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Języki publikacji
Abstrakty
This paper addressees the problem of an early diagnosis of Parkinson’s disease by the classification of characteristic features of person’s voice. A new, two-step classification approach is proposed. In the first step, the voice samples are classified using standard state-of-the-art classifiers. In the second step, the classified samples are assigned to patients and the final classification process based on majority criterion is performed. The advantage of using our new approach is the resulting, reliable patientoriented medical diagnose. The proposed two-step method of classification allows also to deal with the variable number of voice samples gathered for every patient. Preliminary experiments revealed quite satisfactory classification accuracy obtained during the performed leave-one-out cross validation.
Rocznik
Tom
Strony
187--193
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
- Institute of Computer Science, University of Silesia, ul. Bedzinska 39, Sosnowiec
autor
- Institute of Computer Science, University of Silesia, ul. Bedzinska 39, Sosnowiec
autor
- Institute of Computer Science, University of Silesia, ul. Bedzinska 39, Sosnowiec
Bibliografia
- [1] DIETTERICH T., Approximate statistical tests for comparing supervised classification learning algorithms, Neural Computation, 1998, Vol. 10, pp. 1895–1923.
- [2] EFRON B., Estimating the error rate of a prediction rule: improvement on cross-validation, Journal of American Statistical Association, 1983, Vol. 78, pp. 316–33.
- [3] ENE MARIUS, Neural network-based approach to discriminate healthy people from those with Parkinson’s disease, Annals of the University of Craiova, Math. Comp. Sci. Ser., 2008, Vol. 35, pp. 112–116.
- [4] GABRIEL T. R., BERTHOLD M. R, Influence of fuzzy norms and other heuristics on mixed fuzzy rule formation, International Journal of Approximate Reasoning, 2004, Vol. 35, Issue 2, pp. 195202.
- [5] GOLBE L. I., MARK M. H., SAGE J. I., Parkinson’s disease handbook, 2010.
- [6] HARIHARAN M., POLAT KEMAL, SINDHU R., A new hybrid intelligent system for accurate detection of Parkinson’s disease, Computer Methods and Programs in Biomedicine, 2014, Vol. 113 (3), pp. 904–913.
- [7] HUI-LING CHEN, CHANG-CHENG HUANG, XIN-GANG YU, XIN XU, XIN SUN, GANG WANG, SUJING WANG, An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach, Expert Systems with Applications., 2013, Vol. 40 (1), pp. 263–271.
- [8] HUSE D. M., SCHULMAN K., ORSINI L., CASTELLI-HALEY J., KENNEDY S., LENHART G.. Burden of illness in parkinson’s disease, Mov Disord., 2005, pp. 1449–1454.
- [9] KNIME, http://www.knime.org.
- [10] LITTLE MAX A, MCSHARRY PATRICK E, ROBERTS STEPHEN J, COSTELLO DECLAN AE, IRENE M, COSTELLO DECLAN AE, MOROZ IRENE M, Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. 2007.
- [11] PARKINSON J., An essay on the shaking palsy, 1817.
- [12] ROUZBAHANI HAMID KARIMI, DALIRI MOHAMMAD REZA, Diagnosis of Parkinsons disease in human using voice signals. Basic and Clinical Neuroscience, 2011, Vol. 2 (3), pp. 12–20.
- [13] SANG-HONG LEE, JOON S. LIM, Parkinson’s disease classification using gait characteristics and wavelet-based feature extraction, Expert Systems with Applications, 2012, Vol. 39 (8), pp. 7338–7344.
- [14] TSANAS A., LITTLE M. A., FOX C., RAMIG L.O., Objective automatic assessment of rehabilitative speech treatment in Parkinson’s disease, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014, Vol. 22 (1), pp. 181–190.
- [15] TSANAS A., LITTLE M. A., MCSHARRY P. E., SPIELMAN J. L., RAMIG L. O., Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s disease, IEEE Trans. Biomed. Engineering, 2012, Vol. 59 (5), pp. 1264–1271.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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