Srdeční poruchy, jejichž příkladem je ischemie myokardu, infarkt, hypertrofie levé komory a myokarditida, jsou v experimentální kardiologii obvykle studovány na modelu izolovaného srdce. Kritéria pro detekci srdečních poruch však nejsou pro zvířecí modely standardizována, což komplikuje srovnání a interpretaci výsledků různých experimentálních studií. Obzvlášť složitá situace nastává při současném výskytu několika patologických jevů, jejichž vzájemná součinnost komplikuje rozpoznání jejich individuálních účinků. Korektní posouzení stavu srdce vyžaduje také zohlednění mnoha faktorů spojených s akvizicí dat. Tato práce je věnována kvantitativnímu hodnocení elektrofyziologických změn způsobených globální ischemií myokardu. Vliv ischemie byl hodnocen pro fyziologická srdce a srdce se zvětšenou levou komorou a dále pro srdce nabarvená napěťově-citlivým barvivem di-4-ANEPPS. Přestože jsou oba fenomény často zastoupeny v animálních studiích, nebyl dosud popsán jejich vliv na manifestaci ischemie v elektrogramech (EG), ani nebyl kvantifikován jejich vliv na přesnost detekčních algoritmů pro identifikaci ischemie. Práce shrnuje kvantitativní změny srdeční funkce vyvolané ischemií (v normálních podmínkách, při hypertrofii levé komory, a při administraci barviva) založené na hodnocení EG a VKG parametrů. Dále práce obsahuje rozbor důležitých aspektů akvizice záznamů, jako je umístění snímacích elektrod, způsob výpočtu deskriptorů z EG a VKG (s použitím výsledků manuálního rozměření záznamů, nebo bez něj) a identifikace okamžiku vývoje ischemie v preparátu. Nedílnou součást práce tvoří návrh, realizace a ověření metod pro automatickou detekci ischemie v experimentálních záznamech. Výsledky práce dokazují, že dosažení opakovatelných a věrohodných výsledků je podmíněno zohledněním všech výše uvedených faktorů souvisejících jak se stavem srdce, tak s metodikou záznamu a analýzy dat.

Clinical and research practice in the context of Autism rapidly evolved in the last decades. Finer diagnostic procedures, evidence-based models of intervention and higher social inclusivity significantly improved the possibility for autistic children to participate in the fabric of social life. In terms of health best practices, gold-standard procedures still need to be improved, and bridging research and clinical practice still presents several challenges. From the clinical standpoint, the role of process variables, predictors, mechanisms, and timing of change still requires extensive investigation in order to explain response variability and design optimized interventions, tailored to individual needs and maximally effective. Observational techniques represent the elective research methods in child development, especially in clinical contexts, due to their non-invasiveness. However, they still suffer from limited objectivity and poor quantification. Further, their main disadvantage is that they are highly time-consuming and labor-intensive. The aim of this thesis was moving forward to promote translational research in clinical practice of Autism intervention with preschool children. At first, we tried to design and apply quantitative observational techniques to longitudinally study treatment response trajectories during developmental intervention. We tried to characterize different response profiles, and which baseline predictors were able to predict the response over time. Secondly, we investigated mechanisms of change. In particular, we focused on the role of the child-therapist interaction dynamics as a possible active mediator of the process of intervention, especially in the developmental framework that stresses the importance of interpersonal aspects. We also aimed at understanding whether certain time-windows during the intervention were particularly predictive of the response, as well as which specific interaction aspects played a role. Finally, to promote the translational application of observational methods and to improve objective quantification, we proposed and validated an Artificial Intelligence (AI) system to automate data annotation in unconstrained clinical contexts, remaining completely non-invasive and dealing with the specific noisy data that characterize them, for the analysis of the child-therapist acoustic interaction. This effort represents a base building block enabling to employ downstream computational techniques greatly reducing the need for human annotation that usually prevents the application of observational research to large amounts of data . We discuss our findings stressing the importance of assuming a developmental framework in Autism, the key role of the interpersonal experience also in the clinical context, the importance of focusing on trajectories of change and the important need to promote the acquisition of large amounts of quantitative data from the clinical contexts exploiting AI-based systems to assist clinicians, improving objectivity, enabling treatment monitoring, and producing precious data-driven knowledge on treatment efficacy.