Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury

Our goal is to explore quantitative motor features in critically ill patients with severe brain injury (SBI). We hypothesized that computational decoding of these features would yield information on underlying neurological states and outcomes. Using wearable microsensors placed on all extremities, w...

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Bibliographic Details
Published in:	Scientific reports Vol. 11; no. 1; pp. 23654 - 17
Main Authors:	Bhattacharyay, Shubhayu, Rattray, John, Wang, Matthew, Dziedzic, Peter H., Calvillo, Eusebia, Kim, Han B., Joshi, Eshan, Kudela, Pawel, Etienne-Cummings, Ralph, Stevens, Robert D.
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 08.12.2021 Nature Publishing Group Nature Portfolio
Subjects:	631/114/2415 639/166/985 639/705/1042 692/617/375/1345 692/617/375/1370 692/617/375/380 692/617/375/534 692/699/375/1345 692/699/375/1370 692/699/375/380 692/699/375/534 Accelerometry Aged Brain Injuries - classification Brain Injuries - pathology Brain injury Computational neuroscience Critical Illness Extremities Female Glasgow Outcome Scale Humanities and Social Sciences Humans Male Middle Aged Motor activity multidisciplinary Pilot Projects Prediction models Science Science (multidisciplinary) Severity of Illness Index Traumatic brain injury
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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