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). Nevertheless, to obtain a realistic estimation of the statistical strength of trained classification model, it is necessary to use sophisticated techniques. One of such techniques are so called permutation tests.
Software PTT provides an easy way to evaluate the statistical strength of trained classification model. It is possible to set the following parameters before the application’s start: type of cross-validation process (k-fold, leave-one-out), number of cross-validation runs, type of classifier, metric function and the number of permutations to process. It is also possible to plot graphs visualizing the process of permutation tests.
The PTT 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.