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-11-12T19:08:26Z No. of bitstreams: 1 ALCIDES LEITE DE AMORIM - DISSERTAÇÃO PPGECA 1990..pdf: 35306341 bytes, checksum: 58ca99353fdbe330c0375e0d09e45b9a (MD5) / Made available in DSpace on 2018-11-12T19:08:26Z (GMT). No. of bitstreams: 1 ALCIDES LEITE DE AMORIM - DISSERTAÇÃO PPGECA 1990..pdf: 35306341 bytes, checksum: 58ca99353fdbe330c0375e0d09e45b9a (MD5) Previous issue date: 1990-12 / O presente trabalho constitui um estudo da região do nordeste brasileiro, objetivando a definição de regiões representadas pelos postos ou estações com características semelhantes em função de conjuntos de variáveis pluviométricas e fluviométricas. Utilizou-se técnicas de classificação automática aplicadas ao conjunto de variáveis que foram obtidas da combinação do período de referência (ano, semestre e trimestre) com os parâmetros (média aritmética, desvio padrão, coeficiente de variação e coeficiente de assimetria) e o valor máximo. Os dados pluviométricos são compostos por quatrocentos postos no intervalo de tempo entre 1337 e 1973, com trinta anos de registros e uma folga de cinco anos, enquanto os fluviométricos de noventa e sete estações com pelo menos oito anos de registros e que tenham seu inicio nas décadas de sessenta ou setenta ou seu término nas décadas de setenta ou oitenta. Foram aplicados os Métodos "Quick Cluster" e "K-Means" (técnicas de . classificação não hierárquicas) nos conjuntos de variáveis pluviométricas e os Métodos de, "Ward", Ligação Simples, Ligação Completa e Centróide (técnicas hierárquicas) nos conjuntos de variáveis fluviométricas. Foi também discutido a aplicabilidade de cada método. Os resultados decorrentes deste trabalho, ilustrados nos mapas, são úteis para o preenchimento de falhas, geração de dados, determinação da curva regional de probabilidade, determinação de um modelo determinístico tipo Chuva-Uazão, etc. / The present thesis constitutes a study of the north-east region of Brasil, with the objective of defining the groupings of raingauge stations and flow measuring stations, that have similar characteristics. Techniques of automatic cIassifiction as applied to a set of variables were utilised herein. These variables were obtained for a combination of reference periods (being a year, semester or trimester) among the parameters of the station data, such as arithmatic mean, standard deviation, coefficient of variation skewness coefficient and the maximum value. The rainfall data for 400 raingauge stations between the years 1937 to 1973 were utilized in the study. Thirty (30) years of data, with a superposition of atleast 5 years between the stations, were utilized for raingauge stations. The data for flow measuring stations, numbering 97, consisted of reliable data over an eight-year period. The Methods of "Quick Cluster" and "K-Means" (which belong to the techniques of non-hierarquic classification) were applied to the set of precipitation variables and the Methods of," U/ard", Simple Linking, Complete Linking and Centroid (which pertain to hierarquical techniques) were applied to the set of flow variables. The applicability of each of these methods is discussed here-in.

This diploma thesis deals with automatic clasification of vowel R. Purpose of this thesis is to made program for detection of pronounciation of speech defects at vowel R in children. In thesis are processed parts as speech creation, speech therapy, dyslalia and subsequently speech signal processing and analysis methods. In the last part is designed software for automatic detection of pronounciation of vowel R. For recognition of pronounciation is used algorithm MFCC for extracting features. This features are subsequently classified by neural network to the group of correct or incorrect pronounciation and is evaluated classification success.