Brief Review — Towards the classification of heart sounds based on convolutional deep neural network
Towards the classification of heart sounds based on convolutional deep neural network
{AlexNet, VGG} + SVM, by Abant Izzet Baysal University
2019 Health Inf. Sci. Syst., Over 60 Citations (Sik-Ho Tsang @ Medium)2013 … 2018 [RNN Variants] [SVM, DNN, kNN] [LSTM] [Chakir JSVIP’18] 2023 [2LSTM+3FC, 3CONV+2FC] [NRC-Net]
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Outline
- Proposed Approach
- Results
1. Proposed Approach

The system is composed of 3 main components, such as image construction, feature extraction, and concatenation and feature classification.
1.1. Image Construction

The Short Time Fourier Transform (STFT) is used to construct the spectrogram images.
- Given x is the signal, F is the corresponding STFT.

- where ω(i) is the window function.
The magnitude squared of the STFT representation |F(n, ω)|² is called spectrogram.
1.2. Deep Feature Extraction
Pre-trained CNN models such as VGG16, VGG19, and AlexNet are used for feature extraction.
- Spectrogram images are the input for the feature extraction architecture. The images are resized to (224 × 224 × 3) for AlexNet and (227 × 227 × 3) for VGG16 and VGG19 models.
The feature vectors at FC6 of pretrained models are concatenated.
1.3. Classification
The SVM classifier with homogenous mapping and LIBLINEAR library with the L2-regularised L2-loss dual solver is considered.
2. Results
2.1. PASCAL Dataset

PASCAL datasets A and B are used.
2.2. Dataset A Results

Except the Normal category, VGG16 produces the highest precision scores for all categories.
Furthermore, AlexNet–VGG16 also produces the highest precision scores for Normal, Extra Heart Sound, and Artifact categories.
2.3. Dataset B Results
