TY - GEN
T1 - Karar Destek Sistemi İçin Mel-Frekans Kepstral Tabanli Kalp Ses Sinyali Bölütleme Çalişmasi
AU - Celebi, Gulsen
AU - Sozeri, Goksel
AU - Yilmaz, Atila
AU - Katircioglu, Deniz
AU - Okutucu, Sercan
AU - Sayin, Begum Yetis
AU - Aksoy, Hakan
AU - Oto, Ali
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/27
Y1 - 2017/6/27
N2 - The classification of heart diseases depends on the correct identification of S1 and S2 segments. Without the ECG reference signal, segmentation methods become naturally more complicated. In the hospital environment, the heart sounds collected from the patients through the stethoscope carry unrequired environmental sounds such as ambient noise, speech, wheezing, and friction. Besides, depending on the heart condition, noise like murmur is also included in these heart sounds. Discrete Wavelet Transform and Mel-Frequency Cepstral Coefficient (MFCC) have been used as a hybrid solution for the filtering of the noise content of basic heart sounds. In order to determine S1-S2 locations, heart rate and systolic time intervals were predicted by using signal autocorrelation. As a result of this proposed algorithm, S1 and S2 sounds were detected with 98.19% precision and 98.52% recall for normal heart sounds, while S1 and S2 were detected with precision of 94.31% and recall of 96.92% for abnormal heart sounds.
AB - The classification of heart diseases depends on the correct identification of S1 and S2 segments. Without the ECG reference signal, segmentation methods become naturally more complicated. In the hospital environment, the heart sounds collected from the patients through the stethoscope carry unrequired environmental sounds such as ambient noise, speech, wheezing, and friction. Besides, depending on the heart condition, noise like murmur is also included in these heart sounds. Discrete Wavelet Transform and Mel-Frequency Cepstral Coefficient (MFCC) have been used as a hybrid solution for the filtering of the noise content of basic heart sounds. In order to determine S1-S2 locations, heart rate and systolic time intervals were predicted by using signal autocorrelation. As a result of this proposed algorithm, S1 and S2 sounds were detected with 98.19% precision and 98.52% recall for normal heart sounds, while S1 and S2 were detected with precision of 94.31% and recall of 96.92% for abnormal heart sounds.
KW - Mel-Frequency Cepstral Coefficient
KW - Phonocardiogram Signal
KW - Segmentation of heart sounds
KW - Wavelet Trasnform
UR - https://www.scopus.com/pages/publications/85026326509
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=performanshacettepe&SrcAuth=WosAPI&KeyUT=WOS:000413813100293&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1109/SIU.2017.7960430
DO - 10.1109/SIU.2017.7960430
M3 - Konferans katkısı
AN - SCOPUS:85026326509
T3 - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017
BT - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th Signal Processing and Communications Applications Conference, SIU 2017
Y2 - 15 May 2017 through 18 May 2017
ER -