TY - GEN
T1 - Heart Sound Classification based on Fractional Fourier Transformation Entropy
AU - Tan, Yang
AU - Wang, Zhihua
AU - Qian, Kun
AU - Hu, Bin
AU - Zhao, Shiliang
AU - Schuller, Bjorn W.
AU - Yamamoto, Yoshiharu
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Automatic classification of heart sounds has been studied for many years, because computer-aided auscultation of heart sounds can help doctors make a preliminary diagnosis. We propose a classification method for heart sounds that uses fractional Fourier transformation entropy (FRFE) as the features and a support vector machine (SVM) as the classification model. The process of the whole method is cutting of heart sounds, feature extraction, and classification. We compare FRFE of different signal orders, and finally evaluate fused features of multiple orders according to the better classification results. These fused features are used as the input of a SVM, a k-nearest neighbour (KNN), and a Naive bayes classifier (NBC) to compare the most suitable classifiers. Finally, we consider the fused features that reflect both the time and the frequency domain to achieve a better classification performance.
AB - Automatic classification of heart sounds has been studied for many years, because computer-aided auscultation of heart sounds can help doctors make a preliminary diagnosis. We propose a classification method for heart sounds that uses fractional Fourier transformation entropy (FRFE) as the features and a support vector machine (SVM) as the classification model. The process of the whole method is cutting of heart sounds, feature extraction, and classification. We compare FRFE of different signal orders, and finally evaluate fused features of multiple orders according to the better classification results. These fused features are used as the input of a SVM, a k-nearest neighbour (KNN), and a Naive bayes classifier (NBC) to compare the most suitable classifiers. Finally, we consider the fused features that reflect both the time and the frequency domain to achieve a better classification performance.
KW - Classification
KW - Computer Audition
KW - Fractional Fourier Entropy (FRFE)
KW - Heart Sounds
UR - http://www.scopus.com/inward/record.url?scp=85129160017&partnerID=8YFLogxK
U2 - 10.1109/LifeTech53646.2022.9754781
DO - 10.1109/LifeTech53646.2022.9754781
M3 - Conference contribution
AN - SCOPUS:85129160017
T3 - LifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
SP - 588
EP - 589
BT - LifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022
Y2 - 7 March 2022 through 9 March 2022
ER -