Zaznavanje srčnega šuma v fonokardiogramih

KOCUVAN, PRIMOŽ (author), Mramor Kosta, Nežka (mentor), Rozman, Robert (comentor)

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.

• Language: Slovenian
• Keywords: avskultatorni fenomeni, bolezni srcnih zaklopk, elektronski stetoskop, MFCC, digitalno procesiranje signalov
• Work type: Bachelor thesis/paper
• Organization: FRI - Faculty of Computer and Information Science
• Year: 2015
• PID: 20.500.12556/RUL-72082
• Publication date in RUL: 24.08.2015

Secondary language

• Language: English
• Title: Detacting heart murmur in phonocardiograms

Abstract

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.

• Keywords: auscultation phenomena, heart valve disease, electronic stethoscope, MFCC, digital signal processing