This study involved performing tests to detect Parkinson’s disease (PD) based on voice changes, including speech phonation, articulation, and prosody, in patients with PD using different types of speech signal. For this purpose, during the first stage of the investigation, three separately modeled PD diagnosis systems using different types of speech signal characteristics were defined. The classification results were obtained when the SVM method was applied compared to the k-nearest neighbors method applying 1-nn in general. The tests were carried out within the database of patient voice recordings collected in the Department of Neurology at the Medical University of Warsaw. The second stage of the research was the selection of descriptors. The SFFS (sequential floating forward) method was applied together with the k-nn and SVM classifier. These subsets were used to create a new system based on a descriptor loose integration. Within the experiments conducted, general diagnosis results lead to improved classifier performance only in certain cases. This prompted the authors to conduct the last experimental research stage—selection at the feature fusion stage. Feature evaluation ranking methods (Relief, Fisher Score, F-tests, Chi-square) were applied for this purpose. With 10-fold validation, the k-nn method achieved an recognition rate of 92.2% with 91.1% sensitivity and 93.3% specificity.

Voice acoustic analysis can be a valuable and objective tool supporting the diagnosis of many neurodegenerative diseases, especially in times of distant medical examination during the pandemic. The article compares the application of selected signal processing methods and machine learning algorithms for the taxonomy of acquired speech signals representing the vowel a with prolonged phonation in patients with Parkinson’s disease and healthy subjects. The study was conducted using three different feature engineering techniques for the generation of speech signal features as well as the deep learning approach based on the processing of images involving spectrograms of different time and frequency resolutions. The research utilized real recordings acquired in the Department of Neurology at the Medical University of Warsaw, Poland. The discriminatory ability of feature vectors was evaluated using the SVM technique. The spectrograms were processed by the popular AlexNet convolutional neural network adopted to the binary classification task according to the strategy of transfer learning. The results of numerical experiments have shown different efficiencies of the examined approaches; however, the sensitivity of the best test based on the selected features proposed with respect to biological grounds of voice articulation reached the value of 97% with the specificity no worse than 93%. The results could be further slightly improved thanks to the combination of the selected deep learning and feature engineering algorithms in one stacked ensemble model.

The procedure for determination of trap parameters by photo-induced transient spectroscopy is based on the Arrhenius plot that illustrates a thermal dependence of the emission rate. In this paper, we show that the Arrhenius plot obtained by the correlation method is shifted toward lower temperatures as compared to the one obtained with the inverse Laplace transformation. This shift is caused by the model adequacy error of the correlation method and introduces errors to a calculation procedure of defect center parameters. The effect is exemplified by comparing the results of the determination of trap parameters with both methods based on photocurrent transients for defect centers observed in tin-doped neutron-irradiated silicon crystals and in gallium arsenide grown with the Vertical Gradient Freeze method.

Parkinson’s disease is a relatively common illness, constantly progressing, evoking anxiety and depression resulting from significantly restricted independence. Its dominating symptoms include muscle rigidity, rest tremor, slowness and postural disorders. They can lead to progressing gait disorders, hypomimia, extrapyramidal dysarthria and micrography. Acquisition of data imaging the condition of a patient and their objective evaluation can constitute a valuable tool improving the diagnostics and treatment monitoring. The developed data acquisition set enables recording data while the patient is performing selected tests, based on the UDPRS scale. The set consists of an infrared camera, a camera operating within the visible range, a microphone with a preamplifier, a graphic tablet and a laptop. A set of recording devices controlled by a graphic user interface guarantees the acquisition of data for the purposes of studying primarily motor symptoms. The set of recorded data includes a face image in the visible light range and in infrared for studying hypomimia, video images of the limbs for studying finger tapping movement regularity, hand movement regularity, lower limb agility and gait, and spontaneous and forced speech samples to evaluate the strength of the voice, its timbre and quality. In addition, the used graphic tablet enables collecting handwriting samples for testing writing speed and the force used. The suggested solution enables non-invasive quantitative measurements and archiving multimodal data describing the condition of a patient, which after processing can be used in diagnostics, evaluating treatment effectiveness and studying the progression of the disease.