Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

4 Issues per year

Open Access
Online
ISSN
2083-2567
See all formats and pricing
More options …

Profiling Bell’s Palsy based on House-Brackmann Score

Insu Song / John Vong / Nguwi Yok Yen / Joahchim Diederich / Peter Yellowlees
Published Online: 2014-12-30 | DOI: https://doi.org/10.2478/jaiscr-2014-0004

Abstract

In this study, we propose to diagnose 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 based on the House-Brackmann score. Traditional diagnostic approaches involve a medical doctor recording a thorough history of a patient and determining the onset of paralysis, rate of progression and so on. The most important step is to assess the degree of voluntary movement of the facial nerves and document the grade of facial paralysis using House- Brackmann score. The significance of the work is the attempt to understand the diagnosis and grading processes using semi-supervised learning with the aim of automating the process. The value of the research is in identifying and documenting the limited literature seen in this area. The use of automated diagnosis and grading greatly reduces the duration of medical examination and increases the consistency, because many palsy images are stored to provide benchmark references for comparative purposes. The proposed automated diagnosis and grading are computationally efficient. This automated process makes it ideal for remote diagnosis and examination of facial palsy. The profiling of a large number of facial images are captured using mobile phones and digital cameras.

References

  • [1] I. Song, D. Dillon, T. J. Goh, and M. Sung, ”A Health Social Network Recommender System,” in Agents in Principle, Agents in Practice - 14th International Conference, PRIMA 2011, 2011, pp. 361-372.Google Scholar

  • [2] DSM-IV-TR, Diagnostic and Statistical Manual of Mental Disorders,Text Revision, 4th ed.: American Psychiatric Association, 2000.Google Scholar

  • [3] I. Song and N. V. Marsh, ”Anonymous Indexing of Health Conditions for a Similarity Measure,” Information Technology in Biomedicine, IEEE Transactions on, vol. 16, pp. 737-744, 2012.Google Scholar

  • [4] C. A. Kasse, R. G. Ferri, E. Y. C. Vietler, F. D. Leonhardt, J. R. G. Testa, and O. L. M. Cruz, ”Clinical data and prognosis in 1521 cases of Bell’s palsy,” International Congress Series, vol. 1240, pp. 641-647, 2003.Google Scholar

  • [5] MedicineNet.Inc.(1996).MedicineNet.com. Available: http://www.medterms.com/script/main/art.asp?articlekey=6556Google Scholar

  • [6] S. Ghali, A. MacQuillan, and A. O. Grobbelaar, ”Reanimation of the middle and lower face in facial paralysis: Review of the literature and personal approach,” Journal of Plastic, Reconstructive & Aesthetic Surgery, vol. In Press, Corrected Proof, 2010.Web of ScienceGoogle Scholar

  • [7] A.D.A.M. Medical Encyclopedia.( 2010). Bell’s palsy. Available: http://www.ncbi.nlm.nih.gov/pubmedhealth/PMH0001777/Google Scholar

  • [8] J. P. Browder, ”Facial paralysis in children,” Journal of Ear Nose Throat vol. 57, pp. 278-83, 1978.Google Scholar

  • [9] J. W. House and D. E. Brackmann, ”Facial nerve grading system,” Otolaryngol Head Neck Surg, vol. 93 pp. 146-147, 1985.Web of ScienceGoogle Scholar

  • [10] J. T. Vrabec, D. D. Backous, H. R. Djalilian, P. W. Gidley, J. P. Leonetti, S. J. Marzo, et al., ”Facial Nerve Grading System 2.0,” Otolaryngology - Head and Neck Surgery, vol. 140, pp. 445-450, 2009.Web of ScienceGoogle Scholar

  • [11] C. Chang and C. Lin, {LIBSVM}: a library for support vector machines, 2001.Google Scholar

  • [12] A. Ultsch and M. F., ”ESOM-Maps: tools for clustering, visualization, and classification with Emergent SOM,” University of Marburg Dept. of Mathematics and Computer Science, Germany 2005.Google Scholar

  • [13] B. Lei, I. Song, and S. A. Rahman, ”Optimal watermarking scheme for breath sound,” in Neural Networks (IJCNN), The 2012 International Joint Conference on, 2012, pp. 1-6.Google Scholar

