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

doctoral thesis

• 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: dubinska analiza podataka; biomedicinski vremenski nizovi; izlučivanje značajki; računalni radni okvir; nelinearne značajke; odabir atributa; razvrstavanje u više ciljnih razreda; srčani ritam; entropija
• Keywords (english): data mining; biomedical time-series; feature extraction; computer framework; nonlinear features; feature selection; multi-class classification; cardiac rhythm; entropy
• 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
• Created on: 2019-07-17 08:25:11