Zdenek Smekal

TL;DR: To find a subset of handwriting features suitable for identifying subjects with PD and to build a predictive model to efficiently diagnose PD, handwriting samples were collected from medicated PD patients and age- and sex-matched controls.

TL;DR: 36 completely new pathological voice measures based on modulation spectra, inferior colliculus coefficients, bicepstrum, sample and approximate entropy and empirical mode decomposition are introduced, which means, that among all newly designed features those that quantify especially hoarseness or breathiness are good candidates for pathological speech identification.

TL;DR: Assessment of in-air movements during handwriting has a major impact on disease classification accuracy and can be with advance used in decision support systems for differential diagnosis of PD.

TL;DR: This study proves that digital parameterization of pressure and altitude/tilt patterns in children with dysgraphia can be used for preliminary diagnosis of this writing disorder and estimation of difficulty level as determined by the handwriting proficiency screening questionnaire.

TL;DR: While entropy-based feature selection appears to be the most stable, the feature selection techniques yielding the highest prediction performance are minimum redundance maximum relevance method and feature selection based on Bhattacharyya distance.