• MeSH
• artefakty MeSH
• Bayesova věta MeSH
• kardiografie impedanční metody   využití   MeSH
• lidé MeSH
• normální rozdělení MeSH
• počítačové zpracování signálu MeSH
• srdeční komory patofyziologie   MeSH
• statistika jako téma MeSH
• synkopa prevence a kontrola   MeSH
• Check Tag
• lidé MeSH

• MeSH
• matematické výpočty počítačové MeSH
• matematika MeSH
• numerická analýza pomocí počítače * MeSH
• Publikační typ
• monografie MeSH

• Konspekt
• Počítačová věda. Výpočetní technika. Informační technologie
• NLK Obory
• přírodní vědy
• přírodní vědy

Jednou z důležitých úloh při zkoumání struktury sledu akčních potenciálů je odhad tzv. firing rate funkce. Cílem článku je popsat možné zrychlení algoritmu, který může být pro tento odhad použit. Protože neexistuje jednoznačná metoda, kterak firing rate funkci odvodit, bylo navržené celé spektrum odlišných strategií. Jedna z populárních metod odhadu je konvoluce sledu akčních potenciálů gaussovským jádrem s patřičnou šířkou. Výběr konkrétního jádra a šířky je obvykle diskutabilní a autoři v nedávném článku [1] navrhují přesný algoritmus pro výpočet optimální šířky pro (nejen) gaussovská jádra. Pro rozsáhlejší množinu vstupních dat je elementární verze algoritmu bohužel neefektivní z hlediska času potřebného pro výpočet. V příspěvku navrhujeme vylepšenou implementaci algoritmu, která je efektivní i pro velká množství vstupních dat. Na konkrétních výsledcích implementovaného algoritmu bylo demonstrováno dosažené zrychlení, což potvrzuje vhodnost navrhované metody.

One of the important tasks in the spike train analysis is to estimate the underlying firing rate function. The aim of this article is to improve the time performance of an algorithm which can be used for the estimation. As there is no unique way how to infer the firing rate function, several different methods have been proposed. A popular method how to estimate this function is the convolution of a spike train with Gaussian kernel with appropriate kernel bandwidth. The definition of what “appropriate” means remains a matter of discussion and a recent paper [1] proposes a method how to exactly compute optimal bandwidth under certain conditions. For large sets of spike train data the elementary version of the algorithm is unfortunately too inefficient in terms of computational time complexity. We present a refined version of the algorithm which in turn allows us to use the original method even for large data sets. The achieved performance improvement is demonstrated on a particular results and shows usability of the proposed method.

• Klíčová slova
• akční potenciál, sled akčních potenciálů, neurální kódování, firing rate, konvoluce, gaussovské jádro, šířka jádra, Brentova minimalizace, paralelní výpočet, MPI,
• MeSH
• akční potenciály fyziologie   MeSH
• algoritmy MeSH
• modely neurologické MeSH
• neurony fyziologie   MeSH
• signální transdukce fyziologie   MeSH

INTRODUCTION: The aim of this study was to determine the optimal image matrix and half-width of the Gaussian filter after iterative reconstruction of the PET image with point-spread function (PSF) and time-of-flight (TOF) correction, based on measuring the recovery coefficient (RC) curves. The measured RC curves were compared to those from an older system which does not use PSF and TOF corrections. MATERIALS AND METHODS: The measurements were carried out on a NEMA IEC Body Phantom. We measured the RC curves based on SUVmaxand SUVA50in source spheres with different diameters. The change in noise level for different reconstruction parameter settings and the relation between RC curves and the administered activity were also evaluated. RESULTS: With an increasing size of image matrix and reduction in the half-width of the post-reconstruction Gaussian filter, there was a significant increase in image noise and overestimation of the SUV. The local increase in SUV, observed for certain filtrations and objects with a diameter below 13mm, was caused by PSF correction. The decrease in administered activity, while maintaining the same conditions of acquisition and reconstruction, also led to overestimation of readings of the SUV and additionally to deterioration in reproducibility. CONCLUSION: This study proposes a suitable size for the image matrix and filtering for displaying PET and SUV measurements. The benefits were demonstrated as improved image parameters for the newer instrument, these even being found using relatively strong filtration of the reconstructed images.

