Date of creation: 11. 5. 2016
It is possible to download software here.
Publication to be cited
GALÁŽ, Z.; MEKYSKA, J.; MŽOUREK, Z.; SMÉKAL, Z.; REKTOROVÁ, I.; ELIÁŠOVÁ, I.; KOŠŤÁLOVÁ, M.; MRAČKOVÁ, M.; BERANKOVA, D. Prosodic Analysis of Neutral, Stress-modified and Rhymed Speech in Patients with Parkinson’ s Disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2016, č. 1, s. 1-17. ISSN: 0169-2607.
GALÁŽ, Z. Quantification of Prosodic Impairment in Patients with Idiopathic Parkinson’ s Disease. In Proceedings of the 22nd Conference STUDENT EEICT 2016. Brno: 2016. s. 538-542. ISBN: 978-80-214-5350- 0.
In the field of biomedical signal processing (e.g. pathological speech signal processing), often it is necessary to use binary classification to evaluate the sufficiency of selected objective parametrization techniques to quantify and predict the health state of the analysed subject (e.g. healthy subject, disordered subject). For this purpose, the researchers nowadays use multiple sophisticated classification techniques and metrices to evaluate classification models. Classification analysis tool (CAT) provides an easy and fast way to train and test variety of classification models.
Software CAT provides the 6 possible classification techniques: Support Vector Machines (SVM), Naive Bayes Networks (NBN), Linear Discriminant Analysis (LDA), k-Nearest Neighbour (kNN), Classification Trees (RF) and Gaussian Mixture Models (GMM). It also provides 18 metrics evaluating quality of classification process: Classification Accuracy (ACC), sensitivity (SEN), specificity (SPE), Matthew’s Correlation Coefficient (MCC), etc. The package also provides the function for the cross-validation process (k-fold, leave-one-out).
The CAT software is written in the MATLAB programming environment. The testing script demo.m can be used to test functionality of the software. The script loads the data from the test_cls.mat file, which includes the parametrization matrix “feat_matrix”: rows are determined for the observations; columns are determined for the parameters and the vector of labels “labels” (for the classification task: 0/1 – healthy/disordered).
This work was supported by projects AZV 16-30805A, LO1401, COST IC1206 a FEKT-S-14-2335. The described research was performed in laboratories supported by the SIX project; the registration number CZ.1.05/2.1.00/03.0072, the operational program Research and Development for Innovation.