DEEP LEARNING APPROACHES IN CLASSIFICATION OF ECG SIGNALS FOR CARDIOVASCULAR DISEASE DETECTION
Keywords:
Electrocardiogram (ECG), cardiovascular disease detection, deep learning, convolutional neural networks (CNN), long short-term memory (LSTM), Transformer models, signal processing, biomedical engineering, wearable healthcare systemsAbstract
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, emphasizing the critical need for early detection and accurate diagnosis. Electrocardiography (ECG) provides a noninvasive and cost-effective means of assessing cardiac health; however, manual interpretation is time-consuming and prone to human error. Recent advances in deep learning have shown great promise in automating ECG signal analysis by leveraging powerful feature extraction and classification capabilities. This study investigates multiple deep learning architectures, including convolutional neural networks (CNNs), hybrid CNN–LSTM models, and Transformer-based approaches, applied to benchmark ECG datasets. A comprehensive workflow is developed, consisting of data acquisition, preprocessing, beat segmentation, deep learning-based feature extraction, classification, and performance evaluation. Comparative experiments demonstrate that while CNNs effectively capture morphological features, the CNN–LSTM hybrid yields enhanced performance by modeling temporal dependencies. The Transformer-based model achieves the highest accuracy, sensitivity, and specificity, highlighting its ability to capture long-range dependencies in ECG signals. The results confirm the potential of advanced deep learning frameworks in supporting reliable, automated CVD detection. Future directions include cross-dataset validation, multimodal integration, and optimization for deployment on wearable and embedded platforms.
References
1. S. Nithya, M. M. S. Rani, and V. Sivakumar, “Classification of Cardiovascular Arrhythmia Using Deep Learning Techniques: A Review,” EAI Endorsed Trans Pervasive Health Technol, vol. 10, Jun. 2024, doi: 10.4108/eetpht.10.6421.
2. M. Sakib, S. Mustajab, and T. Siddiqui, “Deep Learning-Based Heartbeat Classification of 12-Lead ECG Time Series Signal,” in 2023 4th International Conference on Data Analytics for Business and Industry (ICDABI), IEEE, Oct. 2023, pp. 273–278. doi: 10.1109/ICDABI60145.2023.10629531.
3. Y. Ansari, O. Mourad, K. Qaraqe, and E. Serpedin, “Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023,” Front Physiol, vol. 14, Sep. 2023, doi: 10.3389/fphys.2023.1246746.
4. P. C and V. S. V, “Hybrid Learning Approach for Automated Identification and Categorization of Cardiovascular Disorders,” International Journal of Electrical and Electronics Research, vol. 12, no. 4, pp. 1301–1323, Nov. 2024, doi: 10.37391/ijeer.120423.
5. A. Razzak Khan Qureshi, G. Patil, R. Bhatt, C. Moghe, H. Pal, and C. Tatawat, “A Novel Deep Learning-based Classification Approach for the Detection of Heart Arrhythmias from the Electrocardiography Signal,” Scalable Computing: Practice and Experience, vol. 26, no. 1, pp. 371–387, Jan. 2025, doi: 10.12694/scpe.v26i1.3638.
6. Padathala Visweswara Rao and Dr. Kamal Srivastava, “Analysis of Cardiovascular Disease Classification Through Deep Learning Approach,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10, no. 6, pp. 431–437, Nov. 2024, doi: 10.32628/CSEIT2410441.
7. Y. D. Daydulo, B. L. Thamineni, and A. A. Dawud, “Cardiac arrhythmia detection using deep learning approach and time frequency representation of ECG signals,” BMC Med Inform Decis Mak, vol. 23, no. 1, p. 232, Oct. 2023, doi: 10.1186/s12911-023-02326-w.
8. B. Dushanov, N. Mamatov, and under Tiiame, “A LOW-NOISE ECG SIGNAL ACQUISITION AND CLASSIFICATION PLATFORM FOR CARDIOVASCULAR MONITORING APPLICATIONS,” SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL, vol. 4, 2025, doi: 10.5281/zenodo.15928633.
