Automated Detection of Arrhythmia in ECG Signals using CNN
Keywords:
Cardiovascular Disease, Electro Cardio Gram, MIT-BIH, Convolution Neural Network Based Continual Normalization Classifier.Abstract
In this world, around 31% of the deaths are commonly caused because of cardiovascular diseases. Around 80% of sudden deaths occur due to cardiac arrhythmias and heart diseases. The mortality rate has increased for cardiac disease and therefore early heart disease detection is significant to preclude patients from dying. At the initial phase, the heart disease is detected by analyzing abnormal heartbeats. The existing models failed to select the features before performing the extraction of features. The developed model examined various databases to surpass the overfitting issue. Therefore, in the present research work, the CNN based Continual Normalization (CN) classifier is used to speed up the training to a higher learning rate to enable simpler learning for the standard deviation of the neurons' output. The extracted features were used to classify ECG signals into 5 important classes named as N, S, V, F & Q which denote the kinds of arrhythmia. The findings revealed that the proposed CNN based Continual Normalization technique obtained an accuracy of 99.2 % which is better when compared with the existing research namely the Dual Fully Connected Neural Network that obtained 93.4 % of accuracy, and the Optimization-Enabled Deep Convolutional Neural Network that accomplished 93.19 % of accuracy.
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S. C. Mohonta, M. A. Motin, and D. K. Kumar, “Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model,” Sens. Bio-Sens. Res., vol. 37, p. 100502, Aug. 2022.
M. Hammad, A. A. Abd El-Latif, A. Hussain, F. E. Abd El-Samie, B. B. Gupta, H. Ugail, and A. Sedik, “Deep learning models for arrhythmia detection in IoT healthcare applications,” Comput. Electr. Eng., vol. 100, p. 108011, May 2022.
A. Çınar and S. A. Tuncer, "Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks," Comput. Methods Biomech. Biomed. Eng., vol. 24, no. 2, pp. 203–214, Feb. 2021.
C. Wang and Y. Yang, "Nearest Neighbor with Double Neighborhoods Algorithm for Imbalanced Classification," IAENG International Journal of Applied Mathematics, vol. 50, no. 1, pp. 147–159, Mar. 2020.
J. Zhu, T. Xue, P. Pei, X. Chen, H. Gao, and Q. Liu, "The Synchronization Ability of a Class of Multi-layer Coupled Networks," IAENG International Journal of Applied Mathematics, vol. 53, no.1, pp. 209–216, Mar. 2023.
T. H. Pham, V. Sree, J. Mapes, S. Dua, O. S. Lih, J. E. W. Koh, E. J. Ciaccio, and U. R. Acharya, “A novel machine learning framework for automated detection of arrhythmias in ECG segments,” J. Ambient Intell. Hum. Comput., vol. 12, no. 11, pp. 10145–10162, Nov. 2021.
Y. Guan, Y. An, J. Xu, N. Liu, and J. Wang, “HA-ResNet: Residual neural network with hidden attention for ECG arrhythmia detection using two-dimensional signal,” IEEE/ACM Trans. Comput. Biol. Bioinf., Aug. 2022.
K. L. Lew, K. S. Sim, S. C. Tan, and F. S. Abas, "Biofeedback Upper Limb Assessment Using Electroencephalogram, Electromyographic and Electrocardiographic with Machine Learning in Signal Classification," Engineering Letters, vol. 30, no. 3, pp. 935–947, Sep. 2022.
S. Śmigiel, K. Pałczyński, and D. Ledziński, “ECG Signal Classification Using Deep Learning Techniques Based on the PTB-XL Dataset,” Entropy, vol. 23, no. 9, p. 1121, Aug. 2021.
C. S. Virgeniya and E. Ramaraj, "A novel deep learning based gated recurrent unit with extreme learning machine for electrocardiogram (ECG) signal recognition," Biomed. Signal Process. Control, vol. 68, p. 102779, Jul. 2021.
