ECG classification efficient modeling with artificial bee colony optimization data augmentation and attention mechanism

Mingming Zhang; Huiyuan Jin; Ying Yang; Mingming Zhang; Huiyuan Jin; Ying Yang

doi:10.3934/mbe.2024203

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 3: 4626-4647. doi: 10.3934/mbe.2024203

Previous Article Next Article

Research article Special Issues

ECG classification efficient modeling with artificial bee colony optimization data augmentation and attention mechanism

School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, 100124, China

Received: 20 December 2023 Revised: 03 February 2024 Accepted: 12 February 2024 Published: 28 February 2024

In addressing the key issues of the data imbalance within ECG signals and modeling optimization, we employed the TimeGAN network and a local attention mechanism based on the artificial bee colony optimization algorithm to enhance the performance and accuracy of ECG modeling. Initially, the TimeGAN network was introduced to rectify data imbalance and create a balanced dataset. Furthermore, the artificial bee colony algorithm autonomously searched hyperparameter configurations by minimizing Wasserstein distance. Control experiments revealed that data augmentation significantly boosted classification accuracy to 99.51%, effectively addressing challenges with unbalanced datasets. Moreover, to overcome bottlenecks in the existing network, the introduction of the Efficient network was adopted to enhance the performance of modeling optimized with attention mechanisms. Experimental results demonstrated that this integrated approach achieved an impressive overall accuracy of 99.70% and an average positive prediction rate of 99.44%, successfully addressing challenges in ECG signal identification, classification, and diagnosis.
- ECG modeling,
- data enhancement,
- TimeGAN network,
- artificial bee colony optimization,
- relative position matrix,
- local attention mechanism,
- Ca-EfficientNet
Citation: Mingming Zhang, Huiyuan Jin, Ying Yang. ECG classification efficient modeling with artificial bee colony optimization data augmentation and attention mechanism[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 4626-4647. doi: 10.3934/mbe.2024203

Related Papers:

Abstract

In addressing the key issues of the data imbalance within ECG signals and modeling optimization, we employed the TimeGAN network and a local attention mechanism based on the artificial bee colony optimization algorithm to enhance the performance and accuracy of ECG modeling. Initially, the TimeGAN network was introduced to rectify data imbalance and create a balanced dataset. Furthermore, the artificial bee colony algorithm autonomously searched hyperparameter configurations by minimizing Wasserstein distance. Control experiments revealed that data augmentation significantly boosted classification accuracy to 99.51%, effectively addressing challenges with unbalanced datasets. Moreover, to overcome bottlenecks in the existing network, the introduction of the Efficient network was adopted to enhance the performance of modeling optimized with attention mechanisms. Experimental results demonstrated that this integrated approach achieved an impressive overall accuracy of 99.70% and an average positive prediction rate of 99.44%, successfully addressing challenges in ECG signal identification, classification, and diagnosis.

