Real-time Assessment of ECG Classification based on Time-series Data and Other Types of Features

Thanh-Luan Tran; Bao-Toan Thai; Vy-Khang Tran; Xuan-Nhi Nguyen-Thi; Chi-Ngon Nguyen; Van-Khanh Nguyen

doi:10.5614/j.eng.technol.sci.2025.57.4.8

Real-time Assessment of ECG Classification based on Time-series Data and Other Types of Features

Published: 2025-01-01 | DOI: 10.5614/j.eng.technol.sci.2025.57.4.8 | Vol 57, Issue 4 (2025) | Cited by: 1

Authors

Thanh-Luan Tran

Faculty of Mechanical Power Engineering, Vinh Long University of Technology Education, Nguyen Hue Street, Long Chau Ward, Vinh Long Province 85000

ORCID: 0000-0003-3581-8796 · OpenAlex: A5026534832

Bao-Toan Thai

Faculty of Automation Engineering, College of Engineering, Can Tho University, 3/2 Street, Ninh Kieu Ward, Can Tho City 94000

ORCID: 0009-0005-7036-1893 · OpenAlex: A5104635079

Vy-Khang Tran

Faculty of Automation Engineering, College of Engineering, Can Tho University, 3/2 Street, Ninh Kieu Ward, Can Tho City 94000

ORCID: 0000-0002-4321-0126 · OpenAlex: A5075842943

Xuan-Nhi Nguyen-Thi

Vinh Long Vocational College, 1A National Highway , Phuoc Yen Hamlet, Phu Quoi Commune, Vinh Long Province 85000,

OpenAlex: A5119495054

Chi-Ngon Nguyen

Faculty of Automation Engineering, College of Engineering, Can Tho University, 3/2 Street, Ninh Kieu Ward, Can Tho City 94000

ORCID: 0000-0002-9638-7259 · OpenAlex: A5057216550

Van-Khanh Nguyen

Faculty of Automation Engineering, College of Engineering, Can Tho University, 3/2 Street, Ninh Kieu Ward, Can Tho City 94000

ORCID: 0000-0003-0006-6803 · OpenAlex: A5018373202

Memuat…

Buka di tab baru ↗ Download PDF

Abstract

Cardiovascular diseases are the leading cause of mortality worldwide. An increasing number of studies have applied artificial intelligence (AI) to identify anomalies and classify electrocardiograms (ECGs), supporting early detection and diagnosis. This study proposes and evaluates the classification of ECG signals based on time-series data and features extracted via fast Fourier transform (FFT) and discrete cosine transform (DCT), implemented on resource-limited microcontroller units (MCUs) for selected AI models. Two models, the artificial neural network (ANN) and the convolutional neural network (CNN), were proposed for classifying five common ECG labels. These models were trained and tested with three types of input data: time-series data, FFT features, and DCT features, sourced from an available database. After training, the optimized models were quantized to assess their accuracy before being deployed in real-time to measure inference time on the ESP32 MCU. Before quantization, the ANN model achieved the highest accuracy with both DCT and time-series inputs (98.0%); meanwhile, the CNN model performed best with time-series input (97.0%). After quantization, the ANN maintained the highest accuracy with time-series input (97.1%), followed by the ANN with DCT at 95.6%. CNN models remained stable, with post-quantization accuracy of 95.8% for time-series input, 94.9% for DCT, and 90.0% for FFT. In contrast, ANN with FFT input showed a significant drop to 65.6%.

Keywords

artificial neural network; convolutional neural network; discrete cosine transform; electrocardiograms; fast fourier transform; microcontroller units

Memuat isi artikel…

Buka teks lengkap artikel ini

Research Intelligence

Data from OpenAlex ↗

Metrics

Citations

0.66

FWCIfield-weighted

74th

Percentilevs same year + field

Article

Work type

GOLD

Open Access

Topics

ECG Monitoring and Analysis Primary

0.67

Cardiology and Cardiovascular Medicine › Medicine › Health Sciences

Fault Detection and Control Systems

0.64

Related Research

Citation Trend

2026

Citation Timeline

Year	Citations
2026	1

Semantic Profile AI-classified research signals

Core Domains

Series (stratigraphy) 0.56

level 2

Time series 0.53

level 2

Data mining 0.47

level 1

Secondary Topics

Pattern recognition (psychology) 0.41 Computer science 0.38 Statistics 0.34 Artificial intelligence 0.32 Mathematics 0.31

Institution Network

Vinh Long University of Technology Education VN
Thanh-Luan Tran · Xuan-Nhi Nguyen-Thi
Can Tho University VN
Bao-Toan Thai · Vy-Khang Tran · Chi-Ngon Nguyen · Khanh Nguyen

