IEEE J Biomed Health Inform - Actigraphy-based scratch detection using logistic regression.

Tópicos

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Resumo

Incessant scratching as a result of diseases such as atopic dermatitis causes skin break down, poor sleep quality, and reduced quality of life for affected individuals. In order to develop more effective therapies, there is a need for objective measures to detect scratching. Wrist actigraphy, which detects wrist movements over time using micro-accelerometers, has shown great promise in detecting scratch because it is lightweight, usable in the home environment, can record longitudinally, and does not require any wires. However, current actigraphy-based scratch-detection methods are limited in their ability to discriminate scratch from other nighttime activities. Our previous work demonstrated the separability of scratch from both walking and restless sleep using a clustering technique which employed four features derived from the actigraphic data: number of accelerations above 0.01 gs, epoch variance, peak frequency, and autocorrelation value at one lag. In this paper, we extended these results by employing these same features as independent variables in a logistic regression model. This allows us to directly estimate the conditional probability of scratching for each epoch. Our approach outperforms competing actigraphy-based approaches and has both high sensitivity (0.96) and specificity (0.92) for identifying scratch as validated on experimental data collected from 12 healthy subjects. The model must still be fully validated on clinical data, but shows promise for applications to clinical trials and longitudinal studies of scratch.

Resumo Limpo

incess scratch result diseas atop dermat caus skin break poor sleep qualiti reduc qualiti life affect individu order develop effect therapi need object measur detect scratch wrist actigraphi detect wrist movement time use microacceleromet shown great promis detect scratch lightweight usabl home environ can record longitudin requir wire howev current actigraphybas scratchdetect method limit abil discrimin scratch nighttim activ previous work demonstr separ scratch walk restless sleep use cluster techniqu employ four featur deriv actigraph data number acceler gs epoch varianc peak frequenc autocorrel valu one lag paper extend result employ featur independ variabl logist regress model allow us direct estim condit probabl scratch epoch approach outperform compet actigraphybas approach high sensit specif identifi scratch valid experiment data collect healthi subject model must still fulli valid clinic data show promis applic clinic trial longitudin studi scratch

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