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    Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

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    Type
    Conference Paper
    Authors
    Noman, Fuad
    Ting, Chee-Ming
    Salleh, Sh-Hussain
    Ombao, Hernando cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics
    Statistics Program
    Date
    2019-04-17
    Permanent link to this record
    http://hdl.handle.net/10754/655968
    
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    Abstract
    This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of two-dimensional time-frequency feature maps based on Mel-frequency cepstral coefficients. We further develop a time-frequency CNN ensemble (TF-ECNN) combining the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities. On the large PhysioNet CinC challenge 2016 database, the proposed CNN models outperformed traditional classifiers based on support vector machine and hidden Markov models with various hand-crafted time- and frequency-domain features. Best classification scores with 89.22% accuracy and 89.94% sensitivity were achieved by the ECNN, and 91.55% specificity and 88.82% modified accuracy by the 2D-CNN alone on the test set.
    Citation
    Noman, F., Ting, C.-M., Salleh, S.-H., & Ombao, H. (2019). Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp.2019.8682668
    Publisher
    IEEE
    Conference/Event name
    ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
    DOI
    10.1109/ICASSP.2019.8682668
    arXiv
    arXiv:1810.11573
    Additional Links
    https://ieeexplore.ieee.org/document/8682668/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8682668
    http://arxiv.org/pdf/1810.11573
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICASSP.2019.8682668
    Scopus Count
    Collections
    Conference Papers; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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