Profiling bell's palsy based on House-Brackmann score

Insu Song, Nguwi Yok Yen, John Vong, Joahchim Diederich, Peter Mackinlay Yellowlees

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this study, we propose to examine facial nerve palsy using Support Vector Machines (SVMs) and Emergent Self-Organizing Map (ESOM). This research seeks to analyze facial palsy domain using facial features and grade the degree of nerve damage according to House-Brackmann score. Traditional evaluation methods involve a medical doctor taking a thorough history of a patient and determines the onset of the paralysis, the rate of progression and etc. The most important step is to assess the degree of voluntary movement present and document the grade of facial paralysis using House-Brackmann score. The significance of this work is that we attempt to apprehend this grading using semi-supervised learning with the aim of automating this grading process. The value of this research stems from the fact that there is a lack of literature seen in this area. The use of automated grading system greatly reduces assessment time and increases consistency because references of all palsy images are stored to provide references and comparison. The proposed automated diagnostics methods are computationally efficient making them ideal for remote assessment of facial palsy, profiling of a large number of facial images captured using mobile phones and digital cameras.

Original languageEnglish (US)
Title of host publicationProceedings of the 2013 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, CICARE 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Pages1-6
Number of pages6
DOIs
StatePublished - 2013
Event2013 1st IEEE Symposium on Computational Intelligence in Healthcare and e-Health, CICARE 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 - Singapore, Singapore
Duration: Apr 16 2013Apr 19 2013

Other

Other2013 1st IEEE Symposium on Computational Intelligence in Healthcare and e-Health, CICARE 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
CountrySingapore
CitySingapore
Period4/16/134/19/13

Keywords

  • eHealth
  • face
  • Facial
  • Health Informatics
  • Medical Data Analysis
  • palsy
  • SOM
  • SVM

ASJC Scopus subject areas

  • Artificial Intelligence
  • Health Informatics
  • Health Information Management

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  • Cite this

    Song, I., Yen, N. Y., Vong, J., Diederich, J., & Yellowlees, P. M. (2013). Profiling bell's palsy based on House-Brackmann score. In Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, CICARE 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 (pp. 1-6). [6583060] https://doi.org/10.1109/CICARE.2013.6583060