References

Ahmed, K., & Hassan, M. (2022). tinyCare: A tinyML-based Low-Cost Continuous Blood Pressure Estimation on the Extreme Edge. 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI), 264–275. https://doi.org/10.1109/ICHI54592.2022.00047 DOI: 10.1109/ichi54592.2022.00047
Amirshahi, A., & Hashemi, M. (2021). ECG classification algorithm based on STDP and R-STDP neural networks for real-time monitoring on ultra low-power personal wearable devices. IEEE Transactions on Biomedical Circuits and Systems, 13(6), 1483–1493. https://doi.org/10.1109/ TBCAS.2019.2948920
BinMakhashen, G. M., Arafat, S. M. A., & Dohrmann, M. L. (2014). CAD detection using neural network fusion of the 12 lead stress ECG system. Information Fusion, 6(7), 8.
Caesarendra, W., Hishamuddin, T. A., Lai, D. T. C., Husaini, A., Nurhasanah, L., Glowacz, A., &
Alfarisy, G. A. F. (2022). An Embedded System Using Convolutional Neural Network Model for Online and Real-Time ECG Signal Classification and Prediction. Diagnostics, 12(4), 1–19. https://doi.org/10.3390/diagnostics12040795 DOI: 10.3390/diagnostics12040795
Cai, J., Zhou, G., Dong, M., Hu, X., Liu, G., & Ni, W. (2021). Real-Time Arrhythmia Classification Algorithm Using Time-Domain ECG Feature Based on FFNN and CNN. Mathematical Problems in Engineering, 2021, 6648432. https://doi.org/10.1155/2021/6648432 DOI: 10.1155/2021/6648432
Chowdhury, D., & Chattopadhyay, M. (2023). An on-device machine learning assisted system for unobtrusive cardiac auscultation. Microprocessors and Microsystems, 101, 104906. https://doi.org/https://doi.org/10.1016/j.micpro.2023.104906 DOI: 10.1016/j.micpro.2023.104906
Condran, S., Bewong, M., Islam, M. Z., Maphosa, L., & Zheng, L. (2022). Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades. IEEE Access, 10, 73786–73803. https://doi.org/10.1109/ACCESS.2022.3188649 DOI: 10.1109/access.2022.3188649
Elmir, Y., Himeur, Y., & Amira, A. (2023). ECG classification using Deep CNN and Gramian Angular Field. Proceedings - IEEE 9th International Conference on Big Data Computing Service and Applications, BigDataService 2023, 137–141. https://doi.org/10.48550/arXiv.2308.02395 DOI: 10.48550/arxiv.2308.02395
Falaschetti, L., Alessandrini, M., Biagetti, G., Crippa, P., & Turchetti, C. (2022). ECG-Based Arrhythmia Classification using Recurrent Neural Networks in Embedded Systems. Procedia Computer Science, 207, 3473–3481. https://doi.org/10.1016/j.procs.2022.09.406 DOI: 10.1016/j.procs.2022.09.406
Farag, M. M. (2023). A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge. Sensors, 23(3), 1–23. https://doi.org/10.3390/s23031365 DOI: 10.3390/s23031365
Fikri, M. R., Soesanti, I., & Nugroho, H. A. (2021). ECG Signal Classification Review. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 5(1), 1–6.
Hizem, M., Bousbia, L., Ben Dhiab, Y., Aoueileyine, M. O. E., & Bouallegue, R. (2025). Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications. Sensors, 25(8), 1–24. https://doi.org/10.3390/s25082496 DOI: 10.3390/s25082496
Khan, F., Yu, X., Yuan, Z., & ur Rehman, A. (2023). ECG classification using 1-D convolutional deep residual neural network. PLoS ONE, 18(4 April). https://doi.org/10.1371/journal.pone.0284791 DOI: 10.1371/journal.pone.0284791
Khanh, N. Van, Khang, T. V., Khai, N. M., & Em, T. V. T. (2022). Realtime Non-invasive Fault Diagnosis of Three-phase Induction Motor. Journal of Technical Education Science, 72B, 1–11.
Khorrami, H., & Moavenian, M. (2010). A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Systems with Applications, 37(8), 5751–5757. https://doi.org/10.1016/j.eswa.2010.02.033 DOI: 10.1016/j.eswa.2010.02.033
Kim, E., Kim, J., Park, J., Ko, H., & Kyung, Y. (2023). TinyML-Based Classification in an ECG Monitoring Embedded System. Computers, Materials & Continua, 75, 1751–1764. https://doi.org/ 10.32604/cmc.2023.031663 DOI: 10.32604/cmc.2023.031663
Kiranyaz, S., Ince, T., & Gabbouj, M. (2016). Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks. IEEE Transactions on Biomedical Engineering, 63(3), 664–675.
