TY - GEN
T1 - Transferring Cross-Corpus Knowledge
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
AU - Koike, Tomoya
AU - Qian, Kun
AU - Schuller, Bjorn W.
AU - Yamamoto, Yoshiharu
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Human auscultation has been regarded as a cheap, convenient and efficient method for the diagnosis of cardiovascular diseases. Nevertheless, training professional auscultation skills needs tremendous efforts and is time-consuming. Computer audition (CA) that leverages the power of advanced machine learning and signal processing technologies has increasingly attracted contributions to the field of automatic heart sound classification. While previous studies have shown promising results in CA based heart sound classification with the 'shuffle split' method, machine learning for heart sound classification decreases in accuracy with a cross-corpus test dataset. We investigate this problem with a cross-corpus evaluation using the PhysioNet CinC Challenge 2016 Dataset and propose a new combination of data augmentation techniques that leads to a CNN robust for such cross-corpus evaluation. Compared with the baseline, which is given without augmentation, our data augmentation techniques combined improve by 20.0 % the sensitivity and by 7.9 % the specificity on average across 6 databases, which is a significant difference on 4 out of these (p <.05 by one-tailed z-test).
AB - Human auscultation has been regarded as a cheap, convenient and efficient method for the diagnosis of cardiovascular diseases. Nevertheless, training professional auscultation skills needs tremendous efforts and is time-consuming. Computer audition (CA) that leverages the power of advanced machine learning and signal processing technologies has increasingly attracted contributions to the field of automatic heart sound classification. While previous studies have shown promising results in CA based heart sound classification with the 'shuffle split' method, machine learning for heart sound classification decreases in accuracy with a cross-corpus test dataset. We investigate this problem with a cross-corpus evaluation using the PhysioNet CinC Challenge 2016 Dataset and propose a new combination of data augmentation techniques that leads to a CNN robust for such cross-corpus evaluation. Compared with the baseline, which is given without augmentation, our data augmentation techniques combined improve by 20.0 % the sensitivity and by 7.9 % the specificity on average across 6 databases, which is a significant difference on 4 out of these (p <.05 by one-tailed z-test).
UR - http://www.scopus.com/inward/record.url?scp=85122494837&partnerID=8YFLogxK
U2 - 10.1109/EMBC46164.2021.9629714
DO - 10.1109/EMBC46164.2021.9629714
M3 - Conference contribution
C2 - 34891674
AN - SCOPUS:85122494837
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1976
EP - 1979
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 November 2021 through 5 November 2021
ER -
