Title: Posterior probability profiles for the automated assessment of the recovery of patients with stroke from activity of daily living tasks
Authors: Van Dijck, Gert ×
Van Vaerenbergh, Jozef
Van Hulle, Marc #
Issue Date: Jul-2009
Publisher: Burgverlag
Series Title: Artificial Intelligence in Medicine vol:46 issue:3 pages:233-249
Article number: ARTMED1061
Abstract: Objective: Assessing recovery after stroke has been so far a time consuming procedure
in which trained clinicians are required. A demand for automated assessment
techniques arises due to the increasing number of patients with stroke and the
continuous growth of new treatment options. In this study, we investigate the
applicability of isometric force and torque measurements in activity of daily living
tasks to assess the functional recovery after stroke in an automated way.
Methods and materials: A new hybrid filter-wrapper feature subset technology was
developed for a newmechatronic platform with the aim to identify themost important
features and sensors that can distinguish normal controls frompatients with stroke.We
compared 3 different classification algorithms to make the distinction: k-nearest
neighbors, kernel density estimation and least-squares support vector machines. Based
on isometric force and torquemeasurements obtained from16 patientswith a first-ever
ischemic or haemorrhagic stroke within the middle cerebral artery territory, we
computed for each subject the probability to belong to the class of normal subjects.
These probabilities were computed during a period of 6 months post-stroke to quantify
the level of recovery during this period. The posterior probabilities were validated by
means of a correlation study with the Lindmark modified Fugl-Meyer assessment.
Results: Patients with stroke and normal controls could be distinguished with an
accuracy of 98.25% by means of kernel density estimation. The posterior probability
profiles had a correlation of 76.6% and 80.29% with the global score of the Lindmark
modified Fugl-Meyer scale and ‘part A’, the upper extremity subscore, respectively.
This degree of correlation was as high as obtained with supervised scoring techniques
such as the Barthel index. Conclusion: This study shows that the assessment of recovery after stroke can be
automated by means of posterior probability profiles due to their high correlation with
the Fugl-Meyer assessment. The posterior probability profiles confirm the importance
of a recovery within the first weeks after stroke to obtain a higher recovery plateau
compared to later changes in recovery.
ISSN: 0933-3657
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Laboratory for Neuro- and Psychofysiology
Research Group Neurophysiology
× corresponding author
# (joint) last author

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