https://repositorio.ufu.br/handle/123456789/18979 Sun, 05 Apr 2026 16:04:35 GMT 2026-04-05T16:04:35Z https://repositorio.ufu.br/handle/123456789/48494 Title: Estímulos sonoros e desfechos clínicos em pacientes com distúrbios da consciência: análise em unidade de terapia intensiva Abstract: Disorders of consciousness (DoC) pose a major challenge in the intensive care unit (ICU), particularly regarding diagnosis, prognosis, and therapeutic decision-making. In this context, electroencephalography (EEG) combined with auditory stimulation has been investigated as a promising strategy to assess cortical responsiveness and assist in monitoring the clinical evolution of critically ill patients. This study aimed to describe the demographic and clinical profile of patients with DoC; characterize their clinical course during hospitalization; ana-lyze hospital outcomes; and evaluate behavioral responses to auditory stimulation recorded simultaneously with EEG. This is a cross-sectional, quantitative, and analytical study con-ducted with DoC patients admitted to the Adult ICU of the Hospital de Clínicas de Uber-lândia, for whom an EEG had been requested by the attending team. EEG recordings were performed at bedside over a 20-minute session comprising six auditory stimuli interspersed with verbal stimulation. A wide range of demographic and clinical variables from admis-sion, intervention, and hospital discharge were collected directly from electronic medical records. Statistical analyses were performed using the Statistical Package for the Social Sci-ences (SPSS). A total of 22 patients participated in the study, predominantly male (68.2%), with a mean age of 55.63 years. The clinical profile was heterogeneous, with substantial var-iation in DoC etiologies, severity, and general condition at admission. Clinical evolution during hospitalization varied significantly; longer ICU stay and advanced age were associat-ed with poorer levels of consciousness at discharge. Infectious complications, the need for invasive devices, and cardiorespiratory arrest were observed in part of the sample, contrib-uting to longer hospital stays and worse outcomes. Patients with traumatic brain injury demonstrated higher consciousness scores at discharge compared to other etiologies. Fol-lowing auditory stimulation, some patients exhibited increases in Glasgow Coma Scale scores, whereas sedated individuals showed little change, supporting the notion that the level of sedation influences responsiveness. In conclusion, the integrated analysis of clinical and neurophysiological data contributes to improving clinical management, providing support for the development of more precise and individualized monitoring strategies aimed at en-hancing care for patients with DoC. Wed, 10 Dec 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48494 2025-12-10T00:00:00Z https://repositorio.ufu.br/handle/123456789/48489 Title: Estudo das estimulações cognitivas por meio de eletroencefalografia Abstract: Spirituality may be considered as a practice or cerebral state related to different religious traditions, influencing multiple neural functions. Although studies on its physiological effects are still scarce, the literatura points out that meditation leads to measurable changes in EEG activity. Articles report increases in alpha and theta power, reductions in frontal gamma. However, a significant gap persists in the 40–100 Hz frequency range, which is very importante for understanding complex brain functions. In this context, this study analyze quantitative variations in the EEG of healthy individuals during spiritual meditation. After presenting a systematic review of the literature, the thesis proposes a protocol for experimental studies, during which volunteers undergo four periods of stimulation: silence, music, personalized pre-meditation, and meditation guided by the “Our Father” prayer. This protocol was applied to Twenty-six healthy individuals, whose EEG was recorded with 400 Hz sampling rate, segmented into 2-second epochs and analyzed through coherence. Quantitative analysis of ten individuals was performed, using statistical analysis, the Mann-Whitney test, and by percentage variation (PV). Results point out a mean PV of 11.76%, including maximum power changes above 25%, when EEG signals recorded during silence are compared to those segments during meditation. The greatest changes occurred mainly in the gamma and super-gamma bands, particularly on pairs Fp1-Fp2 and C3-C4. The central region was the most sensitive to changes, while slow rhythms such as delta exhibited moderate power variations. In parallel, stochastic analyses of neuronal cell cultures, involving Wistar rats, plated on microelectrode arrays (MEA), point out that spontaneous neuronal signals exhibit non-Gaussian distributions, high complexity, and subtle transitions between states, revealed through probabilistic tests and probability density analysis. These approaches allow the detection of emergent properties such as self-organization and functional instability, expanding the translational potential for future studies on consciousness and