Open Access
Issue
ITM Web Conf.
Volume 64, 2024
2nd International Conference on Applied Computing & Smart Cities (ICACS24)
Article Number 01023
Number of page(s) 21
DOI https://doi.org/10.1051/itmconf/20246401023
Published online 05 July 2024
  1. Surantha, N., T.F. Lesmana, and S.M. Isa, Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data. Journal of Big Data, 8(1): p. 14 (2021). [CrossRef] [Google Scholar]
  2. World Health Organization “WHO” [Online] https://www.who.int, a.d., Jan (2024). [Google Scholar]
  3. Zhang, W., et al. ECG signal classification with deep learning for heart disease identification. in 2018 International Conference on Big Data and Artificial Intelligence (BDAI), IEEE (2018). [Google Scholar]
  4. Appathurai, A., et al., A study on ECG signal characterization and practical implementation of some ECG characterization techniques. Measurement, 147: p. 106384 (2019). [CrossRef] [Google Scholar]
  5. Eleyan, Alaa, and Ebrahim Alboghbaish. “Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework.” Computers 13, no. 2 (2024). [Google Scholar]
  6. Sharma, P. and S.K. Dinkar, an intelligent deep neural network with Opposition based Laplacian Equilibrium Optimizer to improve feature extraction using ECG signals. Biomedical Signal Processing and Control, 87: p. 105415 (2024). [CrossRef] [Google Scholar]
  7. Tayel, M.B., A.S. Eltrass, and A.I. Ammar, A new multi-stage combined kernel filtering approach for ECG noise removal. Journal of electro cardiology, 51(2): p. 265–275 (2018). [CrossRef] [Google Scholar]
  8. Rajkumar, A., M. Ganesan, and R. Lavanya. Arrhythmia classification on ECG using Deep Learning. in 2019 5th international conference on advanced computing & communication systems (ICACCS), IEEE (2019). [Google Scholar]
  9. Mohonta, S.C., M.A. Motin, and D.K. Kumar, Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model. Sensing and BioSensing Research, 37: p. 100502. (2022). [CrossRef] [Google Scholar]
  10. Subbiah, S., Patro, R., and Subbuthai, Feature extraction and classification for ECG signal processing based on artificial neural network and machine learning approach. International Conference on Inter Disciplinary Research in Engineering and Technology, pp. 50–57, (2015). [Google Scholar]
  11. Shobanadevi, A. and T. Veeramakali, Classification and Interpretation of ECG Arrhythmia through Deep Learning Techniques, (2023). [Google Scholar]
  12. Ullah, A., et al., A hybrid deep CNN model for abnormal arrhythmia detection based on cardiac ECG signal. Sensors, 21(3): p. 951 (2021). [Google Scholar]
  13. Loni, M., et al., Deep Maker: A multi-objective optimization framework for deep neural networks in embedded systems. Microprocessors and Microsystems, 73: p. 102989 (2020). [CrossRef] [Google Scholar]
  14. Wijaya, C., et al., Abnormalities State Detection from P-Wave, QRS Complex, and TWave in Noisy ECG. Journal of Physics: Conference Series, 1230: p. 012015 (2019). [CrossRef] [Google Scholar]
  15. Wasimuddin, M., et al., Stages-Based ECG Signal Analysis from Traditional Signal Processing to Machine Learning Approaches: A Survey. IEEE Access, 8: p. 177782–177803 (2020). [CrossRef] [Google Scholar]
  16. Acharya, U.R., et al., A deep convolutional neural network model to classify heartbeats. Computers in biology and medicine, 89: p. 389–396 (2017). [CrossRef] [Google Scholar]
  17. Allabun, Sarah. “An Intelligent Learning Approach for Improving ECG Signal Classification and Arrhythmia Analysis.” International Journal of Advanced Computer Science & Applications 15, no. 4 (2024). [CrossRef] [Google Scholar]
  18. Kłosowski, G., et al., The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification. Electronics, 9: p. 1452 (2020). [CrossRef] [Google Scholar]
  19. Peimankar, A. and S. Puthusserypady, DENS-ECG: A deep learning approach for ECG signal delineation. Expert systems with applications, 165: p. 113911 (2021). [CrossRef] [Google Scholar]
