Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA

被引：19
作者：
Benba A. [1 ]
Jilbab A. [1 ]
Hammouch A. [1 ]
机构：
[1] Laboratoire de Recherche en Génie Electrique, Ecole Normale Supérieure de l’Enseignement Technique, Mohammed V University, Rabat
来源：
International Journal of Speech Technology | 2016年 / 19卷 / 04期
关键词：
Feature selection; NPCA; Parkinson’s disease; PCA; SVM;
D O I：
10.1007/s10772-016-9367-z
中图分类号：
学科分类号：
摘要：
In this study, we wanted to discriminate between two groups of people. The database used in this study contains 20 patients with Parkinson’s disease and 20 healthy people. Three types of sustained vowels (/a/, /o/ and /u/) were recorded from each participant and then the analyses were done on these voice samples. Firstly, an initial feature vector extracted from time, frequency and cepstral domains. Then we used linear and nonlinear feature extraction techniques, principal component analysis (PCA), and nonlinear PCA. These techniques reduce the number of parameters and choose the most effective acoustic features used for classification. Support vector machine with its different kernel was used for classification. We obtained an accuracy up to 87.50 % for discrimination between PD patients and healthy people. © 2016, Springer Science+Business Media New York.
引用
收藏
页码：743 / 754
页数：11
相关论文
共 34 条
[1]  
Abramson E.L., Et al., Physician experiences transitioning between an older versus newer electronic health record for electronic prescribing, International Journal of Medical Informatics, 81, 8, pp. 539-548, (2012)
[2]  
Andersen T., Et al., Designing for collaborative interpretation in telemonitoring: Re-introducing patients as diagnostic agents, International Journal of Medical Informatics, 80, 8, pp. e112-e126, (2011)
[3]  
Atal B.S., Hanauer S.L., Speech analysis and synthesis by linear prediction of the speech wave, The Journal of the Acoustical Society of America, 50, 2B, pp. 637-655, (1971)
[4]  
Benba A., Jilbab A., Hammouch A., Voice analysis for detecting persons with Parkinson’s disease using PLP and VQ, Journal of Theoretical and Applied Information Technology, 70, 3, pp. 443-450, (2014)
[5]  
Benba A., Jilbab A., Hammouch A., (2014c). Voiceprint analysis using Perceptual Linear Prediction and Support Vector Machines for detecting persons with Parkinson’s disease. In The 3rd international conference on health science and biomedical systems, Florence, pp. 22-24, (2014)
[6]  
Benba A., Jilbab A., Hammouch A., Detecting patients with Parkinson’s disease using Mel Frequency Cepstral Coefficients and Support Vector Machines, International Journal on Electrical Engineering and Informatics, 7, 2, (2015)
[7]  
Benba A., Jilbab A., Hammouch A., (2015b). Detecting patients with Parkinson’s disease using PLP and VQ. In The 7th International conference on information technology, Amman, pp. 12-15, (2015)
[8]  
Boersma P., Weenink D., Praat, a system for doing phonetics by computer, Glot International, 5, pp. 341-345, (2001)
[9]  
Chen H.-L., Et al., An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach, Expert Systems with Applications, 40, 1, pp. 263-271, (2013)
[10]  
De Lau L.M.L., Lau D., Breteler M., Epidemiology of Parkinson’s disease, The Lancet Neurology, 5, 6, pp. 525-535, (2006)
1234