1. |
DE RIJK M C, LAUNER L J, BERGER K, et al. Prevalence of Parkinson's disease in Europe: A collaborative study of population-based cohorts. Neurologic Diseases in the Elderly Research Group[J]. Neurology, 2000, 54(11 Suppl 5): S21-S23.
|
2. |
VAN DEN EEDEN S K, TANNER C M, BERNSTEIN A L, et al. Incidence of Parkinson's disease: variation by age, gender, and race/ethnicity[J]. Am J Epidemiol, 2003, 157(11): 1015-1022.
|
3. |
王宗宝, 黄永志,张新静,等.帕金森病患者局部场电位信号多频耦合特征分析[J].生物医学工程学杂志,2015,32(4):874-880.
|
4. |
O'SULLIVAN S B, SCHMITZ T J. Parkinson disease[M]//Physical rehabilitation. 5th ed. Philadelphia: F. A. Davis Company, 2007: 856-894.
|
5. |
BAGHAI-RAVARY L, BEET S W. Automatic speech signal analysis for clinical diagnosis and assessment of speech disorders [J]. Springer Briefs in Electrical and Computer Engineering, 2012, 115(2): 31-36.
|
6. |
LITTLE M A, MCSHARRY P E, HUNTER E J, et al. Suitability of dysphonia measurements for telemonitoring of Parkinson's disease[J]. IEEE Trans Biomed Eng, 2009, 56(4): 1015-1022.
|
7. |
SAKAR B E, ISENKUL M E, SAKAR C O, et al. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings[J]. IEEE J Biomed Health Inform, 2013, 17(4): 828-834.
|
8. |
HARIHARAN M, POLAT K, SINDHU R. A new hybrid intelligent system for accurate detection of Parkinson's disease[J]. Comput Methods Programs Biomed, 2014, 113(3): 904-913.
|
9. |
ISLAM M S, PARVEZ I, DENG Hai, et al. Performance comparison of heterogeneous classifiers for detection of Parkinson's disease using voice disorder (dysphonia)[C]//2014 International Conference on Informatics, Electronics & Vision (ICIEV). Dhaka, 2014: 1-7.
|
10. |
PÉREZ C J, NARANJO L, MARTIN J, et al. A latent variable-based Bayesian regression to address recording replications in Parkinson's disease[C]//2014 22nd European Signal Processing Conference (EUSIPCO). Lisbon, 2014: 1447-1451.
|
11. |
TSANAS A, LITTLE M A, MCSHARRY P E, et al. Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests[J]. IEEE Trans Biomed Eng, 2010, 57(4): 884-893.
|
12. |
PRASHANTH R, ROY S D, MANDAL P K, et al. Parkinson's disease detection using olfactory loss and REM sleep disorder features[C]//2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago, 2014: 5764-5767.
|
13. |
BENBA A, JILBAB A, HAMMOUCH A, et al. Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson's disease[C]//2015 International Conference on Electrical and Information Technologies (ICEIT). Marrakech, 2015: 300-304.
|
14. |
TSANAS A, LITTLE M A, MCSHARRY P E, et al. Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease[J]. IEEE Trans Biomed Eng, 2012, 59(5): 1264-1271.
|
15. |
SU M, CHUANG K S. Dynamic feature selection for detecting Parkinson's disease through voice signal[C]//2015 IEEE MTT-S 2015 International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-BIO). Taipei, 2015: 148-149.
|
16. |
李娟, 王宇平.考虑局部均值和类全局信息的快速近邻原型选择算法[J].自动化学报,2014(6):1116-1125.
|
17. |
LI Leijun, HU Qinghua, WU Xiangqian, et al. Exploration of classification confidence in ensemble learning[J]. Pattern Recognit, 2014, 47(9): 3120-3131.
|
18. |
HATTORI K, TAKAHASHI M. A new nearest-neighbor rule in the pattern classification problem[J]. Pattern Recognit, 1999, 32(3): 425-432.
|
19. |
RICO-JUAN J R, IÑESTA J M. New rank methods for reducing the size of the training set using the nearest neighbor rule[J]. Pattern Recognit Lett, 2012, 33(5): 654-660.
|