Detection of Parkinson’s Disease Using Fuzzy Inference System

Parkinson’s disease is a degenerative neurological disorder which severely affects the ability to move, speak and behave. Parkinsonism is clinically diagnosed but the laboratory confirmation is hard to obtain and needs very sophisticated investigations with high economic burden. In this work a fuzzy based approach is adapted for efficient and proper detection of Parkinson’s disease using biomedical measurements of voice which is cheap and cost effective. The proposed system can also be utilised as a supplementary test to confirm the clinical diagnosis of Parkinson’s disease in a remote place using the telephony system to track the voice signal. Moreover, it can be used as a guide for the treatment of Parkinson’s disease as it gives a quantitative measure to signify the extent of the disease. The model is constructed using a Sugeno-Takagi Fuzzy Inference System (FIS) based on Fuzzy C-Means clustering. The result shows that the detection accuracy of the system is up to 96-97 percent with a reasonable efficiency. The study also compares the results with the Subtractive Clustering based Fuzzy Inference System. The accuracy of the Fuzzy C-Means based Fuzzy Inference System is found to be higher than the other one.

[1]  Max A. Little,et al.  Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease , 2012, IEEE Transactions on Biomedical Engineering.

[2]  Danial Taheri Far,et al.  Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine , 2014 .

[3]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[4]  B. Murdoch,et al.  Communication Impairments in Parkinson's Disease , 2011, Parkinson's disease.

[5]  K. Tjaden.  Speech and Swallowing in Parkinson's Disease , 2008, Topics in geriatric rehabilitation.

[6]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[7]  L. Ramig,et al.  Current Perspectives on the Lee Silverman Voice Treatment (LSVT) for Individuals With Idiopathic Parkinson Disease , 2002 .

[8]  Shimon Sapir,et al.  Multiple factors are involved in the dysarthria associated with Parkinson's disease: a review with implications for clinical practice and research. , 2014, Journal of speech, language, and hearing research : JSLHR.

[9]  Max A. Little,et al.  Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection , 2007, Biomedical engineering online.

[10]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[11]  S. Murugavalli,et al.  A Survey on Character Recognitions in Tamil Scripts using OCR , 2012 .

[12]  Mohammad Khalilia,et al.  Improving disease prediction using ICD-9 ontological features , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[13]  G. Raju,et al.  Fuzzy Clustering Methods in Data Mining: A Comparative Case Analysis , 2008, 2008 International Conference on Advanced Computer Theory and Engineering.

[14]  Seema Chopra,et al.  Identification of rules using subtractive clustering with application to fuzzy controllers , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[15]  Abdelali En-Nadi,et al.  The impact of the concepts of lean manufacturing on the strategies of the supply chain , 2014 .

[16]  R. Suganya,et al.  Fuzzy C- Means Algorithm- A Review , 2012 .