• MeSH
• fantomy radiodiagnostické * MeSH
• lidé MeSH
• počítačové zpracování obrazu * MeSH
• pozitronová emisní tomografie * MeSH
• reprodukovatelnost výsledků MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

• MeSH
• algoritmy * MeSH
• databáze faktografické MeSH
• emoce fyziologie   MeSH
• kvalita hlasu MeSH
• lidé MeSH
• neuronové sítě MeSH
• počítačové zpracování signálu přístrojové vybavení   MeSH
• řeč fyziologie   MeSH
• ROC křivka MeSH
• rozpoznávání automatizované * MeSH
• rozpoznávání fyziologické fyziologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Parkinson's disease (PD) and essential tremor (ET) are prevalent movement disorders that mainly affect elderly people, presenting diagnostic challenges due to shared clinical features. While both disorders exhibit distinct speech patterns-hypokinetic dysarthria in PD and hyperkinetic dysarthria in ET-the efficacy of speech assessment for differentiation remains unexplored. Developing technology for automatic discrimination could enable early diagnosis and continuous monitoring. However, the lack of data for investigating speech behavior in these patients has inhibited the development of a framework for diagnostic support. In addition, phonetic variability across languages poses practical challenges in establishing a universal speech assessment system. Therefore, it is necessary to develop models robust to the phonetic variability present in different languages worldwide. We propose a method based on Gaussian mixture models to assess domain adaptation from models trained in German and Spanish to classify PD and ET patients in Czech. We modeled three different speech dimensions: articulation, phonation, and prosody and evaluated the models' performance in both bi-class and tri-class classification scenarios (with the addition of healthy controls). Our results show that a fusion of the three speech dimensions achieved optimal results in binary classification, with accuracies up to 81.4 and 86.2% for monologue and /pa-ta-ka/ tasks, respectively. In tri-class scenarios, incorporating healthy speech signals resulted in accuracies of 63.3 and 71.6% for monologue and /pa-ta-ka/ tasks, respectively. Our findings suggest that automated speech analysis, combined with machine learning is robust, accurate, and can be adapted to different languages to distinguish between PD and ET patients.

• Publikační typ
• časopisecké články MeSH

• MeSH
• magnetická rezonanční spektroskopie metody   diagnostické užití   MeSH
• magnetická rezonanční tomografie MeSH
• Publikační typ
• monografie MeSH

• Konspekt
• Lékařské vědy. Lékařství
• NLK Obory
• radiologie, nukleární medicína a zobrazovací metody
• techniky diagnostické zobrazovací

• MeSH
• interpretace obrazu počítačem MeSH

• Konspekt
• Lékařské vědy. Lékařství
• NLK Obory
• lékařská informatika

OBJECTIVE: We examine annual rates of emergency department (ED) visits, hospital admissions, and alternate levels of care (ALC) days (ie, the number of days that an older adult remained in hospital when they could not be safely discharged to an appropriate setting in their community) among older adults. DESIGN: Repeated cross-sectional study. SETTING AND PARTICIPANTS: Linked, individual-level health system administrative data on community-dwelling persons, home care recipients, residents of assisted living facilities, and residents of nursing homes aged 65 years and older in Ontario, Canada, from January 1, 2013, to December 31, 2019. METHODS: We calculated rates of ED visits, hospital admissions, and ALC days per 1000 individuals per older adult population per year. We used a generalized linear model with a gaussian distribution, log link, and year fixed effects to obtain rate ratios. RESULTS: There were 1,655,656 older adults in the community, 237,574 home care recipients, 42,600 older adults in assisted living facilities, and 94,055 older adults in nursing homes in 2013; there were 2,129,690 older adults in the community, 281,028 home care recipients, 56,975 older adults in assisted living facilities, and 95,925 older adults in nursing homes in 2019. Residents of assisted living facilities had the highest rates of ED visits (1260.692019 vs 1174.912013), hospital admissions (482.632019 vs 480.192013), and ALC days (1905.572019 vs 1443.032013) per 1000 individuals. Residents of assisted living facilities also had significantly higher rates of ED visits [rate ratio (RR) 3.30, 95% CI 3.20, 3.41), hospital admissions (RR 6.24, 95% CI 6.01, 6.47), and ALC days (RR 25.68, 95% CI 23.27, 28.35) relative to community-dwelling older adults. CONCLUSIONS AND IMPLICATIONS: The disproportionate use of ED visits, hospital admissions, and ALC days among residents of assisted living facilities may be attributed to the characteristics of the population and fragmented licensing and regulation of the sector, including variable models of care. The implementation of interdisciplinary, after-hours, team-based approaches to home and primary care in assisted living facilities may reduce the potentially avoidable use of ED visits, hospital admissions, and ALC days among this population and optimize resource allocation in health care systems.

