SavedAdd your journal

Journals / Artificial Intelligence Theory and Applications / 2026 / Volume: 6 Issue: 1

Heartbeat Detection on Lightweight Architectures Using an Improved Deep Learning Model by Integration of Attention Mechanisms and Dynamic Activation Function

Fatma Akalın(SAKARYA ÜNİVERSİTESİ)0000-0001-6670-915X
Pages
—
Publication Date
2026-05-03
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The improved structure provided approximately 11% improvement in accuracy compared to the original MobileNetv1 architecture.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:ECG is a diagnostic technique that measures the electrical activity of the heart over a period of time.
📊
Key Findings & Data:It plays a critical role in the diagnosis and follow-up process of various heart problems in patients.
💡
Conclusion & Impact:The improved structure provided approximately 11% improvement in accuracy compared to the original MobileNetv1 architecture.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:MobileNet transfer learning · Channel Attention · Spatial Attention

Abstract

ECG is a diagnostic technique that measures the electrical activity of the heart over a period of time. It plays a critical role in the diagnosis and follow-up process of various heart problems in patients. However, manual assessments of this noninvasive diagnostic technique are error-prone and time-consuming. Therefore, it is very valuable to support early diagnosis with informatics-based studies. In this study, heart beats for N, S, V, F and Q categories were provided via the Kaggle platform. Then, an artificial intelligence-supported classification was done to detect these heartbeats. The classification architecture is based on MobileNet transfer learning. In the architecture modified by integrating channel attention, spatial attention and coordinate attention, the DY-ReLU activation function, which is dynamically changed according to the input, is used. These integrations aims to achieve stronger representation capabilities. The improved structure provided approximately 11% improvement in accuracy compared to the original MobileNetv1 architecture.

🔖

Cite This Article

Akalın, F. (2026). Heartbeat Detection on Lightweight Architectures Using an Improved Deep Learning Model by Integration of Attention Mechanisms and Dynamic Activation Function. Artificial Intelligence Theory and Applications, Volume: 6 Issue: 1. https://ajindex.com/articles/1609586

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (33)

