Early-stage parkinson's disease detection based on action unit derivatives
Résumé
Background: Hypomimia is a symptom of Parkinson’s disease (PD), characterizedby a decrease in facial movements and loss of face emotional expressions. This studyaims to detect hypomimia in participants with early-stage PD based on facial actionunits (AUs). Methods: A total of 299 video recordings were included, consisting of 208PD subjects and 91 healthy controls (HC), asked to perform fast syllable repetitions.To distinguish typical facial muscle movements from PD subjects associated withhypomimia, we compute the AUs derivatives. Global features were extracted basedon the AUs intensities and their derivatives, and XGBoost was used to classify PD vs.HC. Results: We obtain classification scores up to 73.00% in terms of balanced accuracy(BA) and an area under the curve (AUC) of 78.38% at video visit level. These resultsare promising for detecting hypomimia at an early stage of PD, and this work couldpotentially allow for continuous monitoring of hypomimia outside of hospitals throughtelemedicine.
Domaines
Informatique [cs]| Origine | Fichiers produits par l'(les) auteur(s) |
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