The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessment. In the paper [6], [PERSON] et al. focus on hypokinetic dysarthria, which is associated with PD, affects several speech dimensions, including phonation. Although the scientific community has dealt with a quantitative analysis of phonation in PD patients, a complex research revealing probable relations between phonatory features and progress of PD is missing. Therefore, the aim of this study is to explore these relations and model them mathematically to be able to estimate progress of PD during a two-year follow-up. Authors enrolled 51 PD patients who were assessed by three commonly used clinical scales. In addition, eight possible phonatory disorders in five vowels were quantified. To identify the relationship between baseline phonatory features and changes in clinical scores, a partial correlation analysis was performed. Finally, XGBoost models to predict the changes in clinical scores during a two-year follow-up were trained. For two years, the patients' voices became more aperiodic with increased microperturbations of frequency and amplitude. Next, the XGBoost models were able to predict changes in clinical scores with an error in range 11-26%. Although some significant correlations between changes in phonatory features and clinical scores were identified, they are less interpretable. This study suggests that it is possible to predict the progress of PD based on the acoustic analysis of phonation. Moreover, it recommends utilizing the sustained vowel /i/ instead of /a/. In the paper [7], [PERSON] et al. describe an efficient method to construct an overcomplete and multi-scale dictionary for sparse electrocardiogram (ECG) representation using waveforms recorded from real-world patients. The ECG was the first biomedical signal for which digital signal processing techniques were extensively applied. By its own nature, the ECG is typically a sparse signal, composed of regular activations (QRS complexes and other waveforms, such as the P and T waves) and periods of inactivity (corresponding to isoelectric intervals, such as the PQ or ST segments), plus noise and interferences. Unlike most existing methods (which require multiple alternative iterations of the dictionary learning and sparse representation stages), the proposed approach learns the dictionary first, and then applies a fast sparse inference algorithm to model the signal using the constructed dictionary. As a result, the introduced method is much more efficient from a computational point of view than other existing algorithms, thus becoming amenable to dealing with long recordings from multiple patients. Regarding the dictionary construction, first all the QRS complexes were located in the training database, then authors computed a single average waveform per patient, and finally the most representative waveforms (using a correlation-based approach) as the basic atoms that were resampled to construct the multi-scale dictionary were selected. Simulations on real-world records from Physionet's PTB database show the good performance of the proposed approach. In the work [8], written by [PERSON] et al., a fully automatic method for high resolution 3D volumetric segmentation of medical image data using modern supervised deep learning approach is presented. Authors introduce 3D Dense-U-Net neural network architecture implementing densely connected layers. It has been optimized for graphic process unit accelerated high resolution image processing on currently available hardware (Nvidia GTX 1080 ti). The method has been evaluated on MRI brain 3D volumetric dataset and computed tomography (CT) thoracic scan dataset for spine segmentation. In contrast with many previous methods, the approach is capable of precise segmentation of the input image data in the original resolution, without any pre-processing of the input image.