  • [14] J. Vong, J. Fang, and I. Song, ”Delivering financial services through mobile phone technology: a pilot study on impact of mobile money service on micro-entrepreneurs in rural Cambodia,” International Journal of Information Systems and Change Management, vol. 6, pp. 177-186, 2012.Google Scholar

  • [15] D. M. Angaran, ”Telemedicine and telepharmacy: Current status and future implications,” American Journal of Health-System Pharmacy, vol. 56, pp. 1405-1426, Jul 1999.Google Scholar

  • [16] D. M. Hilty, J. S. Luo, C. Morache, D. A. Marcelo, and T. S. Nesbitt, ”Telepsychiatry - An overview for psychiatrists,” Cns Drugs, vol. 16, pp. 527-548, 2002.PubMedCrossrefGoogle Scholar

  • [17] D. M. Hilty, S. L. Marks, D. Urness, P. M. Yellowlees, and T. S. Nesbitt, ”Clinical and educational telepsychiatry applications: A review,” Canadian Journal of Psychiatry-Revue Canadienne De Psychiatrie, vol. 49, pp. 12-23, Jan 2004.Google Scholar

  • [18] P. Yellowlees, M. M. Burke, S. L. Marks, D. M. Hilty, and J. H. Shore, ”Emergency telepsychiatry,” Journal of Telemedicine and Telecare, vol. 14, pp. 277-281, 2008CrossrefGoogle Scholar

  • [19] R. Harrison, W. Clayton, and P. Wallace, ”Can telemedicine be used to improve communication between primary and secondary care?,” British Medical Journal, vol. 313, pp. 1377-1381, 1996Google Scholar

  • [20] C. A. Frantzidis, C. Bratsas, M. A. Klados, E. Konstantinidis, C. D. Lithari, A. B. Vivas, et al., ”On the Classification of Emotional Biosignals Evoked While Viewing Affective Pictures: An Integrated Data-Mining-Based Approach for Healthcare Applications,” Information Technology in Biomedicine, IEEE Transactions on, vol. 14, pp. 309-318, 2010Google Scholar

  • [21] L. Tang, X. Zhou, Z. Yu, Y. Liang, D. Zhang, and H. Ni, ”MHS: A multimedia system for improving medication adherence in elderly care,” IEEE Systems Journal, vol. 5, pp. 506-517, 2011Web of ScienceGoogle Scholar

  • [22] B. Elvevag, P. W. Foltz, M. Rosenstein, and L. E. DeLisi, ”An automated method to analyze language use in patients with schizophrenia and their first-degree relatives,” Journal of Neurolinguistics, vol. 23, pp. 270-284, 2009.Web of ScienceGoogle Scholar

  • [23] AD Tilaka, J Diederich, I Song, and A. Teoh, ”Automated Method for Diagnosing Speech and Language Dysfunction in Schizophrenia,” in Mental Health Informatics, I. S. Margaret Lech, Peter Yellowlees, and Joachim Diederich, Ed., ed: Springer, 2013.Google Scholar

  • [24] L. S. A. Low, N. C. Maddage, M. Lech, L. B. Sheeber, and N. B. Allen, ”Detection of clinical depression in adolescents’ speech during family interactions,” IEEE Transactions on Biomedical Engineering, vol. 58, pp. 574-586, 2011.CrossrefWeb of ScienceGoogle Scholar

  • [25] B. Lei, I. Song, and S. A. Rahman, ”Robust and secure watermarking scheme for breath sound,” Journal of Systems and Software, 2013.Google Scholar

  • [26] R. Cowie, E. Douglas-Cowie, N. Tsapatsoulis, G. Votsis, S. Kollias, W. Fellenz, et al., ”Emotion recognition in human-computer interaction,” IEEE Signal Processing Magazine, vol. 18, pp. 32-80, 2001.CrossrefGoogle Scholar

  • [27] Y. Yacoob and L. S. Devis, ”Recognizing human facial expressions from long image sequences using optical flow,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 636-642, 1996.Google Scholar

  • [28] T. Otsuka and J. Ohya, ”Recognition of facial expressions using HMM with continuous output probabilities,” 1996, pp. 323-328.Google Scholar

