data show that the predictive models such developed present good prediction performance. As a byproduct, the associations between certain finger-tapping attributes ('Number of taps', 'Average of intervals', and 'Frequency of taps' of both hands of bimanual in-phase task) and MMSE score are discovered with copula entropy, which may be interpreted as the biological relationship between cognitive ability and motor ability and therefore makes the predictive models explainable. The selected finger-tapping attributes can be considered as dementia biomarkers.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.2004.08730