Ústav biomedicínského inženýrství
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- ItemAnalyzing the performance of biomedical time-series segmentation with electrophysiology data(NATURE PORTFOLIO, 2025-04-06) Ředina, Richard; Hejč, Jakub; Filipenská, Marina; Stárek, ZdeněkAccurate segmentation of biomedical time-series, such as intracardiac electrograms, is vital for understanding physiological states and supporting clinical interventions. Traditional rule-based and feature engineering approaches often struggle with complex clinical patterns and noise. Recent deep learning advancements offer solutions, showing various benefits and drawbacks in segmentation tasks. This study evaluates five segmentation algorithms, from traditional rule-based methods to advanced deep learning models, using a unique clinical dataset of intracardiac signals from 100 patients. We compared a rule-based method, a support vector machine (SVM), fully convolutional semantic neural network (UNet), region proposal network (Faster R-CNN), and recurrent neural network for electrocardiographic signals (DENS-ECG). Notably, Faster R-CNN has never been applied to 1D signals segmentation before. Each model underwent Bayesian optimization to minimize hyperparameter bias. Results indicated that deep learning models outperformed traditional methods, with UNet achieving the highest segmentation score of 88.9 % (root mean square errors for onset and offset of 8.43 ms and 7.49 ms), closely followed by DENS-ECG at 87.8 %. Faster R-CNN and SVM showed moderate performance, while the rule-based method had the lowest accuracy (77.7 %). UNet and DENS-ECG excelled in capturing detailed features and handling noise, highlighting their potential for clinical application. Despite greater computational demands, their superior performance and diagnostic potential support further exploration in biomedical time-series analysis.
- ItemSpeech production under stress for machine learning: multimodal dataset of 79 cases and 8 signals(Springer Nature, 2024-11-12) Pešán, Jan; Juřík, Vojtěch; Růžičková, Alexandra; Svoboda, Vojtěch; Janoušek, Oto; Němcová, Andrea; Bojanovská, Hana; Aldabaghová, Jasmína; Kyslík, Filip; Vodičková, Kateřina; Sodomová, Adéla; Bartys, Patrik; Chudý, Peter; Černocký, JanEarly identification of cognitive or physical overload is critical in fields where human decision making matters when preventing threats to safety and property. Pilots, drivers, surgeons, and operators of nuclear plants are among those affected by this challenge, as acute stress can impair their cognition. In this context, the significance of paralinguistic automatic speech processing increases for early stress detection. The intensity, intonation, and cadence of an utterance are examples of paralinguistic traits that determine the meaning of a sentence and are often lost in the verbatim transcript. To address this issue, tools are being developed to recognize paralinguistic traits effectively. However, a data bottleneck still exists in the training of paralinguistic speech traits, and the lack of high-quality reference data for the training of artificial systems persists. Regarding this, we present an original empirical dataset collected using the BESST experimental protocol for capturing speech signals under induced stress. With this data, our aim is to promote the development of pre-emptive intervention systems based on stress estimation from speech.
- ItemMInfer: Bridging MetaboAnalyst and Jacobian analysis for metabolomic networks(Elsevier, 2025-02-13) Schwarzerová, Jana; Mate, Erdő Gabor; Idkowiak, Jakub; Olešová, Dominika; Kvasnička, Aleš; Dobesova, Dana; Friedecký, David; Provazník, Valentýna; Skarda, Jozef; Weckwerth, Wolfram; Nägele, ThomasMetabolomic interaction networks provide critical insights into the dynamic relationships between metabolites and their regulatory mechanisms. This study introduces MInfer, a novel computational framework that integrates outputs from MetaboAnalyst, a widely used metabolomic analysis tool, with Jacobian analysis to enhance the derivation and interpretation of these networks.
- ItemMethod Comparison for Bone Density in Multiple Myeloma Patients(Czech Society for Biomedical Engineering and Medical Informatics, 2024-09-30) Nohel, Michal; Mézl, Martin; Válek, Vlastimil; Dostál, Marek; Chmelík, JiříBone mineral density (BMD) is an important indicator of bone health, particularly in patients with conditions such as multiple myeloma. This study aims to compare three methodologies for quantifying BMD in vertebral regions affected by lytic lesions: two using data from conventional CT with different corrections for tissue composition, and one using data acquired on a dual-energy CT system. Method 1 is based on conventional CT with corrections using reference values for muscle and fat, Method 2 uses conventional CT with corrections based on the measured CT values of paraspinal muscle, and Method 3 is based on dual-energy CT. The Wilcoxon signed-rank test was used for statistical comparison, as the dataset did not follow a normal distribution. The results indicated significant differences between Methods 1 and 2 for BMD in regions of interest (ROIs) within lytic lesions, while no significant differences were found for other comparisons in this group. For vertebrae affected by multiple myeloma, significant differences were found between Methods 1 and 2, and Methods 2 and 3, but not between Methods 1 and 3. In healthy vertebrae, a significant difference was found only between Methods 2 and 3. When all ROIs were combined, significant differences were found between Methods 1 and 2, and Methods 2 and 3, with no difference between Methods 1 and 3. Future research will focus on objectively assessing the accuracy of these methods by comparing their results with a calibration phantom.
- ItemThe histone chaperones ASF1 and HIRA are required for telomere length and 45S rDNA copy number homeostasis(Wiley, 2024-10-14) Machelová, Adéla; Dadejová, Martina; Franek, Michal; Mougeot, Guillaume; Simon, Lauriane; Le Goff, Samuel; Duc, Celine; Bassler, Jasmin; Demko, Martin; Schwarzerová, Jana; Desset, Sophia; Probst, Aline; Dvořáčková, MartinaGenome stability is significantly influenced by the precise coordination of chromatin complexes that facilitate the loading and eviction of histones from chromatin during replication, transcription, and DNA repair processes. In this study, we investigate the role of the Arabidopsis H3 histone chaperones ANTI-SILENCING FUNCTION 1 (ASF1) and HISTONE REGULATOR A (HIRA) in the maintenance of telomeres and 45S rDNA loci, genomic sites that are particularly susceptible to changes in the chromatin structure. We find that both ASF1 and HIRA are essential for telomere length regulation, as telomeres are significantly shorter in asf1a1b and hira mutants. However, these shorter telomeres remain localized around the nucleolus and exhibit a comparable relative H3 occupancy to the wild type. In addition to regulating telomere length, ASF1 and HIRA contribute to silencing 45S rRNA genes and affect their copy number. Besides, ASF1 supports global heterochromatin maintenance. Our findings also indicate that ASF1 transiently binds to the TELOMERE REPEAT BINDING 1 protein and the N terminus of telomerase in vivo, suggesting a physical link between the ASF1 histone chaperone and the telomere maintenance machinery.