Universiti Teknologi Malaysia Institutional Repository

Short-segment heart sound classification using an ensemble of deep convolutional neural networks

Noman, Fuad and Ting, Chee-Ming and Salleh, Sh-Hussain and Ombao, Hernando (2019) Short-segment heart sound classification using an ensemble of deep convolutional neural networks. In: 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, 12 - 17 May 2019, Brighton, England.


Official URL: http://dx.doi.org/10.1109/ICASSP.2019.8682668


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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:convolutional neural network, ensemble classifiers, Heart sound classification
Subjects:Q Science > QH Natural history > QH301 Biology
Divisions:Biosciences and Medical Engineering
ID Code:96987
Deposited By: Widya Wahid
Deposited On:06 Sep 2022 16:06
Last Modified:06 Sep 2022 16:06

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