Detecting the effect of Alzheimer's disease on everyday motion behavior

Abstract

Background: Early detection of behavioral changes in Alzheimer's disease (AD) would help the design and implementation of specific interventions.

Objective: The target of our investigation was to establish a correlation between diagnosis and unconstrained motion behavior in subjects without major clinical behavior impairments.

Method: We studied everyday motion behavior in 23 dyads with one partner suffering from AD dementia and one cognitively healthy partner in the subjects' home, employing ankle-mounted three-axes accelerometric sensors. We determined frequency features obtained from the signal envelopes computed by an envelope detector for the carrier band 0.5 Hz to 5 Hz. Based on these features, we employed quadratic discriminant analysis for building models discriminating between AD patients and healthy controls.

Results: After leave-one-out cross-validation, the classification accuracy of motion features reached 91% and was superior to the classification accuracy based on the Cohen-Mansfield Agitation Inventory (CMAI). Motion features were significantly correlated with MMSE and CMAI scores.

Conclusion: Our findings suggest that changes of everyday behavior are detectable in accelerometric behavior protocols even in the absence of major clinical behavioral impairments in AD.

Keywords: Actigraphy; Alzheimer's disease; Cohen-Mansfield Agitation Inventory; Fourier analysis; chronobiology phenomena; circadian rhythm disorders; discriminant analysis; linear models; principal component analysis; psychomotor agitation.