An Electrocardiogram Signal Classification Algorithm Based on Improved Deep Residual Shrinkage Networks

GONG Yuxiao; GAO Shuping

doi:10.21656/1000-0887.440074

Volume 44 Issue 8

Aug. 2023

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GONG Yuxiao, GAO Shuping. An Electrocardiogram Signal Classification Algorithm Based on Improved Deep Residual Shrinkage Networks[J]. Applied Mathematics and Mechanics, 2023, 44(8): 977-988. doi: 10.21656/1000-0887.440074

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An Electrocardiogram Signal Classification Algorithm Based on Improved Deep Residual Shrinkage Networks

doi: 10.21656/1000-0887.440074

GONG Yuxiao,
GAO Shuping^,

School of Mathematics and Statistics, Xidian University, Xi'an 710126, P.R.China

Received Date: 2023-03-22
Rev Recd Date: 2023-06-19
Publish Date: 2023-08-01

Abstract

Abstract

The electrocardiogram (ECG) signal classification is a significant research topic in the healthcare field. Most existing methods could not effectively reduce the missed diagnosis rate of classification with small-size samples and tackle the complexity of preprocessing operations. An electrocardiogram signal classification algorithm based on the improved deep residual shrinkage networks was proposed, namely the DRSL algorithm. Firstly, the small-size classification samples were augmented with the synthetic minority over-sampling technique to solve the classification imbalance problem. Secondly, the spatial features were extracted by mean of the improved deep residual shrinkage networks, where the residual module can avoid overfitting caused by deepening of network layers, and the squeeze-and-excitation operation with soft threshold subnetwork can extract important local features and remove noises automatically. Then, the time features were extracted with the long short-term memory networks. Finally, the classification results were output with the fully connected neural networks. The experimental results on the MIT-BIH arrhythmia database show that, the proposed algorithm is superior to IDRSN, DRSN, GAN+2DCNN, CNN+LSTM_ATTENTION, SE-CNN-LSTM in terms of classification performances.
- ECG signal,
- synthetic minority over-sampling technique,
- deep residual shrinkage network,
- squeeze-and-excitation,
- long short-term memory network

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References(27)

References

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