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- W2969658445 abstract "Analysis of biomedical time-series encompasses a broad interdisciplinary area of scientific research. Researchers in this area intend to quantify the complexity of biological system in order to facilitate the differentiation between healthy and unhealthy organisms. The role of computing in this research is mostly related to feature extraction and data mining. The aim of this thesis is to provide a systematic approach starting from databases and continuing through feature extraction, data mining, and model evaluation that would ensure more accurate classification results for biomedical time-series analysis. As a part of the systematic approach, an implementation of a computer framework is proposed that consists of a large number of domain-relevant features. In this thesis, the framework is built for the domain of heart rhythm. The described features include: time domain, frequency domain, time-frequency, and nonlinear measures. Additional two novel methods for feature extraction are also proposed: alphabet entropy and advanced sequential trend analysis, which are shown to increase classification accuracy. The proposed method is evaluated on two distinct classification problems and the obtained results are better than any previously reported in literature. The results of this thesis contribute to the field of computer analysis of biomedical time-series because they provide a framework based on best practice for achieving high discrimination of different types of disorders." @default.
- W2969658445 created "2019-08-29" @default.
- W2969658445 creator A5038428045 @default.
- W2969658445 date "2012-04-20" @default.
- W2969658445 modified "2023-09-26" @default.
- W2969658445 title "Dubinska analiza biomedicinskih vremenskih nizova zasnovana na računalnom radnom okviru za izlučivanje značajki" @default.
- W2969658445 hasPublicationYear "2012" @default.
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