  • [29] M. Pantic and L. . M. Rothkrantz, ”Automatic analysis of facial expressions: The state of the art,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 1424-1445, 2000.Google Scholar

  • [30] P. Ekman, E. L. Rosenberg, and M. Heller, ”What the face reveals. Basic and applied studies of spontaneous expression using the facial action coding system (FACS),” Psychotherapies, vol. 18, pp. 179-180, 1998.Google Scholar

  • [31] M. F. Valstar and M. Pantic, ”Fully Automatic Recognition of the Temporal Phases of Facial Actions,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2011.Google Scholar

  • [32] R. T. Stone and C. S. Wei, ”Exploring the linkage between facial expression and mental workload for arithmetic tasks,” 2011, pp. 616-619.Google Scholar

  • [33] M. Nambu, K. Nakajima, M. Noshiro, and T. Tamura, ”An algorithm for the automatic detection of health conditions,” Engineering in Medicine and Biology Magazine, IEEE, vol. 24, pp. 38-42, 2005.Google Scholar

  • [34] V. Vapnik and A. Lerner, ”Pattern Recognition using Generalized Portrait Method,” Automation and Remote Control, vol. 24, 1963.Google Scholar

  • [35] B. E. Boser, I. M. Guyon, and V. N. Vapnik, ”A training algorithm for optimal margin classifiers,” in Proc. Fifth Annual Workshop on Computational Learning Theory, 1992, pp. 144-152.Google Scholar

  • [36] C. Cortes and V. Vapnik, ”Support-vector networks,” Machine Learning, vol. 20, pp. 273-297, 1995.CrossrefGoogle Scholar

  • [37] C. J. C. Burges, ”A tutorial on support vector machines for pattern recognition,” Data mining and knowledge discovery, vol. 2, pp. 121-167, 1998.Google Scholar

  • [38] T. Joachims, ”Text categorization with support vector machines: Learning with many relevant features,” Machine learning: ECML-98, pp. 137-142, 1998.Google Scholar

  • [39] J. D. M. Rennie, ”Improving multi-class text classification with naive Bayes,” Massachusetts Institute of Technology, 2001.Google Scholar

  • [40] T. Kohonen, ”The self-organizing map,” Proceedings of the IEEE, vol. 78, pp. 1464-1480, 1990.CrossrefGoogle Scholar

  • [41] A. Ultsch, ”Self-organizing neural networks for visualization and classification,” 1993.Google Scholar

  • [42] A. Ultsch, ”Maps for the visualization of highdimensional data spaces,” in Proc. Workshop on Self organizing Maps, 2003, pp. 225-230.Google Scholar

  • [43] A. Ultsch, U*-matrix: a tool to visualize clusters in high dimensional data: Fachbereich Mathematik und Informatik, 2003.Google Scholar

  • [44] A. Ultsch, ”Clustering with SOM: U*C,” presented at the WSOM, 2005.Google Scholar

  • [45] J.-J. Wong and S.-Y. Cho, ”A Support Vector Reduced Multivariate Polynomial Model for Face Emotion Recognition,” IEEE Transactions on Information Forensics and Security, 2007.Google Scholar

  • [46] P. Viola and M. Jones, ”Rapid object detection using a boosted cascade of simple features,” in Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, 2001, pp. I-511-I-518 vol. 1.Google Scholar

  • [47] Hamming and R. W., ”Error detecting and error correcting codes,” Bell System Technical Journal, vol. 29, pp. 147-160, 1950.Google Scholar

  • [48] M. Maloof, ”Learning when data sets are imbalanced and when costs are unequal and unknown,” in Proceedings of the ICML,Workshop on Learning from Imbalanced Data Sets, 2003.Google Scholar

  • [49] R. Barandela, J. S. Snchez, V. Garca, and E. Rangel, ”Strategies for learning in class imbalance problems,” Pattern Recognition, vol. 36, pp. 849-851, 2003.CrossrefGoogle Scholar

About the article

Published Online: 2014-12-30

Published in Print: 2013-01-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.2478/jaiscr-2014-0004.

Export Citation

© 2015. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

Comments (0)

Please log in or register to comment.
Log in