Made available in DSpace on 2016-04-15T23:45:12Z (GMT). No. of bitstreams: 2 Roberta de Marchis Cosulich1.pdf: 978844 bytes, checksum: 598e87daf18ec2e4e77278fe0fde20ee (MD5) Roberta de Marchis Cosulich2.pdf: 2175146 bytes, checksum: 2d3e31ea9fe8acc63141cfd1b7131e52 (MD5) Previous issue date: 2007-03-05 / The research is the consequence of seeking for the understanding of art and design in Brazil. This work met (found) in the experience and the creations of the architect Lina Bo Bardi, a possible way to an answer. Coming from Italy to Brazil in 1946, the architect used her foreign look to analyze the popular art of Bahia s sertão (north-east of Brazil) with an anthropologic method and, having absorbed the information she transformed it in exhibitions, architecture and products, which are one of the possibilities to understand the evolution (story) of the design product in Brazil. / Conseqüência de uma busca para o entendimento da arte e, mais especificamente, do projeto de produtos no Brasil; este trabalho encontrou na experiência da produção da arquiteta Lina Bo Bardi (1914-1992) um caminho possível para uma resposta. Tendo vindo da Itália para o Brasil em 1946 a arquiteta utilizou seu olhar estrangeiro para interpretar a arte popular do sertão da Bahia como uma metodologia antropológica e, tendo absorvido estas informações as transformou em exposições, arquiteturas e produtos que são uma das tantas possibilidades para o entendimento da história do produto no Brasil.

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Cardiovascular diseases are the global leading cause of death. Automated electrocardiogram (ECG) analysis can support clinicians to identify abnormal excitation of the heart and prevent premature cardiovascular death. An explainable classification is particularly important for support systems. Our contribution to the PhysioNet/CinC Challenge 2021 (team name: ibmtPeakyFinders) therefore pursues an approach that is based on interpretable features to be as explainable as possible. To meet the challenge goal of developing an algorithm that works for both 12-lead and reduced lead ECGs, we processed each lead separately. We focused on signal processing techniques based on template delineation that yield the template's fiducial points to take the ECG waveform morphology into account. In addition to beat intervals and amplitudes obtained from the template, various heart rate variability and QT interval variability features were extracted and supplemented by signal quality indices. Our classification approach utilized a decision tree ensemble in a one-vs-rest approach. The model parameters were determined using an extensive grid search. Our approach achieved challenge scores of 0.47, 0.47, 0.34, 0.40, and 0.41 on hidden 12-, 6-, 4-, 3-, and 2-lead test sets, respectively, which corresponds to the ranks 12, 10, 23, 18, and 16 out of 39 teams.