Date of creation: 7. 4. 2017
It is possible to download software here.
Publication to be cited
MEKYSKA, J.; FAÚNDEZ ZANUY, M.; MŽOUREK, Z.; GALÁŽ, Z.; SMÉKAL, Z.; ROSENBLUM, S. Identification and Rating of Developmental Dysgraphia by Handwriting Analysis. IEEE Transactions on Human-Machine Systems, 2016, roč. PP, č. 99, s. 1-14. ISSN: 2168-2291.
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.
In the field of biomedical signal processing (e.g. pathological speech signal processing), often it is necessary to use binary classification. For this purpose, the researchers nowadays use multiple sophisticated classification techniques. Classification grid search tool (CLCT) provides an easy and fast way to plot so-called learning curves to visualize if we deal with the high-bias or high-variance problem and consequently adjust the following analysis setup.
Software SGCT 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 the quality of classification process: Classification Accuracy (ACC), sensitivity (SEN), specificity (SPE), Matthew’s Correlation Coefficient (MCC), etc. The CGST software is written in the MATLAB programming environment. The testing scripts can be used to test the functionality of the software.
This work was supported by projects AZV 16-30805A and LO1401. 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.