prikaz prve stranice dokumenta Analiza biomedicinskih signala prikupljenih nosivim uređajima metodama strojnog učenja
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master's thesis
Analiza biomedicinskih signala prikupljenih nosivim uređajima metodama strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:052675
Podhraški, Silvio
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Podhraški, S. (2023). Analiza biomedicinskih signala prikupljenih nosivim uređajima metodama strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:052675

Podhraški, Silvio. "Analiza biomedicinskih signala prikupljenih nosivim uređajima metodama strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:052675

Podhraški, Silvio. "Analiza biomedicinskih signala prikupljenih nosivim uređajima metodama strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:052675

Podhraški, S. (2023). 'Analiza biomedicinskih signala prikupljenih nosivim uređajima metodama strojnog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 03 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:052675

Podhraški S. Analiza biomedicinskih signala prikupljenih nosivim uređajima metodama strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 June 03] Available at: https://urn.nsk.hr/urn:nbn:hr:168:052675

S. Podhraški, "Analiza biomedicinskih signala prikupljenih nosivim uređajima metodama strojnog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:052675

Metadata
TitleAnaliza biomedicinskih signala prikupljenih nosivim uređajima metodama strojnog učenja
Title (english)Machine learning-based analysis of biomedical signals acquired by wearable devices
AuthorSilvio Podhraški
MentorHrvoje Džapo (mentor)
Committee memberHrvoje Džapo (predsjednik povjerenstva)
Committee memberMario Cifrek (član povjerenstva)
Committee memberŽeljka Lučev Vasić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-02-21, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U radu je opisan i implementiran proces korištenja metoda strojnog učenja prilikom analize biomedicinskih signala, s fokusom na fotopletizmografski signal (PPG). Navedeni proces obuhvaća predobradu biomedicinskih signala, ekstrakciju i odabir značajki signala, korištenje modela strojnog učenja te evaluaciju modela. Cilj rada je istražiti uspješnost klasifikacije ili grupiranja pojedinih epoha signala s obzirom na razinu šuma, za što se koriste metode strojnoga učenja. Predobrada biomedicinskih signala obuhvaća korištenje pojasnopropusnog Butterworth filtra za uklanjanje šuma, podjelu signala na prozore, te označavanje signala korištenjem klasifikatora baziranog na pravilima. Izlučuju se statističke i domenske značajke. Provodi se odabir značajki radi postizanja boljih rezultata klasifikacije i grupiranja. Za odabir značajki, tj. transformaciju značajki, korišten je mRMR algoritam i Lasso regularizacija, odnosno PCA. Korišteni su modeli nadziranog i nenadziranog strojnog učenja implementirani u scikit-learn biblioteci. Implementacija je izvedena u programskom jeziku Python. Provedena je usporedna analiza performansi različitih modela s obzirom na odabrane značajke. Uspoređuje se učinkovitost klasifikacije modela s obzirom na definirane mjere uspješnosti modela (F1 rezultat, AUC). Na kraju se zaključuje o primjenjivosti strojnog učenja kao jedne od mogućnosti analize biomedicinskih signala.
Abstract (english)
This paper describes and implements the process of using machine learning techniques for biomedical signal analysis, with an emphasis on PPG signals. The process includes biomedical signal preprocessing, feature extraction and selection, using machine learning models and model evaluation. The goal is to explore whether the machine learning algorithms are successful in the classification and grouping of the signal regarding the presented noise in the signal. The preprocessing step includes using the band-pass Butterworth filter, windowing, and labeling using a rule-based classifier. Statistical and domain features are extracted. Feature selection (mRMR, Lasso regularization) and feature transformation (PCA) are also used to improve the classification results. Supervised and unsupervised learning models are used, which are implemented in the scikit-learn library. The Python programming language is used for implementation. Based on selected features, the performance analysis of multiple models is carried out. The paper analyzes model performance regarding various evaluation metrics (F1 score, AUC). In the end, the paper concludes the usability of machine learning for biomedical signal analysis.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:052675
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-07-13 10:28:53