Parkinson’s Disease Recognition Using Artificial Immune System
ABSTRACT
This work deals the application of the artificial immune system to discriminate between healthy and people with Parkinson’s disease (PWP). As the symptoms of Parkinson’s disease (PD) occur gradually and mostly targeting the elderly people for whom physical visits to the clinic are inconvenient and costly, telemonitoring of the disease using measurements of dysphonia (vocal features) has a vital role in its early diagnosis. Taking inspiration from natural immune systems, we try to grab useful properties such as automatic recognition, memorization and adaptation. The developed algorithms have as a base the algorithm of training bio inspired CLONCLAS. The results obtained are satisfactory and show a great reliability of the approach.
This work deals the application of the artificial immune system to discriminate between healthy and people with Parkinson’s disease (PWP). As the symptoms of Parkinson’s disease (PD) occur gradually and mostly targeting the elderly people for whom physical visits to the clinic are inconvenient and costly, telemonitoring of the disease using measurements of dysphonia (vocal features) has a vital role in its early diagnosis. Taking inspiration from natural immune systems, we try to grab useful properties such as automatic recognition, memorization and adaptation. The developed algorithms have as a base the algorithm of training bio inspired CLONCLAS. The results obtained are satisfactory and show a great reliability of the approach.
KEYWORDS
Parkinson’s Disease, Dysphonia Measures, Speech Analysis, Immune System, Clonal Selection Algorithm
Parkinson’s Disease, Dysphonia Measures, Speech Analysis, Immune System, Clonal Selection Algorithm
Cite this paper
nullB. Kihel and M. Benyettou, "Parkinson’s Disease Recognition Using Artificial Immune System," Journal of Software Engineering and Applications, Vol. 4 No. 7, 2011, pp. 391-395. doi: 10.4236/jsea.2011.47045.
nullB. Kihel and M. Benyettou, "Parkinson’s Disease Recognition Using Artificial Immune System," Journal of Software Engineering and Applications, Vol. 4 No. 7, 2011, pp. 391-395. doi: 10.4236/jsea.2011.47045.
References
[1] M. A. Little, et al., “Suitability of Dysphonia Measurments for Telemonitoring of Parkinson Disease,” IEEE Transactions on Biomedical Engineering, Vol. 56, No. 4, 2009, pp. 1015-1022. [2] K. Revett, et al., “Feature Selection in Parkinson’s Disease: A Rough Sets Approach,” Proceedings of the International Multiconference on Computer Science and Information Technology, Mragowo, 12-14 October 2009, pp. 425-428. [3] N. Singh, V. Pillay and Y. E. Choonara, “Advances in the Treatment of Parkinson’s Disease,” Progress in Neurobiology, Vol. 81, No. 1, 2007, pp. 29-44. doi:10.1016/j.pneurobio.2006.11.009 [4] J. A. White and S. M. Garrett, “Improved Pattern Recognition with Artificial Clonal Selection?” Proceedings of the 2nd International Conference on Artificial Immune Systems, Edinburgh, 1-3 September 2003, pp. 181-193. [5] A. H. Deneche, “Approche bio Inspirées Pour la Reconnaissance de Formes,” Thèse de Magistère, Université Mentouri de Constantine, Algeria, 2006. [6] A. Tsanas, et al., “Enhanced Classical Dysphonia Measures and Sparse Regression for Telemonitoring of Parkinson’s Disease Progression,” 2011. [7] http://people.maths.ox.ac.uk/tsanas/Preprints/ICASSP2010.pdf C. O. Sakar and O. Kursun, “Telediagnosis of Parkinson’s Disease Using Measurements of Dysphonia,” Journal of Medical Systems, Vol. 34, No. 4, 2009, pp. 591-599. [8] B. K. Kihel and M. Benyettou, “Identification Biométrique des Individus par Leurs Empreintes Digitales par l’AIS,” The 2009 IEEE International Symposium on Parallel and Distributed Proceeding with Applications, Chengdu and Jiuzhai Valley, 9-11 August 2009, p. 3. [9] N. Neggaz and A. Benyettou, “Les Algorithmes Evolutionnaires Appliqués à la Classification Phonétique,” Mémoire de Projet de Fin d’Etudes, Université des Sciences et de la Technologie d’Oran, 2004.
[1] M. A. Little, et al., “Suitability of Dysphonia Measurments for Telemonitoring of Parkinson Disease,” IEEE Transactions on Biomedical Engineering, Vol. 56, No. 4, 2009, pp. 1015-1022. [2] K. Revett, et al., “Feature Selection in Parkinson’s Disease: A Rough Sets Approach,” Proceedings of the International Multiconference on Computer Science and Information Technology, Mragowo, 12-14 October 2009, pp. 425-428. [3] N. Singh, V. Pillay and Y. E. Choonara, “Advances in the Treatment of Parkinson’s Disease,” Progress in Neurobiology, Vol. 81, No. 1, 2007, pp. 29-44. doi:10.1016/j.pneurobio.2006.11.009 [4] J. A. White and S. M. Garrett, “Improved Pattern Recognition with Artificial Clonal Selection?” Proceedings of the 2nd International Conference on Artificial Immune Systems, Edinburgh, 1-3 September 2003, pp. 181-193. [5] A. H. Deneche, “Approche bio Inspirées Pour la Reconnaissance de Formes,” Thèse de Magistère, Université Mentouri de Constantine, Algeria, 2006. [6] A. Tsanas, et al., “Enhanced Classical Dysphonia Measures and Sparse Regression for Telemonitoring of Parkinson’s Disease Progression,” 2011. [7] http://people.maths.ox.ac.uk/tsanas/Preprints/ICASSP2010.pdf C. O. Sakar and O. Kursun, “Telediagnosis of Parkinson’s Disease Using Measurements of Dysphonia,” Journal of Medical Systems, Vol. 34, No. 4, 2009, pp. 591-599. [8] B. K. Kihel and M. Benyettou, “Identification Biométrique des Individus par Leurs Empreintes Digitales par l’AIS,” The 2009 IEEE International Symposium on Parallel and Distributed Proceeding with Applications, Chengdu and Jiuzhai Valley, 9-11 August 2009, p. 3. [9] N. Neggaz and A. Benyettou, “Les Algorithmes Evolutionnaires Appliqués à la Classification Phonétique,” Mémoire de Projet de Fin d’Etudes, Université des Sciences et de la Technologie d’Oran, 2004.