Towards to Optimal Wavelet Denoising Scheme-A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing.

Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.

Machine learning approach to flare-up detection and clustering in chronic obstructive pulmonary disease (COPD) patients.

Purpose: Chronic obstructive pulmonary disease (COPD) is a prevalent and preventable condition that typically worsens over time. Acute exacerbations of COPD significantly impact disease progression, underscoring the importance of prevention efforts. This observational study aimed to achieve two main objectives: (1) identify patients at risk of exacerbations using an ensemble of clustering algorithms, and (2) classify patients into distinct clusters based on disease severity. Methods: Data from portable medical devices were analyzed post-hoc using hyperparameter optimization with Self-Organizing Maps (SOM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest, and Support Vector Machine (SVM) algorithms, to detect flare-ups. Principal Component Analysis (PCA) followed by KMeans clustering was applied to categorize patients by severity. Results: 25 patients were included within the study population, data from 17 patients had the required reliability. Five patients were identified in the highest deterioration group, with one clinically confirmed exacerbation accurately detected by our ensemble algorithm. Then, PCA and KMeans clustering grouped patients into three clusters based on severity: Cluster 0 started with the least severe characteristics but experienced decline, Cluster 1 consistently showed the most severe characteristics, and Cluster 2 showed slight improvement. Conclusion: Our approach effectively identified patients at risk of exacerbations and classified them by disease severity. Although promising, the approach would need to be verified on a larger sample with a larger number of recorded clinically verified exacerbations.

The effectiveness of glucocorticoid treatment in post-COVID-19 pulmonary involvement.

RATIONALE: Persistent respiratory symptoms following Coronavirus Disease 2019 (COVID-19) are associated with residual radiological changes in lung parenchyma, with a risk of development into lung fibrosis, and with impaired pulmonary function. Previous studies hinted at the possible efficacy of corticosteroids (CS) in facilitating the resolution of post-COVID residual changes in the lungs, but the available data is limited. AIM: To evaluate the effects of CS treatment in post-COVID respiratory syndrome patients. PATIENTS AND METHODS: Post-COVID patients were recruited into a prospective single-center observational study and scheduled for an initial (V1) and follow-up visit (V2) at the Department of Respiratory Medicine and Tuberculosis, University Hospital Olomouc, comprising of pulmonary function testing, chest x-ray, and complex clinical examination. The decision to administer CS or maintain watchful waiting (WW) was in line with Czech national guidelines. RESULTS: The study involved 2729 COVID-19 survivors (45.7% male; mean age: 54.6). From 2026 patients with complete V1 data, 131 patients were indicated for CS therapy. These patients showed significantly worse radiological and functional impairment at V1. Mean initial dose was 27.6 mg (SD ± 10,64), and the mean duration of CS therapy was 13.3 weeks (SD ± 10,06). Following therapy, significantly better improvement of static lung volumes and transfer factor for carbon monoxide (DLCO), and significantly better rates of good or complete radiological and subjective improvement were observed in the CS group compared to controls with available follow-up data (n = 894). CONCLUSION: Better improvement of pulmonary function, radiological findings and subjective symptoms were observed in patients CS compared to watchful waiting. Our findings suggest that glucocorticoid therapy could benefit selected patients with persistent dyspnea, significant radiological changes, and decreased DLCO.

Patients' UX Impact on Medication Adherence in Czech Pilot Study for Chronically Ill.

This article presents a comprehensive and multistage approach to the development of the user experience (UX) for an mHealth application targeting older adult patients with chronic diseases, specifically chronic heart failure and chronic obstructive pulmonary disease. The study adopts a mixed methods approach, incorporating both quantitative and qualitative components. The underlying hypothesis posits that baseline medicine adherence knowledge (measured by the MARS questionnaire), beliefs about medicines (measured by the BMQ questionnaire), and level of user experience (measured by the SUS and UEQ questionnaires) act as predictors of adherence change after a period of usage of the mHealth application. However, contrary to our expectations, the results did not demonstrate the anticipated relationship between the variables examined. Nevertheless, the qualitative component of the research revealed that patients, in general, expressed satisfaction with the application. It is important to note that the pilot testing phase revealed a notable prevalence of technical issues, which may have influenced participants' perception of the overall UX. These findings contribute to the understanding of UX development in the context of mHealth applications for older adults with chronic diseases and emphasise the importance of addressing technical challenges to enhance user satisfaction and engagement.