Arrhythmia Detection Based on Wavelet Decomposition and 1D-GoogLeNet

Shuying YANG; Binbin GUI; Shengyong CHEN

doi:10.11999/JEIT200774

Volume 43 Issue 10

Oct. 2021

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Shuying YANG, Binbin GUI, Shengyong CHEN. Arrhythmia Detection Based on Wavelet Decomposition and 1D-GoogLeNet[J]. Journal of Electronics & Information Technology, 2021, 43(10): 3018-3027. doi: 10.11999/JEIT200774

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Shuying YANG, Binbin GUI, Shengyong CHEN. Arrhythmia Detection Based on Wavelet Decomposition and 1D-GoogLeNet[J]. Journal of Electronics & Information Technology, 2021, 43(10): 3018-3027. doi: 10.11999/JEIT200774

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Arrhythmia Detection Based on Wavelet Decomposition and 1D-GoogLeNet

doi: 10.11999/JEIT200774

1.
School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
2.
Key Laboratory of Computer Vision and Systems, Ministry of Education, Tianjin 300384, China

Funds: The National Natural Science Foundation of China(U1509207), Tianjin Virtual Simulation Experiment Teaching Construction Project Fund(JMEC[2019]69)

Received Date: 2020-08-31
Rev Recd Date: 2021-03-23

Available Online: 2021-04-14

Publish Date: 2021-10-18

Abstract

Abstract

The accurate classification of ElectroCardioGram (ECG) signals is essential for the automatic diagnosis of heart disease. In order to realize the intelligent classification of arrhythmia, an accurate classification method based on wavelet decomposition and 1D-GoogLeNet is proposed. In this method, Db6 wavelet is used to decompose the ECG signal in eight levels to obtain multi-dimensional data containing both time domain information and frequency domain information. Subsequently, Decomposed samples are used as input to 1D-GoogLeNet to train the model. In the proposed 1D-GoogLeNet model, using Inception's excellent performance in image feature extraction, the two-dimensional convolution is transformed into one-dimensional convolution to learn the features of ECG, and the structure of each Inception is simplified, and the model parameters are reduced. The deep learning classifier proposed in this paper can effectively alleviate the problems of low computational efficiency, difficulty in convergence and model degradation. In the experiment, the MIT-BIH arrhythmia dataset is used to test the performance of the proposed model. The experiment compares the detection results when different decomposition component combinations are used as input. When the input data is combined by {d2-d7}, the proposed 1D-GoogLeNet model can achieve an average accuracy of 96.58%. In addition, the performance of the model and the simple one-dimensional GoogLeNet without structural optimization on the data set is compared. The accuracy of the former is 4.7% higher than the latter, and the training efficiency is increased by 118%.
- Classification of arrhythmia,
- Wavelet decomposition,
- Inception,
- Convolutional Neural Network(CNN)

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References(31)

References

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