9. S. Sattar et al., “Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets,” Sensors, vol. 24, no. 8, p. 2484, Apr. 2024, doi: 10.3390/s24082484.
10. S. Garg, U. Jadia, A. Sagtani, and K. K. Hiran, “ECG-Lense: Benchmarking ML & DL Models on PTB-XL Dataset,” in 2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), IEEE, Jul. 2024, pp. 1–8. doi: 10.1109/ETNCC63262.2024.10767459.
11. Z. Hu, “ECG signal classification based on CNN deep learning,” Theoretical and Natural Science, vol. 13, no. 1, pp. 171–180, Nov. 2023, doi: 10.54254/2753-8818/13/20240838.
12. A. Munõz, J. Torres-Santamaria, M. Iregui, E. Romero, and A. Cruz-Roa, “Comparative Analysis of Deep Neural Network Architectures for Heart Disease Classification in Electrocardiography Signals,” in 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM), IEEE, Nov. 2024, pp. 1–4. doi: 10.1109/SIPAIM62974.2024.10783577.
13. S. P. S, A. J. V. K, B. S, P. S. Gresa, M. D, and V. Sathya, “Exploring the Potential of Machine Learning and Deep Learning in ECG Image Analysis for Cardiovascular Disease Diagnosis,” in 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), IEEE, Aug. 2024, pp. 1344–1349. doi: 10.1109/ICESC60852.2024.10689932.
14. T. Mahmud et al., “Ensemble Deep Learning Approach for ECG-Based Cardiac Disease Detection: Signal and Image Analysis,” in 2023 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), IEEE, Sep. 2023, pp. 70–74. doi: 10.1109/ICICT4SD59951.2023.10303625.
15. M. T. Tahmid, M. E. Kader, T. Mahmud, and S. A. Fattah, “MD-CardioNet: A Multi-Dimensional Deep Neural Network for Cardiovascular Disease Diagnosis From Electrocardiogram,” IEEE J Biomed Health Inform, vol. 28, no. 4, pp. 2005–2013, Apr. 2024, doi: 10.1109/JBHI.2023.3308856.
16. A. Elmassaoudi, S. Douzi, and M. Abik, “Machine Learning Approaches for Automated Diagnosis of Cardiovascular Diseases: A Review of Electrocardiogram Data Applications,” Cardiol Rev, Sep. 2024, doi: 10.1097/CRD.0000000000000764.
17. A. Eleyan and E. Alboghbaish, “Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework,” Computers, vol. 13, no. 2, p. 55, Feb. 2024, doi: 10.3390/computers13020055.
18. Mrs. M. S. R. Pavani, C. R. Chowdary, N. Reena, U. Jagadeesh, and P. K. Vamsi, “Detection of Cardiovascular Diseases from ECG Images Using Machine Learning and Deep Learning Methods,” Int J Res Appl Sci Eng Technol, vol. 12, no. 4, pp. 3826–3830, Apr. 2024, doi: 10.22214/ijraset.2024.60790.
19. A. Irsyad, P. P. Widagdo, P. P. Widagdo, and R. Wardhana, “Deep learning Methods for ECG-Based Heart Disease Detection,” Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 6, no. 4, pp. 467–477, Sep. 2024, doi: 10.35882/jeeemi.v6i4.498.
20. Hepsiba. D, L. D. Vijay Anand, Jencia. J, Vinotha. R, A. F. F, and R. Immaculate S, “Cardiac Arrhythmia Diagnosis Using Deep Learning: A 1D CNN-GRU Approach with Multiclass SVM and DWT Analysis,” in 2024 Tenth International Conference on Bio Signals, Images, and Instrumentation (ICBSII), IEEE, Mar. 2024, pp. 1–7. doi: 10.1109/ICBSII61384.2024.10564086.
21. H. Nursalim, A. Bustamam, Hermawan, and D. Sarwinda, “Classification of Electrocardiogram Signal Using Deep Learning Models,” in 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), IEEE, Feb. 2023, pp. 767–772. doi: 10.1109/ICCoSITE57641.2023.10127690.
