Towards the classification of heart sounds based on convolutional deep neural network
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Background and objective
Heart sound contains various important quantities that help early detection of heart diseases. Many methods have been proposed so far where various signal-processing techniques have been used on heart sounds for heart disease detection.
In this paper, a methodology is introduced for heart disease detection based on heart sounds. The proposed method employs three successive stages, such as spectrogram generation, deep feature extraction, and classification. In the spectrogram generation stage, the heart sounds are converted to spectrogram images by using time–frequency transformation.
The deep features are extracted from three different pre-trained convolutional neural network models such as AlexNet, VGG16, and VGG19. Support vector machine classifier is used in the third stage of the proposed method. The proposed method is evaluated on two datasets, which are taken from The Classifying Heart Sounds Challenge.
The obtained results are compared with some of the existing methods. The comparisons show that the proposed method outperformed.
KeywordsHeart sound Convolutional neural network (CNN) Modeling Classification
All authors have contributed equally in all the areas such as implementation, paper writing, and experimentations.
Compliance with ethical standards
Conflict of interest
The authors of the paper declare that they have no conflict of interest.
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