20.500.12556/RUL-72082 Zaznavanje srčnega šuma v fonokardiogramih Detacting heart murmur in phonocardiograms Srcna avskultacija je najstarejsa neinvazivna metoda za odkrivanje bolezni srcnih zaklopk. V diplomi smo se osredotocili na analizo fonokardiogramov s pomocjo metod digitalnega procesiranja signalov ter metod umetne inte- ligence za klasikacijo. Signal pridobljen iz elektronskega stetoskopa smo razdelili v segmente, kjer en segment ustreza enemu kardialnemu ciklu. Nad segmentom smo izracunali MFCC znacilke, katere smo uporabili kot vhod algoritmom strojnega ucenja v programskem sistemu Orange. Ciljni kla- sikacijski razred je stanje pacienta. Locevali smo med fonokardiogrami s prisotnim sumom in brez. Najboljso klasikacijsko tocnost smo dosegli z na- ivnim Bayesovim klasikatorjem. Dosegli smo 92,4 % tocnost pri pricakovani tocnosti vecinskega klasikatorja 75,2 %. Dosegli smo 76,9 % senzitivnost in 95,4 % specicnost, prav tako z naivnim Bayesovim klasikatorjem. Po nasem mnenju bi lahko bil tak sistem pomoc zdravnikom pri diagnosticiranju bolezni srcnih zaklopk. V teoreticnem delu diplome smo na kratko opisali algoritme, katere smo uporabljali ter kaksne so naravne omejitve pri delu s procesiranjem signalov. Heart auscultation is one of the oldest non-invasive method for detection valvular heart disease. In thesis we have focused on the analysis of phono- cardiograms with digital signal processing methods and methods of articial intelligence for classication. We have divided the signal obtained from elec- tronic stethoscope in to segments where one of the segment corresponds to one cardiac cycle. After that we calculate MFCC features on one of the segments. The features serve as an input to machine learning algorithms in system Orange. Target classication class is the condition of a patient. We have distinguish the phonocardiograms with a heart murmur and without it. The best classication accuracy that we achieved is with naive Bayes classicator of 92.4 %. The expected accuracy of majority class was 75.2 %. The best achieved sensitivity and specicity was 76.9 % and 95.4 % respec- tively, also with naive Bayes classicator. In our opinion such system could be used by physicians to help diagnose heart valve diseases. In theoretical part of the thesis we have described algorithms that we used and what are the limitations of processing a signal. avskultatorni fenomeni bolezni srcnih zaklopk elektronski stetoskop MFCC digitalno procesiranje signalov auscultation phenomena heart valve disease electronic stethoscope MFCC digital signal processing true false false Slovenski jezik Angleški jezik Diplomsko delo/naloga 2015-08-24 12:05:05 2015-08-24 12:05:07 2022-08-01 15:20:20 0000-00-00 00:00:00 2015 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1970-01-01 16661 Kocuvan_Primoz_-_Zaznavanje_srcnega_suma_v_fonokardiogramih.pdf Kocuvan_Primoz_-_Zaznavanje_srcnega_suma_v_fonokardiogramih.pdf 1 E5EAD871FBA9465C05FA9A609CC1E7DE 8d6729fde82da9e2fdeb9628f9deab0a2f3b08305f56a68f6c7e7eff8d1d6e5e 56cbe960-a1ad-11eb-a523-00155dcfd717 20.500.12556/rul/a01a3391-53b3-46f7-b5db-900896751d56 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=72113 Fakulteta za računalništvo in informatiko 0 0 0