Abstract
Phonocardiogram classification plays an important rule in the diagnosis of heart disease. It can be used in selecting a proper treatment to the patients. However, automated PCG classification has many issues. One of the important issues is the feature extraction process. It is difficult to extract relevant features from PCG signals due to some noises that corrupt the original signal. The noises are included murmur, intestine and breathing sounds. To overcome this problem, several works have been proposed such as performing segmentation on PCG signals before the feature extraction process, using many types of signal features including wavelet, mfcc, spectral, time-frequency and statistical features. These types of features experimentally affect the classification accuracy of PCG signals. This study proposes a feature fusion based using an autoencoder model in order to obtained new repfresentation features. The result shows that this approach provides a competitive result of PCG classification compare to those of the baseline methods.
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Acknowledgments
This research is supported by Internal Research Grant of Universitas YARSI Number: 0007.2/FTI/ST-PN.00/I/2019.
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Fathurahman, M., Rachmawati, U.A., Haryanti, S.C. (2020). Multi-modal Feature Based for Phonocardiogram Signal Classification Using Autoencoder. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_17
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DOI: https://doi.org/10.1007/978-3-030-36056-6_17
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