P. Jiang, R. Guo, R. Liang, Y. Xie, and C. Zou, "Convolutional-Recurrent Neural Network with the Tensor Fusion Mechanism for Acoustic Scene Classification," Engineering Letters, vol. 30, no. 4, pp. 1470–1474, Dec. 2022.
A. A. Syed, Y. Heryadi, Lukas, and A. Wibowo, "A Comparison of Machine Learning Classifiers on Laptop Products Classification Task," Lecture Notes in Engineering and Computer Science: Proceedings of The International MultiConference of Engineers and Computer Scientists 2021, 20-22 October, 2021, Hong Kong, pp. 104–110.
Y. Li, R. Qian, and K. Li, “Inter-patient arrhythmia classification with improved deep residual convolutional neural network,” Comput. Methods Programs Biomed., vol. 214, p. 106582, Feb. 2022.
S. Jamil and M. Rahman, “A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia,” J. Imaging, vol. 8, no. 3, p. 70, Mar. 2022.
A. O. Aseeri, “Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals,” Computers, vol. 10, no. 6, p. 82, Jun. 2021.
H. Wang, H. Shi, K. Lin, C. Qin, L. Zhao, Y. Huang, and C. Liu, “A high-precision arrhythmia classification method based on dual fully connected neural network,” Biomed. Signal Process. Control, vol. 58, p. 101874, Apr. 2020.
A. K. Sangaiah, M. Arumugam, and G. B. Bian, “An intelligent learning approach for improving ECG signal classification and arrhythmia analysis,” Artif. Intell. Med., vol. 103, p. 101788, Mar. 2020.
D. K. Atal and M. Singh, “Arrhythmia Classification with ECG signals based on the Optimization-Enabled Deep Convolutional Neural Network,” Comput. Methods Programs Biomed., vol. 196, p. 105607, Nov. 2020.
B. M. Mathunjwa, Y. T. Lin, C. H. Lin, M. F. Abbod, and J. S. Shieh, “ECG arrhythmia classification by using a recurrence plot and convolutional neural network,” Biomed. Signal Process. Control, vol. 64, p. 102262, Feb. 2021.
J. Huang, B. Chen, B. Yao, and W. He, "ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network," IEEE Access, vol. 7, pp. 92871–92880, Jul. 2019.
S. Kuila, N. Dhanda, and S. Joardar, “ECG signal classification to detect heart arrhythmia using ELM and CNN,” Multimedia Tools Appl., vol. 82, no. 19, pp. 29857-29881, Aug. 2023.
S. Kumar, A. Mallik, A. Kumar, J. Del Ser, and G. Yang, “Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals,” Comput. Biol. Med., vol. 153, p. 106511, Feb. 2023.
H. Yin, P. Yang, and P. Li, “Mitigating forgetting in online continual learning with neuron calibration,” Advances in Neural Information Processing Systems34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, 2021, pp. 10260–10272.
L. Xu, M. Xu, Z. Ma, K. Wang, T.-P. Jung, and D. Ming, "Enhancing transfer performance across datasets for brain-computer interfaces using a combination of alignment strategies and adaptive batch normalization," J. Neural Eng., vol. 18, no. 4, p. 0460e5, Aug. 2021.
Y. Zhang, S. Liu, Z. He, Y. Zhang, and C. Wang, "A CNN Model for Cardiac Arrhythmias Classification Based on Individual ECG Signals," Cardiovasc. Eng. Technol., vol. 13, no. 4, pp. 548–557, Aug. 2022.
A. Ullah, S. ur Rehman, S. Tu, R. M. Mehmood, Fawad, and M. Ehatisham-Ul-Haq, "A hybrid deep CNN model for abnormal arrhythmia detection based on cardiac ECG signal," Sensors, vol. 21, no. 3, p. 951, Feb. 2021.
O. Cheikhrouhou, R. Mahmud, R. Zouari, M. Ibrahim, A. Zaguia, and T. N. Gia, "One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments," IEEE Access, vol. 9, pp. 103513–103523, Jul. 2021.
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