  20. Ebrahimi, Z., et al., A review on deep learning methods for ECG arrhythmia classification. Expert Systems with Applications: X, 7: p. 100033 (2020). [CrossRef] [Google Scholar]
  21. Arsene, C.T., R. Hankins, and H. Yin. Deep learning models for denoising ECG signals. in 2019 27th European Signal Processing Conference (EUSIPCO), IEEE (2019). [Google Scholar]
  22. Baloglu, U.B., et al., Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern recognition letters, 122: p. 23–30 (2019). [CrossRef] [Google Scholar]
  23. Liu, F., et al. A LSTM and CNN based assemble neural network framework for arrhythmias classification. in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE (2019). [Google Scholar]
  24. Kiranyaz, S., T. Ince, and M. Gabbouj, Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Transactions on Biomedical Engineering, 63(3): p. 664–675 (2015). [Google Scholar]
  25. Savalia, S., and V. Emamian, Cardiac arrhythmia classification by multi-layer perceptron and convolution neural networks. Bioengineering, 5(2): p. 35 (2018). [CrossRef] [Google Scholar]
  26. Murat, F., et al., Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review. Computers in biology and medicine, 120: p. 103726 (2020). [CrossRef] [Google Scholar]
  27. Śmigiel, S., K. Pałczyński, and D. Ledziński, ECG signal classification using deep learning techniques based on the PTB-XL dataset. Entropy, 23(9): p. 1121 (2021). [CrossRef] [Google Scholar]
  28. Karim, F., et al., LSTM fully convolutional networks for time series classification. IEEE access, 6: p. 1662–1669 (2017). [Google Scholar]
  29. Sharma, P. and S.K. Dinkar, an intelligent deep neural network with Opposition based Laplacian Equilibrium Optimizer to improve feature extraction using ECG signals. Biomedical Signal Processing and Control, 87: p. 105415 (2024). [CrossRef] [Google Scholar]
  30. Kłosowski, G., et al., The use of time-frequency moments as inputs of LSTM network for ECG signal classification. Electronics, 9(9): p. 1452 (2020). [CrossRef] [Google Scholar]
  31. Xie, J. and B. Yao, Physics-constrained deep learning for robust inverse ECG modeling. IEEE Transactions on Automation Science and Engineering, 20(1): p. 151–166 (2022). [Google Scholar]
  32. Wu, M.-H. and E.Y. Chang. Deep arrhythmia database: a large-scale dataset for arrhythmia detector evaluation. in Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care, (2017). [Google Scholar]
  33. Rashed-Al-Mahfuz, M., et al., Deep convolutional neural networks-based ECG beats classification to diagnose cardiovascular conditions. Biomedical engineering letters, 11: p. 147–162 (2021). [CrossRef] [Google Scholar]
  34. Amirshahi, A. and M. Hashemi, ECG classification algorithm based on STDP and RSTDP neural networks for real-time monitoring on ultra-low-power personal wearable devices. IEEE transactions on biomedical circuits and systems, 13(6): p. 1483–1493 (2019). [CrossRef] [Google Scholar]
  35. Rana, A., and K.K. Kim. ECG heartbeat classification using a single layer LSTM model. in 2019 International SoC Design Conference (ISOCC), IEEE (2019). [Google Scholar]
  36. Saadatnejad, S., M. Oveisi, and M. Hashemi, LSTM-based ECG classification for continuous monitoring on personal wearable devices. IEEE journal of biomedical and health informatics, 24(2): p. 515–523 (2019). [Google Scholar]
  37. Yao, Q., et al., multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network. Information Fusion, 53: p. 174–182 (2020). [CrossRef] [Google Scholar]
  38. Kumar M, A. and A. Chakrapani, Classification of ECG signal using FFT based improved Alexnet classifier. PLOS one, 17(9): p. e0274225 (2022). [CrossRef] [Google Scholar]
  39. Espin-Ramos, D., et al. A Deep Learning-Based Algorithm for ECG Arrhythmia Classification. in 2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS), IEEE. (2023). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.