22. H. Nursalim, A. Bustamam, Hermawan, and D. Sarwinda, “Classification of Electrocardiogram Signal Using Deep Learning Models,” in 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), IEEE, Feb. 2023, pp. 767–772. doi: 10.1109/ICCoSITE57641.2023.10127690.
23. A. T. -, A. S. -, and Dr. M. L. S. K. -, “Cardiovascular Disease Detection using Deep Learning,” International Journal For Multidisciplinary Research, vol. 6, no. 1, Feb. 2024, doi: 10.36948/ijfmr.2024.v06i01.13749.
24. M. M. Abo-Zahhad and A. M. A. Hassan, “Classification of ECG Signals for Detecting Coronary Heart Diseases Using Deep Transfer Learning Techniques,” in 2022 10th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC), IEEE, Dec. 2022, pp. 138–143. doi: 10.1109/JAC-ECC56395.2022.10043970.
25. M. M. Abo-Zahhad and A. M. A. Hassan, “Classification of ECG Signals for Detecting Coronary Heart Diseases Using Deep Transfer Learning Techniques,” in 2022 10th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC), IEEE, Dec. 2022, pp. 138–143. doi: 10.1109/JAC-ECC56395.2022.10043970.
26. H. adly yamani, M. Salem, N. Yhiea, and F. Melgani, “Cardiac Abnormality Detection Model with Inverted Residual Blocks for Large Electrocardiogram Datasets,” Advances in Environmental and Life Sciences, vol. 0, no. 0, pp. 0–0, Dec. 2024, doi: 10.21608/aels.2024.336248.1071.
27. H. M. Khater and A. Suliman, “Deep Learning-Based ECG Analysis for Myocardial Infarction Detection,” in 2023 15th International Conference on Innovations in Information Technology (IIT), IEEE, Nov. 2023, pp. 61–66. doi: 10.1109/IIT59782.2023.10366419.
28. G. S. Jameel, M. N. Al-Turfi, Dr. H. F. Mahdi, and S. N. Hussain, “Designing and Implementing an ECG Reader and Heart Disease Classification Device Using Deep Learning (Comprehensive Review),” Iraqi Journal for Computer Science and Mathematics, vol. 5, no. 3, Jan. 2024, doi: 10.52866/ijcsm.2024.05.03.034.
29. A. Eleyan, F. Bayram, and G. Eleyan, “Spectrogram-Based Arrhythmia Classification Using Three-Channel Deep Learning Model with Feature Fusion,” Applied Sciences, vol. 14, no. 21, p. 9936, Oct. 2024, doi: 10.3390/app14219936.
30. 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, doi: 10.3390/e23091121.
31. Omar Hashim Yahya et al., “Deep Learning Approach for Detecting Cardiovascular Arrhythmias in Seven Lead ECG Signal from Holter,” International Journal of Online and Biomedical Engineering (iJOE), vol. 19, no. 14, pp. 160–170, Oct. 2023, doi: 10.3991/ijoe.v19i14.43059.
32. V. Manimaran, N. Shanthi, N. Aravindhraj, K. M. Aatarsh, G. Adharshini, and P. Gokul, “Advancements in Heart Disease Classification: Leveraging Deep Learning Techniques for ECG Analysis,” in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, Jun. 2024, pp. 1–7. doi: 10.1109/ICCCNT61001.2024.10724585.
33. M. Khalid, C. Pluempitiwiriyawej, S. Wangsiripitak, G. Murtaza, and A. A. Abdulkadhem, “The Applications of Deep Learning in ECG Classification for Disease Diagnosis: A Systematic Review and Meta-Data Analysis,” Engineering Journal, vol. 28, no. 8, pp. 45–77, Aug. 2024, doi: 10.4186/ej.2024.28.8.45.
