Brief Review — ResHNet: Spectrograms Based Efficient Heart Sounds Classification Using Stacked Residual Networks
ResHNet, Using Residual Blocks in ResNet
2 min readJan 7, 2024
ResHNet: Spectrograms Based Efficient Heart Sounds Classification Using Stacked Residual Networks
ResHNet, Institute for Infocomm Research, A*STAR; National University of Singapore; Chongqing Jiaotong University; YITU Tech
2019 BHI (Sik-Ho Tsang @ Medium)Heart Sound Classification
2013 … 2023 [2LSTM+3FC, 3CONV+2FC] [NRC-Net] [Log-MelSpectrum+Modified VGGNet] [CNN+BiGRU]
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- Spectrogram is used as input to train the proposed residual network based classifier, namely ResHNet, for identifying normal and abnormal heart sounds.
Outline
- ResHNet
- Results
1. ResHNet
1.1. Spectrogram
Heart sound is converted into spectrogram as CNN input.
1.2. ResHNet
Residual Block in ResNet is utilized to constructed ResHNet.
- ReLU is used.
- Batch Normalization is incorporated in the residual module.
- The initial convolution layer before reducing spatial dimensions will have a total of 64 filters. Then 3 sets of residual modules are stacked with three convolution layers in each module learning 32, 32 and 128 convolution filters respectively.
- Then another four sets of residual modules are stacked with three convolution layers in each module learning 64, 64 and 256 filters respectively after reducing the spatial dimensions.
- The same series of action is repeated for 6 sets of residual modules.
- Average Pooling is performed after the final spatial dimension reduction and on top of it a softmax classifier is applied to get the output.
2. Results
ResHNet obtains the highest Sensitivity, Specificity and Mean Accuracy (Score).