spirituality. The thesis integrates EEG findings, a systematic review, and probabilistic analyses, indicating that spiritual practices modulate brain rhythms, thus activating complex cognitive functions. In consequence, they may influence health and well-being. Despite sample limitations, the findings reinforce the need for specific protocols, greater methodological standardization, and randomized studies, contributing to the advancement of interfaces between medicine, spirituality, neuroscience, and healthcare. Mon, 15 Dec 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48489 2025-12-15T00:00:00Z https://repositorio.ufu.br/handle/123456789/47859 Title: DeepLabCut Applied to the assessment of spiral and sinusoidal patterns in individuals with Parkinson's disease Abstract: Parkinson’s disease (PD) is a neurodegenerative disorder that affects both motor and non-motor functions. It is caused by dopamine deficiency in the brain, particularly in the substantia nigra. This shortage leads to symptoms such as bradykinesia, tremor, and rigidity, as well as non-motor symptoms like memory loss, anxiety, and depression. One of the major challenges in diagnosing PD is the lack of a precise, objective, and early-stage diagnostic method. Most current diagnostic techniques are subjective and expensive, and imaging methods such as magnetic resonance imaging only become informative at later disease stages. This study proposes a low-cost and replicable protocol supported by Machine Learning (ML) to aid in the diagnosis of PD. The method involves drawing tasks (spirals and sinusoidal waves) recorded via smartphone video. These recordings were processed using DeepLabCut (DLC) (Deep learning tool to train a specific neural network to extract positional data), which was then analyzed using PyCaret to classify individuals with different ML algorithms. Multiple tests were conducted, varying the number of folds and combining feature selection (FS) and tune function (TF) techniques. The protocol successfully simulated symptoms relevant to PD diagnosis in a simple, accessible format. DeepLabCut, when trained, achieved an average detection precision of 99%, struggling only with abrupt movements due to motion blur. The ML models performed well, with the best results occurring in 3, 7, and 9-fold trials. The 7-fold setup was found to be the most balanced. The top three classifiers — Extra Trees, Gradient Boosting Classifier, and K-Nearest Neighbors — each achieved accuracy rates above 90%. FS and tf generally improved model performance, although some trade-offs were observed. Extra Trees and Gradient Boosting Classifier experienced overfitting due to the small dataset, whereas K Nearest Neighbors, despite having slightly lower performance, remained more balanced. Overall, the study presents a promising, low-cost, and reproducible approach to assist PD diagnosis using simple tools and low-code ML pipelines. Fri, 07 Nov 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/47859 2025-11-07T00:00:00Z https://repositorio.ufu.br/handle/123456789/47674 Title: Avaliação computacional das doses de radiação em radiografias de tórax de neonatos em UTI Abstract: Chest radiographs in neonatal patients are an essential diagnostic resource in neonatal intensive care units (NICUs), widely used for clinical monitoring and the assessment of respiratory and cardiovascular conditions. Although these procedures are fast, efficient, and relatively accessible, they involve radiation doses that, despite being low, may present an increased risk due to the high radiosensitivity of neonates and the frequency of repeated examinations during hospitalization. In this context, the present study aims to investigate the absorbed doses in different organs during neonatal chest X-ray examinations through Monte Carlo simulations. The MCNP6.3 radiation transport code was employed, using neonatal anthropomorphic phantoms provided by ICRP Publication 143, inserted into a virtual environment representing a NICU, including an incubator and a mobile X-ray unit. Simulations considered different tube voltages (45 to 65 kV) and field sizes (10×10 cm², 15×15 cm², and 20×20 cm²), quantifying entrance skin dose and doses to radiosensitive organs such as the eye lenses and thyroid. The validation of the simulated results was performed through experimental measurements with the Black Piranha with accessories (RTI Group AB). The results indicated that increasing tube voltage was the main factor leading to higher absorbed doses, while enlarging the irradiated field had a smaller impact but resulted in the inclusion of non-essential organs, such as the eye lenses and brain, in the primary beam. Conversely, smaller fields proved effective in reducing dose without compromising image quality. It is concluded that the application of optimized protocols, with emphasis on strict collimation and appropriate tube voltage, can significantly reduce radiation doses received by neonatal patients in radiology, reinforcing the importance of the ALARA principle in clinical practice. Fri, 14 Feb 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/47674 2025-02-14T00:00:00Z