• MeSH
• domy s pečovatelskou službou * MeSH
• hospitalizace MeSH
• lidé MeSH
• nemocnice MeSH
• průřezové studie MeSH
• senioři MeSH
• urgentní služby nemocnice MeSH
• Check Tag
• lidé MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Ontario MeSH

BACKGROUND: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis is a heterogenous autoimmune disease. While traditionally stratified into two conditions, granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA), the subclassification of ANCA-associated vasculitis is subject to continued debate. Here we aim to identify phenotypically distinct subgroups and develop a data-driven subclassification of ANCA-associated vasculitis, using a large real-world dataset. METHODS: In the collaborative data reuse project FAIRVASC (Findable, Accessible, Interoperable, Reusable, Vasculitis), registry records of patients with ANCA-associated vasculitis were retrieved from six European vasculitis registries: the Czech Registry of ANCA-associated vasculitis (Czech Republic), the French Vasculitis Study Group Registry (FVSG; France), the Joint Vasculitis Registry in German-speaking Countries (GeVas; Germany), the Polish Vasculitis Registry (POLVAS; Poland), the Irish Rare Kidney Disease Registry (RKD; Ireland), and the Skåne Vasculitis Cohort (Sweden). We performed model-based clustering of 17 mixed-type clinical variables using a parsimonious mixture of two latent Gaussian variable models. Clinical validation of the optimal cluster solution was made through summary statistics of the clusters' demography, phenotypic and serological characteristics, and outcome. The predictive value of models featuring the cluster affiliations were compared with classifications based on clinical diagnosis and ANCA specificity. People with lived experience were involved throughout the FAIRVASVC project. FINDINGS: A total of 3868 patients diagnosed with ANCA-associated vasculitis between Nov 1, 1966, and March 1, 2023, were included in the study across the six registries (Czech Registry n=371, FVSG n=1780, GeVas n=135, POLVAS n=792, RKD n=439, and Skåne Vasculitis Cohort n=351). There were 2434 (62·9%) patients with GPA and 1434 (37·1%) with MPA. Mean age at diagnosis was 57·2 years (SD 16·4); 2006 (51·9%) of 3867 patients were men and 1861 (48·1%) were women. We identified five clusters, with distinct phenotype, biochemical presentation, and disease outcome. Three clusters were characterised by kidney involvement: one severe kidney cluster (555 [14·3%] of 3868 patients) with high C-reactive protein (CRP) and serum creatinine concentrations, and variable ANCA specificity (SK cluster); one myeloperoxidase (MPO)-ANCA-positive kidney involvement cluster (782 [20·2%]) with limited extrarenal disease (MPO-K cluster); and one proteinase 3 (PR3)-ANCA-positive kidney involvement cluster (683 [17·7%]) with widespread extrarenal disease (PR3-K cluster). Two clusters were characterised by relative absence of kidney involvement: one was a predominantly PR3-ANCA-positive cluster (1202 [31·1%]) with inflammatory multisystem disease (IMS cluster), and one was a cluster (646 [16·7%]) with predominantly ear-nose-throat involvement and low CRP, with mainly younger patients (YR cluster). Compared with models fitted with clinical diagnosis or ANCA status, cluster-assigned models demonstrated improved predictive power with respect to both patient and kidney survival. INTERPRETATION: Our study reinforces the view that ANCA-associated vasculitis is not merely a binary construct. Data-driven subclassification of ANCA-associated vasculitis exhibits higher predictive value than current approaches for key outcomes. FUNDING: European Union's Horizon 2020 research and innovation programme under the European Joint Programme on Rare Diseases.

• MeSH
• ANCA-asociované vaskulitidy * klasifikace   diagnóza   epidemiologie   krev   imunologie   MeSH
• dospělí MeSH
• kohortové studie MeSH
• lidé středního věku MeSH
• lidé MeSH
• mikroskopická polyangiitida klasifikace   epidemiologie   krev   diagnóza   imunologie   MeSH
• registrace * statistika a číselné údaje   MeSH
• senioři MeSH
• shluková analýza MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Evropa MeSH