Review — Classification of heart sound signals using a novel deep WaveNet model
WaveNet for 5-Class Heart Sound Classification
Classification of heart sound signals using a novel deep WaveNet model
WaveNet, by Ngee Ann Polytechnic, Singapore University of Social Sciences, National Heart Centre, Columbia University, Kumamoto University, Asia University
2020 Elsevier J. CMPB, Over 110 Citations ( @ Medium)Heart Sound Classification
2013 [PASCAL] 2018 [RNN Variants] 2023 [2LSTM+3FC, 3CONV+2FC] [NRC-Net]
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- A novel in-house developed deep WaveNet model for automated classification of five types of heart sounds. The model is developed using a total of 1000 PCG recordings belonging to five classes with 200 recordings in each class.
Outline
- Heart Sound Preliminaries
- WaveNet Model
- Results
1. Heart Sound Preliminaries
1.1. PCG Signals for Different Classes
- Different types of Heart valve diseases (HVDs) such as aortic stenosis (AS), mitral stenosis (MS), mitral regurgitation (MR) and mitral valve prolapse (MVP) can be diagnosed using PCG signals.
- Yet, Visual screening of the PCG signal is time-consuming and prone to error.
Thus, WaveNet deep learning model is proposed using PCG signals for the categorization of heart sounds in HVD.
- Fig. 2a-2b represents the normal and pathological images of aortic stenosis (AS).
- Fig. 3a–3d represents the normal and pathological images of mitral valve disease (mitral stenosis (MS), mitral regurgitation (MR) and mitral valve prolapse (MVP)).
1.2. Summary of Prior Arts
- Table 1 presents a summary of studies that employ deep learning models for automated categorization of heart sounds in HVD.
2. WaveNet Model
2.1. Dataset & Preprocessing
- PCG signals used in this study were obtained from a public database [7]. A total of 1000 PCG recordings were obtained from five different classes with 200 recordings each. The different classes of signals were N, AS, MS, MR and MVP.
- Each recording was sampled at a frequency of 8000 Hz. Each audio sound wave was normalized between −1 to 1 to ensure that the data shared a common scale for easier analysis.
- As the samples had varying length, they were zero-padded to a 31,943 discrete sample point length for consistency.
- Dataset Link: https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-
2.2. Model Architecture
- The proposed WaveNet model consists of 6 residual blocks.
- The residual block is different from the one in ResNet, as shown above. It uses dilated two parallel 1D convolution (DeepLab or DilatedNet).
- Tanh and sigmoid are used respectively at each branch then mulitplied:
- Then it is followed by another 1×1 convolution.
- After 6 residual blocks, all signals from different residual blocks are added together, pass through two 1×1 convolutions, then two fully connected layers, with the use of ReLU and Dropout.
- Finally, softmax is used at output layer.
- It was trained using 3 epochs, with a batch size of 3.
- It took less than a millisecond to classify each sample.
3. Results
The 5 classes were classified with accuracies of above 95%.
- Authors claim that, to the best of their knowledge, the first to report on the 5-class problem for heart sound classification using a deep WaveNet model
The validation accuracy has increased steeply from epochs 1 to 5, with minimal changes and gradual improvement from epoch 5 onwards.
It is noteworthy that the misclassification rates of N, MVP, MS, MR and AS are 6%, 11%, 4%, 11%, and 6%, respectively.
