Dubinska analiza biomedicinskih vremenskih nizova zasnovana na računalnom radnom okviru za izlučivanje značajki

Jović, Alan

prikaz prve stranice dokumenta Dubinska analiza biomedicinskih vremenskih nizova zasnovana na računalnom radnom okviru za izlučivanje značajki

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doctoral thesis

Dubinska analiza biomedicinskih vremenskih nizova zasnovana na računalnom radnom okviru za izlučivanje značajki

Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2012. urn:nbn:hr:168:228345

Jović, Alan

University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Electronics, Microelectronics, Computer and Intelligent Systems

Cite this document

APA 6th Edition

Jović, A. (2012). Dubinska analiza biomedicinskih vremenskih nizova zasnovana na računalnom radnom okviru za izlučivanje značajki (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:228345

MLA 8th Edition

Jović, Alan. "Dubinska analiza biomedicinskih vremenskih nizova zasnovana na računalnom radnom okviru za izlučivanje značajki." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2012. https://urn.nsk.hr/urn:nbn:hr:168:228345

Chicago 17th Edition

Harvard

Jović, A. (2012). 'Dubinska analiza biomedicinskih vremenskih nizova zasnovana na računalnom radnom okviru za izlučivanje značajki', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 18 September 2024, https://urn.nsk.hr/urn:nbn:hr:168:228345

Vancouver

Jović A. Dubinska analiza biomedicinskih vremenskih nizova zasnovana na računalnom radnom okviru za izlučivanje značajki [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2012 [cited 2024 September 18] Available at: https://urn.nsk.hr/urn:nbn:hr:168:228345

IEEE

A. Jović, "Dubinska analiza biomedicinskih vremenskih nizova zasnovana na računalnom radnom okviru za izlučivanje značajki", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2012. Available at: https://urn.nsk.hr/urn:nbn:hr:168:228345

Cite this item: https://urn.nsk.hr/urn:nbn:hr:168:228345

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Metadata

Title	Dubinska analiza biomedicinskih vremenskih nizova zasnovana na računalnom radnom okviru za izlučivanje značajki
Title (english)	Data mining of biomedical time-series based on computer framework for feature extraction
Author	Alan Jović
Mentor	Nikola Bogunović (mentor)
Committee member	Sven Lončarić (predsjednik povjerenstva)
Committee member	Leo Budin (član povjerenstva)
Committee member	Dragan Gamberger (član povjerenstva)
Granter	University of Zagreb Faculty of Electrical Engineering and Computing (Department of Electronics, Microelectronics, Computer and Intelligent Systems) Zagreb
Defense date and country	2012-04-20, Croatia
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Computing
Universal decimal classification (UDC)	004 - Computer science and technology. Computing. Data processing
Abstract	Analiza biomedicinskih vremenskih nizova (BVN) obuhvaća široko interdisciplinarno područje. Istraživači u tom području nastoje kvantizirati složenost biološkog sustava kako bi se ostvarilo lakše razlikovanje između zdravog i bolesnog organizma. Uloga računarstva u tim istraživanjima je izlučivanje i dubinska analiza značajki. Cilj ove disertacije je sistematizirati pristupe izlučivanju značajki i predložiti sveobuhvatan postupak analize podataka na putu od baze podataka do konačnog modela koji ce omogućiti što bolje rezultate pri razvrstavanju poremećaja BVN. U tu svrhu predlaže se implementacija računalnog radnog okvira koji omogućuje izlučivanje velikog broja značajki za pojedinu vrstu BVN. U okviru disertacije implementacija okvira provedena je za područje analize srčanog ritma. Pritom su detaljno opisane vremenske, frekvencijske, vremensko-frekvencijske i nelinearne značajke korištene u tom području. Razvijena su dva nova nelinearna postupka za izlučivanje značajki iz BVN: abecedna entropija i napredna analiza slijednog trenda, za koje se pokazuje da poboljšavaju rezultate razvrstavanja poremećaja. Sustavni postupak vrednovan je na dva zasebna problema razvrstavanja, a postignuta je viša točnost od sličnih pristupa navedenih u literaturi. Rezultati dobiveni ovom disertacijom unaprijeđuju područje računalne analize BVN jer daju radni okvir temeljen na najboljoj praksi za postizanje visoke točnosti razvrstavanja poremećaja.
Abstract (english)	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.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:168:228345
Study programme	Title: Computer Science Study programme type: university Study level: postgraduate Academic / professional title: Doktor znanosti (Doktor znanosti)
Catalog URL	http://lib.fer.hr/cgi-bin/koha/opac-detail.pl?biblionumber=38858
Type of resource	Text
Extent	195 str ; 30 cm
File origin	Born digital
Access conditions	Closed access
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Repository	FER Repository
Created on	2019-07-17 08:25:11