Outcome Prediction of Bell’s Palsy by Kinect II
Journal Title: Biomedical Journal of Scientific & Technical Research (BJSTR) - Year 2019, Vol 14, Issue 5
Abstract
Purpose: Bell’s palsy is a facial paralysis resulting from the 7th cranial nerve lesion. The House-Brackmann facial nerve grading system is widely used to characterize the severity of an attack. Like other subjective clinical scales it has insufficient inter-rater agreement. Prediction value of House-Brackmann grading scale and distance measures of Kinect 2 images for outcome of unilateral Bell’s palsy was compared. Methods: Five mimic muscles were tested by voluntary contraction. Data was recorded by high definition face tracking mode of Kinect 2. Corresponding virtual markers on both sides of the face was identified during neutral facial expression by affine transformation. The proportions of trajectories of markers on the affected and unaffected side of the face indicate the degree of muscle disability. For prediction of patient’s outcome the House-Brackmann grading scale, Hausdorf and Euclidean distance in the first and second examination was used. The third examination after 6 months served to determine the outcome. Data Analysis: Binary support vector machine classifier with leave-one-out cross-validation was used for prediction of outcome. Results: House-Brackmann grading scale outperformed distance measures. Hausdorf distance had comparable prediction value while Euclidean distance had the lowest prediction value. Conclusion: Lower predictive value of distance measures may be due to low accuracy of the Kinect 2 depth sensor. With the rapid development of 3D scanners, greater accuracy of scanning and therefore better outcome prediction of patient’s with Bell’s palsy can be expected. Bells palsy is the most common acute mononeuropathy affecting a 7th cranial nerve. In some patients this leads to a permanent weakening of the muscles in one side of the face [1]. The grading system developed by House and Brackmann (HB) [2] ranges between I (normal) and VI (no movement). This and similar clinical scales allow only a very approximate assessment of the degree of muscle weakness. The presence of great interobserver variability of the subjective grading systems justify technological enhancements of objective classifications for facial nerve paresis. Holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components were used for automatically recognizing facial actions in sequences of images. Quantification of muscle weakness 3D scanners with Bell’s palsy was not to the best knowledge of the authors performed. The aim of this work is to design a simple outpatient system for monitoring asymmetry of muscle strength in patients with Bell’s palsy based on cheap 3D scanner Microsoft Kinect 2. MS Kinect’s 3D facial model is derived from the Candide3 model (https://www.icg.isy.liu.se/candide/). The Face Tracking SDK tries to fit a 3D mask to the users face. The group of patients with unilatera Bell’s palsy included 7 men and 8 woman (19-56 years). Each subject was examined up to 3rd day from first symptoms. The second examination was one week later and control examination after 6 months. The following examinations were performed: a) House-Brackmann scale b) Conduction studies and needle EMG c) Kinect II 3D facial mode recording The parameter for outcome prediction was the grade of weakness in the first and second examination. Mean score was in the first examination 4.27 ± 0.96, in the second one 3.00 ± 1.18 and in the last examination 1.67 ± 0.98
Authors and Affiliations
Filip Caisberger, Olřich Vyšata, Vojtěch Sedlák, Ondřej Dostál, Jakub Kopal, Aleš Procházka, Jiří Kuchyňka, Martin Vališ
Keywords
Related Articles
- EP ID EP594790
- DOI 10.26717/BJSTR.2019.14.002616
- Views 168
- Downloads 0
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