References

[1]	R. G. Kumar, Y. S. Kumaraswamy, Investigating cardiac arrhythmia in ECG using random forest classification, J. Int. J. Computer Appl., 37 (2012), 31–34. https://doi.org/10.5120/4599-6557 doi: 10.5120/4599-6557
[2]	M. Zhang. H. Jin. B. Zheng. W. Luo, Deep learning modeling of cardiac arrhythmia classification on information feature fusion image with attention mechanism, Entropy, 25 (2023), 1264. https://doi.org/10.3390/e25091264 doi: 10.3390/e25091264
[3]	Q. Qin, J. Li, L. Zhang, C.Y. Liu, Combining low-dimensional wavelet features and support vector machine for arrhythmia beat classification, Sci. Rep., 7 (2017), 6067. https://doi.org/10.1038/s41598-017-06596-z doi: 10.1038/s41598-017-06596-z
[4]	C. U. Kumari, A. S. D. Murthy, B. L. Prasanna, M. P. P. Reddy, A. K. Panigrahy, An automated detection of heart arrhythmias using machine learning technique: SVM, Mater. Today Proceed., 45 (2021), 1393–1398. https://doi.org/10.1016/j.matpr.2020.07.088 doi: 10.1016/j.matpr.2020.07.088
[5]	M. R. Ekta, R. Devi, Arrhythmia discrimination using support vector machine, in 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC), 2017,283–287. https://doi.org/10.1109/ISPCC.2017.8269690
[6]	Ö. Yıldırım, P. Pławiak, R. S. Tan, U. Rajendra Acharya, Arrhythmia detection using deep convolutional neural network with long duration ECG signals, J. Comput. Biol. Med., 102 (2018), 411–420. https://doi.org/10.1016/j.compbiomed.2018.09.009 doi: 10.1016/j.compbiomed.2018.09.009
[7]	U. R. Acharya, H. Fujita, S. L. Oh, Y. Hagiwara, J. H. Tan, M. Adam, R. S. Tan, Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals, Appl. Intell., 49 (2019), 16–27.
[8]	X. Fan, Q. Yao, Y. Cai, F. Miao, F. Sun, Y. Li, Multiscaled fusion of deep convolutional neural networks for screening atrial fibrillation from single lead short ECG recordings, IEEE J. Biomed. Health Inform., 22 (2018), 1744–1753. https://doi.org/10.1007/s10489-018-1179-1 doi: 10.1007/s10489-018-1179-1
[9]	U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, M. Adam, A. Gertych, R. San Tan, A deep convolutional neural network model to classify heartbeats, Comput. Biol. Med., 89 (2017), 389–396. https://doi.org/10.1016/j.compbiomed.2017.08.022 doi: 10.1016/j.compbiomed.2017.08.022
[10]	J. Liu, M. Fu, S. Zhang, Application of convolutional neural network in automatic classification of arrhythmia, in Proceedings of the ACM Turing Celebration Conference-China, (2019), 1–8. https://doi.org/10.1145/3321408.3326660
[11]	M. Porumb, E. Iadanza, S. Massaro, L. Pecchia, A convolutional neural network approach to detect congestive heart failure, J. Biomed. Signal Process. Control, 55 (2020), 101–597. https://doi.org/10.1016/j.bspc.2019.101597 doi: 10.1016/j.bspc.2019.101597
[12]	H. Sun, Research on Automatic Detection Algorithm of Atrial Fibrillation Based on Feature Fusion, Ph.D thesis, Shandong University in ShanDong, China, 2021.
[13]	T. Zheng, Research on ECG Data Augmentation Method Based on Generative Adversarial Networks, Ph.D thesis, Jiangxi University of Finance and Economics in Jiangxi, China, 2021.
[14]	Y. Wang, Research on ECG Data Augmentation Algorithm Based on Generative Adversarial Neural Network, Ph.D thesis, Beijing University of Posts and Telecommunications in Beijing, China, 2020.
[15]	P. Wang, B. Hou, S. Shao, R. Yan, ECG arrhythmias detection using auxiliary classifier generative adversarial network and residual network, IEEE Access, 7 (2019), 100910–100922. https://doi.org/10.1109/ACCESS.2019.2930882 doi: 10.1109/ACCESS.2019.2930882
[16]	J. Yoon, D. Jarrett, M. Van der Schaar, Time-series generative adversarial networks, Adv. Neural Inform. Process. Syst., 32 (2019). https://dl.acm.org/doi/abs/10.5555/3454287.3454781
[17]	M. Tan, Q. Le, Efficientnet: Rethinking model scaling for convolutional neural networks, in Proceedings of the International conference on machine learning, (2019), 6105–6114. https://doi.org/10.48550/arXiv.1905.11946
[18]	Q. B. Hou, D. Q. Zhou, J. S. Feng, Coordinate attention for efficient mobile network design, in Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2021), 13708–13717. https://doi.org/10.1109/CVPR46437.2021.01350
[19]	X. F. Zha, F. Yang, Y. N. Wu, Y. Liu, S. F. Yuan, ECG classification based on transfer learning and deep convolution neural network, Chin. J. Med. Phys, 35 (2018), 1307–1312. https://doi.org/10.3969/j.issn.1005-202X.2018.11.013 doi: 10.3969/j.issn.1005-202X.2018.11.013
[20]	J. Wang, M. Shi, X. Zhang, Research on classification of arrhythmia based on EMD and ApEn feature extraction, J. Instrum. Meas., 37 (2016), 168–173.
[21]	S. L. Oh, E. Y. Ng, S. T. Ru, A. U. R, Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats, Comput. Biol. Med., 102 (2018), 278–287. https://doi.org/10.1016/j.compbiomed.2018.06.002 doi: 10.1016/j.compbiomed.2018.06.002
[22]	K. N. V. P. Rajesh, R. Dhuli, Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine, Comput. Biol. Med., 87 (2017), 271–284. https://doi.org/10.1016/j.compbiomed.2017.06.006 doi: 10.1016/j.compbiomed.2017.06.006
[23]	D. Li, M. Jiang, M. Li, W. H, R. Xu, A floating offshore platform motion forecasting approach based on EEMD hybrid ConvLSTM and chaotic quantum ALO, Appl. Soft Comput., 144 (2023), 110–487. https://doi.org/10.1016/j.asoc.2023.110487 doi: 10.1016/j.asoc.2023.110487

Reader Comments

Your name:*

Email:*
© 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)