34. K. N, R. Ravindaran, A. V S, A. S, D. K, and C. N. Kasthuri, “Cardiac Arrhythmia Analysis using Spectrogram and Deep Learning,” in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, Jul. 2023, pp. 1–4. doi: 10.1109/ICCCNT56998.2023.10307868.
35. P. Pabitha, R. Praveen, K. C. J. Chandana, S. Ponlibarnaa, and A. S. Aparnaa, “A Comparative Study of Deep Learning Models for ECG Signal-based User Classification,” in 2023 12th International Conference on Advanced Computing (ICoAC), IEEE, Aug. 2023, pp. 1–8. doi: 10.1109/ICoAC59537.2023.10249402.
36. S. Goyal, “Automatic Diagnosis of Cardiovascular Diseases from ECG Signals using Convolutional Neural Network and Soft Computing Methods,” Nov. 17, 2022. doi:10.21203/rs.3.rs-2157214/v1.
37. H. A. Elyamani, M. A. Salem, F. Melgani, and N. M. Yhiea, “Deep residual 2D convolutional neural network for cardiovascular disease classification,” Sci Rep, vol. 14, no. 1, p. 22040, Sep. 2024, doi: 10.1038/s41598-024-72382-3.
38. ST. Aarthy and J. L. Mazher Iqbal, “A novel deep learning approach for early detection of cardiovascular diseases from ECG signals,” Med Eng Phys, vol. 125, p. 104111, Mar. 2024, doi: 10.1016/j.medengphy.2024.104111.
39. R. Wu, X. Wang, and Q. Feng, “Deep Learning Approach to Cardiovascular Disease Classification Employing Long Duration ECG Signal via Variational Mode Decomposition,” in 2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), IEEE, Nov. 2023, pp. 106–110. doi: 10.1109/ISKE60036.2023.10481118.
40. V. D. Nguyen, T. Thu Hien Hoang, K. S. Vu, and N. Lam Nguyen, “ECG-based Cardiovascular Disease Classification using Modified Inception,” in 2024 Tenth International Conference on Communications and Electronics (ICCE), IEEE, Jul. 2024, pp. 577–581. doi: 10.1109/ICCE62051.2024.10634650.
41. S. khanam, “Analysing and Evaluating the Performance of Deep-Learning-Based Arrhythmia Detection Using Electrocardiogram Signals,” international journal of advanced research in computer science, vol. 15, no. 2, pp. 74–83, Apr. 2024, doi: 10.26483/ijarcs.v15i2.7065.
42. H. Bensenane, D. Aksa, F. W. Omari, and A. Rahmoun, “A deep learning-based cardio-vascular disease diagnosis system,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 25, no. 2, p. 963, Feb. 2022, doi: 10.11591/ijeecs.v25.i2.pp963-971.
43. V. Ramalakshmi, N. Garg, M. E. M. Soudagar, D. Vekariya, H. Patil, and A. S. L. V, “Developing Deep Learning Model for Abnormal Heartbeat Detection in Electrocardiography Signals,” in 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), IEEE, Jan. 2024, pp. 315–320. doi: 10.1109/IDCIoT59759.2024.10467337.
44. A. Pokharel, S. Dahal, B. Bimal Chhetri, and P. Sapkota, “Electrocardiogram (ECG) Based Cardiac Arrhythmia Detection and Classification using Machine Learning Algorithms.”
45. S. Dey, R. Pal, and S. Biswas, “Deep Learning Algorithms for Efficient Analysis of ECG Signals to Detect Heart Disorders,” 2022. doi: 10.5772/intechopen.103075.
46. M. Sraitih and Y. Jabrane, “A survey of deep learning approaches for classifying ECG heartbeat arrhythmias,” in 2022 International Conference on Intelligent Systems and Computer Vision (ISCV), IEEE, May 2022, pp. 1–8. doi: 10.1109/ISCV54655.2022.9806085.
47. D. S. AbdElminaam, A. G. Fahmy, Y. M. Ali, O. A. D. El-Din, A. R. aly, and M. Heidar, “DeepECG :Building an Efficient Framework for Automatic Arrhythmia classification model,” in 2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), IEEE, May 2022, pp. 203–209. doi: 10.1109/MIUCC55081.2022.9781646.