  1. M. F. Issa, A. Yousry, G. Tuboly, Z. Juhasz, A. H. AbuEl-Atta, and M. M. Selim, “Heartbeat classification based on single lead-II ECG using deep learning,” Heliyon, vol. 9, no. 7, Jul. 2023, doi: 10.1016/j.heliyon.2023.e17974.
  2. S. N. M. S. Ismail, S. F. A. Razak, and N. A. Ab Aziz, “ECG-based transfer learning for cardiovascular disease: A scoping review,” Dec. 01, 2025, KeAi Communications Co. doi: 10.1016/j.ijcce.2025.01.007.
  3. S. Nahak and G. Saha, “Analysis and classification of beat-level ECG arrhythmia using WST-inspired CNN framework,” Biomed Signal Process Control, vol. 109, Nov. 2025, doi: 10.1016/j.bspc.2025.108031.
  4. W. Kharzi, N. Meziane, H. Zairi, M. K. Talha, L. Madaoui, and O. Kerdjidj, “Real-time embedded ECG anomaly detection system with comparative analysis of CWT-based transfer learning performance across electrode types,” Biomed Signal Process Control, vol. 109, Nov. 2025, doi: 10.1016/j.bspc.2025.107898.
  5. C. Plaza-Seco, K. E. Barner, R. Holgado-Cuadrado, F. M. Melgarejo-Meseguer, J. L. Rojo-Álvarez, and M. Blanco-Velasco, “Detection of intra-beat waves on ambulatory ECG using manifolds: An explainable deep learning approach,” Biomed Signal Process Control, vol. 105, Jul. 2025, doi: 10.1016/j.bspc.2025.107523.
  6. T. A. Sathi et al., “An interpretable electrocardiogram-based model for predicting arrhythmia and ischemia in cardiovascular disease,” Results in Engineering, vol. 24, Dec. 2024, doi: 10.1016/j.rineng.2024.103381.
  7. S. C. Mohonta, M. A. Motin, and D. K. Kumar, “Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model,” Sens Biosensing Res, vol. 37, Aug. 2022, doi: 10.1016/j.sbsr.2022.100502.
  8. M. Roy, A. Halder, S. Majumder, and U. Biswas, “AttentivECGRU: GRU based autoencoder with attention mechanism and automated fuzzy thresholding for ECG arrhythmia detection,” Appl Soft Comput, vol. 167, Dec. 2024, doi: 10.1016/j.asoc.2024.112337.
  9. R. Tao, L. Wang, Y. Xiong, and Y. R. Zeng, “IM-ECG: An interpretable framework for arrhythmia detection using multi-lead ECG,” Expert Syst Appl, vol. 237, Mar. 2024, doi: 10.1016/j.eswa.2023.121497.
  10. 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, Feb. 2023, doi: 10.1016/j.compbiomed.2022.106511.
  11. U. Lomoio et al., “A convolutional autoencoder framework for ECG signal analysis,” Heliyon, vol. 11, no. 2, Jan. 2025, doi: 10.1016/j.heliyon.2024.e41517.
  12. W. Liu, S. Pan, Z. Li, S. Chang, Q. Huang, and N. Jiang, “Lead-fusion Barlow twins: A fused self-supervised learning method for multi-lead electrocardiograms,” Information Fusion, vol. 114, Feb. 2025, doi: 10.1016/j.inffus.2024.102698.
  13. E. Essa and X. Xie, “Multi-model Deep Learning Ensemble for ECG Heartbeat Arrhythmia Classification,” in 28th European Signal Processing Conference (EUSIPCO 2020) proceedings: 24-28 August 2020, Amsterdam, the Nederlands, IEEE, 2021, pp. 1085–1089.
  14. https://www.kaggle.com/datasets/bhargavidimple/ecg-cleaned-dataset, “ecg cleaned dataset.”
  15. M. A. B. Abbass and Y. Ban, “MobileNet-Based Architecture for Distracted Human Driver Detection of Autonomous Cars,” Electronics (Switzerland), vol. 13, no. 2, Jan. 2024, doi: 10.3390/electronics13020365.
  16. B. Han, M. Hu, X. Wang, and F. Ren, “A Triple-Structure Network Model Based upon MobileNet V1 and Multi-Loss Function for Facial Expression Recognition,” Symmetry (Basel), vol. 14, no. 10, Oct. 2022, doi: 10.3390/sym14102055.
  17. A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” Apr. 2017, [Online]. Available: http://arxiv.org/abs/1704.04861
  18. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Mar. 2019, [Online]. Available: http://arxiv.org/abs/1801.04381
  19. A. Howard et al., “Searching for MobileNetV3,” Nov. 2019, [Online]. Available: http://arxiv.org/abs/1905.02244
  20. S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “CBAM: Convolutional Block Attention Module,” Jul. 2018, [Online]. Available: http://arxiv.org/abs/1807.06521
  21. Q. Hou, D. Zhou, and J. Feng, “Coordinate Attention for Efficient Mobile Network Design,” Mar. 2021, [Online]. Available: http://arxiv.org/abs/2103.02907
  22. Y. Chen, X. Dai, M. Liu, D. Chen, L. Yuan, and Z. Liu, “Dynamic ReLU,” Aug. 2020, [Online]. Available: http://arxiv.org/abs/2003.10027
  23. T. F. Romdhane, H. Alhichri, R. Ouni, and M. Atri, “Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss,” Comput Biol Med, vol. 123, Aug. 2020, doi: 10.1016/j.compbiomed.2020.103866.
  24. A. Rath, D. Mishra, G. Panda, and S. C. Satapathy, “Heart disease detection using deep learning methods from imbalanced ECG samples,” Biomed Signal Process Control, vol. 68, Jul. 2021, doi: 10.1016/j.bspc.2021.102820.
  25. F. Murat, O. Yildirim, M. Talo, U. B. Baloglu, Y. Demir, and U. R. Acharya, “Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review,” May 01, 2020, Elsevier Ltd. doi: 10.1016/j.compbiomed.2020.103726.
  26. W. Ullah, I. Siddique, R. M. Zulqarnain, M. M. Alam, I. Ahmad, and U. A. Raza, “Classification of Arrhythmia in Heartbeat Detection Using Deep Learning,” Comput Intell Neurosci, vol. 2021, 2021, doi: 10.1155/2021/2195922.
  27. Z. Li, D. Zhou, L. Wan, J. Li, and W. Mou, “Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram,” J Electrocardiol, vol. 58, pp. 105–112, Jan. 2020, doi: 10.1016/j.jelectrocard.2019.11.046.
  28. G. Sannino and G. De Pietro, “A deep learning approach for ECG-based heartbeat classification for arrhythmia detection,” Future Generation Computer Systems, vol. 86, pp. 446–455, Sep. 2018, doi: 10.1016/j.future.2018.03.057.
  29. U. R. Acharya et al., “A deep convolutional neural network model to classify heartbeats,” Comput Biol Med, vol. 89, pp. 389–396, Oct. 2017, doi: 10.1016/j.compbiomed.2017.08.022.
  30. P. Madan, V. Singh, D. P. Singh, M. Diwakar, B. Pant, and A. Kishor, “A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification,” Bioengineering, vol. 9, no. 4, Apr. 2022, doi: 10.3390/bioengineering9040152.
  31. M. F. Issa, A. Yousry, G. Tuboly, Z. Juhasz, A. H. AbuEl-Atta, and M. M. Selim, “Heartbeat classification based on single lead-II ECG using deep learning,” Heliyon, vol. 9, no. 7, Jul. 2023, doi: 10.1016/j.heliyon.2023.e17974.
  32. S. Irfan, N. Anjum, T. Althobaiti, A. A. Alotaibi, A. B. Siddiqui, and N. Ramzan, “Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique,” Sensors, vol. 22, no. 15, Aug. 2022, doi: 10.3390/s22155606.
  33. E. Essa and X. Xie, “Multi-model Deep Learning Ensemble for ECG Heartbeat Arrhythmia Classification,” in 28th European Signal Processing Conference (EUSIPCO 2020) proceedings: 24-28 August 2020, Amsterdam, the Nederlands, IEEE, 2021, pp. 1085–1089.