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master's thesis
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Finding characteristic features on ECG signal based on neural networks
Antonio Sušac (2017)
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Visual Computing
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Sušac, A. (2017). Pronalaženje ključnih točaka EKG signala pomoću neuronskih mreža (Master's thesis). Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:571762

Sušac, Antonio. "Pronalaženje ključnih točaka EKG signala pomoću neuronskih mreža." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2017. https://urn.nsk.hr/urn:nbn:hr:200:571762

Sušac, Antonio. "Pronalaženje ključnih točaka EKG signala pomoću neuronskih mreža." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2017. https://urn.nsk.hr/urn:nbn:hr:200:571762

Sušac, A. (2017). 'Pronalaženje ključnih točaka EKG signala pomoću neuronskih mreža', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 16 June 2019, https://urn.nsk.hr/urn:nbn:hr:200:571762

Sušac A. Pronalaženje ključnih točaka EKG signala pomoću neuronskih mreža [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2017 [cited 2019 June 16] Available at: https://urn.nsk.hr/urn:nbn:hr:200:571762

A. Sušac, "Pronalaženje ključnih točaka EKG signala pomoću neuronskih mreža", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:200:571762

Metadata
TitlePronalaženje ključnih točaka EKG signala pomoću neuronskih mreža
Title (english)Finding characteristic features on ECG signal based on neural networks
AuthorAntonio Sušac
MentorIrena Galić (mentor)
Committee memberIrena Galić (predsjednik povjerenstva)
Committee memberČaslav Livada (član povjerenstva)
Committee memberKrešimir Romić (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Visual Computing)
Osijek
Defense date and country2017-09-28, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Process Computing
Abstract
Tema ovoga rada je pronalaženje ključnih točaka EKG signala pomoću umjetnih neuronskih mreža. Analiza i pronalaženje ključnih točaka predstavljaju važan čimbenik za proučavanje rada srca i lakše otkrivanje određenih nepravilnosti u radu srca. Nakon uvodnog dijela rada, obrađene su teme usko vezane za sam EKG i električnu aktivnost srca. Zatim se kroz cijelo poglavlje obrađuje model umjetnih neuronskih mreža. Kroz rad su spomenuti i drugi postojeći načini za detekciju ključnih točaka signala, a u završnom poglavlju napravljena je analiza i pronalaženje ključnih točaka EKG signala pomoću umjetnih neuronskih mreža. Za izvedbu je korišteno MATLAB programsko okruženje i skup alata Neural Network Toolbox.
Abstract (english)
In this paper, the topic was detection of key ECG signal points by artificial neural networks. Analyzing and finding key points is an important factor for studying heart function and for easier detection of certain irregularities in cardiac work. After introduction, topics closely related to ECG and electroactive heart activity were processed. Then, through the whole chapter, the model of artificial neural networks was processed. Through this work were also mentioned other existing ways of detecting key point signals, and in the final chapter, analysis and finding key ECG signal points were made using artificial neural networks. The MATLAB programming environment and the set of Neural Network Toolbox tools were used for performance.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:571762
Study programmeTitle: Računarstvo
Study programme type: sveučilišni
Study level: diplomski
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourcetext
File originBorn digital
Access conditionsOpen access
Terms of usehttp://rightsstatements.org/vocab/InC/1.0/
Created on2017-10-18 15:25:24