48. D. S. AbdElminaam, A. G. Fahmy, Y. M. Ali, O. A. D. El-Din, A. R. aly, and M. Heidar, “DeepECG :Building an Efficient Framework for Automatic Arrhythmia classification model,” in 2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), IEEE, May 2022, pp. 203–209. doi: 10.1109/MIUCC55081.2022.9781646.
49. M. Sraitih and Y. Jabrane, “A survey of deep learning approaches for classifying ECG heartbeat arrhythmias,” in 2022 International Conference on Intelligent Systems and Computer Vision (ISCV), IEEE, May 2022, pp. 1–8. doi: 10.1109/ISCV54655.2022.9806085.
50. A. Odugoudar and J. S. Walia, “ECG Classification System for Arrhythmia Detection Using Convolutional Neural Networks,” Jun. 2024, [Online]. Available: http://arxiv.org/abs/2303.03660
51. P. N. Malleswari, C. Hanuman, V. R. Kammampati, S. Swathi, and B. E. Raju, “Deep Learning based DWT- Bi-LSTM Classifier for Enhanced Cardiovascular Arrhythmia Classification,” International Journal of Electrical and Electronics Research, vol. 12, no. 3, pp. 934–939, Aug. 2024, doi: 10.37391/ijeer.120325.
52. M. S., B. R.A., and R. R., “A Novel Deep Learningbased Model for the Efficient Classification of Electrocardiogram Signals,” CARDIOMETRY, no. 24, pp. 1033–1039, Nov. 2022, doi: 10.18137/cardiometry.2022.24.10331039.
53. J. Gutierrez-Ojeda, V. Ponomaryov, J.-A. Almaraz-Damian, R. Reyes-Reyes, and C. Cruz-Ramos, “ECG Arrhythmia Classification Using Recurrence Plot and ResNet-18,” International Journal of Computing, pp. 140–148, Jul. 2023, doi: 10.47839/ijc.22.2.3083.
54. K. Qaddoum, A. Alazzam, and R. Al Azawi, “A Deep Neural Network heartbeat classification approach for arrhythmia detection,” in 2020 Seventh International Conference on Information Technology Trends (ITT), IEEE, Nov. 2020, pp. 131–134. doi: 10.1109/ITT51279.2020.9320777.
55. K. M. P, P. Chaudhary, and M. Dheer, “Retracted: Investigating the Use of Deep Learning Networks to Classify Cardiovascular Diseases,” in 2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC), IEEE, Jan. 2024, pp. 1–6. doi: 10.1109/ICOCWC60930.2024.10470714.
56. S. Khan et al., “A Deep Learning Framework for the Classification of ECG Signals,” in 2022 International Conference on Engineering and Emerging Technologies (ICEET), IEEE, Oct. 2022, pp. 1–5. doi: 10.1109/ICEET56468.2022.10007143.
57. Begmamat, D., Sanjarbek, B., Utkirjon, U., Shohrukh, N., Umidjon, K. (2023). SMART Education Framework to Assess the Knowledge of Engineering Students. In: Cioboată, D.D. (eds) International Conference on Reliable Systems Engineering (ICoRSE) - 2023. ICoRSE 2023. Lecture Notes in Networks and Systems, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-031-40628-7_7.
58. Begmamat, D., Sanjarbek, B., Utkirjon, U., Shohrukh, N., Umidjon, K. (2023). Comparative Analysis of SMART Education Framework and Traditional Assessment Techniques in Evaluating the Knowledge of Engineering Students. In: Cioboată, D.D. (eds) International Conference on Reliable Systems Engineering (ICoRSE) - 2023. ICoRSE 2023. Lecture Notes in Networks and Systems, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-031-40628-7_8.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Dushanov Begmamat Berdimurodovich, Mamatov Narzillo Solidjonovich
This work is licensed under a Creative Commons Attribution 4.0 International License.
