Application of deep neural network with stacked denoising autoencoder for ECG signal classification
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Published:
Jun 30, 2024
Keywords:
Arrhythmia, Deep Learning, Electrocardiogram (ECG), Neural Network, Stacked Denoising Autoencoders (SDAEs)
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Abstract
Applying deep neural networks with stacked denoising autoencoders (SDAEs) for ECG signal classification presents a promising approach for improving the accuracy of arrhythmia diagnosis. This study aims to develop a robust model that enhances the classification of ECG signals by effectively denoising the input data and extracting rich feature representations. The research employs a method involving data preprocessing, feature extraction using SDAEs, and classification with a deep neural network (DNN) validated on the MIT-BIH Arrhythmia Database. The results demonstrate that the proposed model achieves an impressive accuracy of 98.91%, significantly outperforming traditional machine learning methods. The implications of this research are substantial, offering a reliable and automated tool for arrhythmia diagnosis that can be utilized in clinical settings to improve patient care. The study highlights the model's potential for real-time clinical application, although further validation on more extensive and diverse datasets is necessary to confirm its generalizability and robustness. This research contributes to the field by integrating advanced SDAEs with deep learning, paving the way for more accurate and efficient ECG signal classification systems
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Gunawan, G. ., Aimar Akbar, A. ., & Andriani, W. . (2024). Application of deep neural network with stacked denoising autoencoder for ECG signal classification . Journal of Intelligent Decision Support System (IDSS), 7(2), 173-187. https://doi.org/10.35335/idss.v7i2.247
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Al Rahhal, M. M., Bazi, Y., AlHichri, H., Alajlan, N., Melgani, F., & Yager, R. R. (2016). Deep learning approach for active classification of electrocardiogram signals. Information Sciences, 345, 340–354.
Albaba, A., Simões-Capela, N., Wang, Y., Hendriks, R. C., De Raedt, W., & Van Hoof, C. (2021). Assessing the signal quality of electrocardiograms from varied acquisition sources: A generic machine learning pipeline for model generation. Computers in Biology and Medicine, 130, 104164.
Antiperovitch, P., Mortara, D., Barrios, J., Avram, R., Yee, K., Khaless, A. N., Cristal, A., Tison, G., & Olgin, J. (2024). Continuous Atrial Fibrillation Monitoring From Photoplethysmography: Comparison Between Supervised Deep Learning and Heuristic Signal Processing. JACC: Clinical Electrophysiology.
Chatterjee, S., Thakur, R. S., Yadav, R. N., Gupta, L., & Raghuvanshi, D. K. (2020). Review of noise removal techniques in ECG signals. IET Signal Processing, 14(9), 569–590.
Dasan, E., & Panneerselvam, I. (2021). A novel dimensionality reduction approach for ECG signal via convolutional denoising autoencoder with LSTM. Biomedical Signal Processing and Control, 63, 102225.
Egger, J., Gsaxner, C., Pepe, A., Pomykala, K. L., Jonske, F., Kurz, M., Li, J., & Kleesiek, J. (2022). Medical deep learning—A systematic meta-review. Computer Methods and Programs in Biomedicine, 221, 106874.
Eltrass, A. S., Tayel, M. B., & Ammar, A. I. (2022). Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures. Neural Computing and Applications, 34(11), 8755–8775.
Ertuğrul, Ö. F., Acar, E., Aldemir, E., & Öztekin, A. (2021). Automatic diagnosis of cardiovascular disorders by sub images of the ECG signal using multi-feature extraction methods and randomized neural network. Biomedical Signal Processing and Control, 64, 102260.
Kulkarni, C., Quraishi, A., Raparthi, M., Shabaz, M., Khan, M. A., Varma, R. A., Keshta, I., Soni, M., & Byeon, H. (2024). Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer. BMC Medical Informatics and Decision Making, 24(1), 92.
Liu, G., Han, X., Tian, L., Zhou, W., & Liu, H. (2021). ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features. Computer Methods and Programs in Biomedicine, 208, 106269.
Meng, L., Tan, W., Ma, J., Wang, R., Yin, X., & Zhang, Y. (2022). Enhancing dynamic ECG heartbeat classification with lightweight transformer model. Artificial Intelligence in Medicine, 124, 102236.
Mishra, A., Dharahas, G., Gite, S., Kotecha, K., Koundal, D., Zaguia, A., Kaur, M., & Lee, H.-N. (2022). ECG data analysis with denoising approach and customized CNNs. Sensors, 22(5), 1928.
Murat, F., Yildirim, O., Talo, M., Demir, Y., Tan, R.-S., Ciaccio, E. J., & Acharya, U. R. (2021). Exploring deep features and ECG attributes to detect cardiac rhythm classes. Knowledge-Based Systems, 232, 107473.
Prusty, M. R., Pandey, T. N., Lekha, P. S., Lellapalli, G., & Gupta, A. (2024). Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals. Scientific Reports, 14(1), 2633.
Sahoo, S., Dash, M., Behera, S., & Sabut, S. (2020). Machine learning approach to detect cardiac arrhythmias in ECG signals: A survey. Irbm, 41(4), 185–194.
Saini, S. K., & Gupta, R. (2022). Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: state-of-the-art and future challenges. Artificial Intelligence Review, 55(2), 1519–1565. https://doi.org/10.1007/s10462-021-09999-7
Sun, X., Liu, P., He, Z., Han, Y., & Su, B. (2022). Automatic classification of electrocardiogram signals based on transfer learning and continuous wavelet transform. Ecological Informatics, 69, 101628.
Tarekegn, A. N., Michalak, K., & Giacobini, M. (2020). Cross-validation approach to evaluate clustering algorithms: An experimental study using multi-label datasets. SN Computer Science, 1, 1–9.
Vijayakumar, V., Ummar, S., Varghese, T. J., & Shibu, A. E. (2022). ECG noise classification using deep learning with feature extraction. Signal, Image and Video Processing, 16(8), 2287–2293.
Voet, L. J. A., Prashanth, P., Speth, R. L., Sabnis, J. S., Tan, C. S., & Barrett, S. R. H. (2024). Automatic Continuous Thrust Control for Supersonic Transport Takeoff Noise Reduction. Journal of Aircraft, 61(1), 291–306.
Wang, K., Wu, P., Xuan, C., Zhang, Y., Bu, K., & Ma, Y. (2021). Identification of grass growth conditions based on sheep grazing acoustic signals. Computers and Electronics in Agriculture, 190, 106463.
Wang, X., Chen, B., Zeng, M., Wang, Y., Liu, H., Liu, R., Tian, L., & Lu, X. (2022). An ECG signal denoising method using conditional generative adversarial net. IEEE Journal of Biomedical and Health Informatics, 26(7), 2929–2940.
Wang, Y., Li, F., Li, Q., Lü, H., & Zhou, K. (2021). Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning. Physics Letters B, 822, 136669.
Xie, L., Li, Z., Zhou, Y., He, Y., & Zhu, J. (2020). Computational diagnostic techniques for electrocardiogram signal analysis. Sensors, 20(21), 6318.
Zhang, N., Ying, S., Zhu, K., & Zhu, D. (2022). Software defect prediction based on stacked sparse denoising autoencoders and enhanced extreme learning machine. IET Software, 16(1), 29–47.
Zheng, J., Chu, H., Struppa, D., Zhang, J., Yacoub, S. M., El-Askary, H., Chang, A., Ehwerhemuepha, L., Abudayyeh, I., & Barrett, A. (2020). Optimal multi-stage arrhythmia classification approach. Scientific Reports, 10(1), 2898.
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