Kumar, M. A., & Chakrapani, A. (2022). Classification of ECG signal using FFT based improved Alexnet classifier. PLoS ONE, 17(9 September), 1–22. https://doi.org/10.1371/journal.pone.0274225 DOI: 10.1371/journal.pone.0274225
Laureanti, R., Bilucaglia, M., Zito, M., Circi, R., Fici, A., Rivetti, F., Valesi, R., Oldrini, C., Mainardi, L. T., & Russo, V. (2020). Emotion assessment using Machine Learning and low-cost wearable devices. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2020-July, 576–579. https://doi.org/10.1109/EMBC44109.2020. 9175221 DOI: 10.1109/embc44109.2020
Li, F., Feng, Y., Fu, Y., Wu, Z., & Yuan, J. (2025). Design of a wearable ECG signal monitoring system and research on arrhythmia classification methods. Proceedings of the 2024 5th International Symposium on Artificial Intelligence for Medicine Science, 102–107. https://doi.org/10.1145/ 3706890.3706907.
Liu, C. L., Xiao, B., & Tsai, C. F. (2025). ECG-STAR: Spatio-temporal attention residual networks for multi-label ECG abnormality classification. Information Sciences, 717, 122273. https://doi.org/10.1016/j.ins.2025.122273 DOI: 10.1016/j.ins.2025.122273
Liu, M., Shao, N., Zheng, C., & Wang, J. (2021). Real Time Arrhythmia Monitoring and Classification Based on Edge Computing and DNN. Wireless Communications and Mobile Computing, 2021, 5563338. https://doi.org/10.1155/2021/5563338 DOI: 10.1155/2021/5563338
Makhoul, J. (1980). A Fast Cosine Transform in One and Two Dimensions. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(1), 27–34. https://doi.org/10.1109/TASSP. 1980.1163351 DOI: 10.1109/tassp
Marasović, T., & Papić, V. (2018). A Comparative Study of FFT, DCT, and DWT for Efficient Arrhytmia Classification in RP-RF Framework. International Journal of E-Health and Medical Communications, 9(1), 35–49. https://doi.org/10.4018/IJEHMC.2018010103 DOI: 10.4018/ijehmc.2018010103
Mborgerding, M. (2021). A fast Fourier transform (FFT) library that tries to keep it simple [Computer software]. Github. https://github.com/mborgerding/kissfft
Nayuki. (2018). Fast discrete cosine transform algorithms. Nayuki. https://www.nayuki.io/ page/fast-discrete-cosine-transform-algorithms
Nguyen, V. K., Tran, V. K., Pham, H., Nguyen, V. M., Nguyen, H. D., & Nguyen, C. N. (2023). A multi-microcontroller-based hardware for deploying Tiny machine learning model. International Journal of Electrical and Computer Engineering, 13(5), 5727–5736. https://doi.org/10.11591/ ijece.v13i5.pp5727-5736
Panwar, A., Narendra, M., Arya, A., Raj, R., & Kumar, A. (2025). Integrated portable ECG monitoring system with CNN classification for early arrhythmia detection. Frontiers in Digital Health, 7, 1–13. https://doi.org/10.3389/fdgth.2025.1535335 DOI: 10.3389/fdgth.2025.1535335
Pham, M. H. (2024). The global burden of cardiovascular disease over the past 40 years: our challenges and actions. Journal of Vietnamese Cardiology, 107S(1). https://jvc.vnha.org.vn/tmh/article/view/785
Saadatnejad, S., Oveisi, M., & Hashemi, M. (2020). LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices. IEEE Journal of Biomedical and Health Informatics, 24(2), 515–523. https://doi.org/10.1109/JBHI.2019.2911367 DOI: 10.1109/jbhi.2019.2911367
Song, Y., Chen, J., & Zhang, R. (2023). Heart Rate Estimation from Incomplete Electrocardiography Signals. Sensors, 23(2), 1–20. https://doi.org/10.3390/s23020597 DOI: 10.3390/s23020597
Sun, B., Bayes, S., Abotaleb, A. M., & Hassan, M. (2023). The Case for tinyML in Healthcare: CNNs for Real-Time On-Edge Blood Pressure Estimation. Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 629–638. https://doi.org/10.1145/3555776.3577747 DOI: 10.1145/3555776.3577747
Thilagavathy, R., Srivatsan, R., Sreekarun, S., Sudeshna, D., Lakshmi Priya, P., & Venkataramani, B. (2020). Real-Time ECG Signal Feature Extraction and Classification using Support Vector Machine. 2020 International Conference on Contemporary Computing and Applications, IC3A 2020, 44–48. https://doi.org/10.1109/IC3A48958.2020.233266 DOI: 10.1109/ic3a48958.2020.233266
Ullah, W., Siddique, I., Zulqarnain, R. M., Alam, M. M., Ahmad, I., & Raza, U. A. (2021). Classification of Arrhythmia in Heartbeat Detection Using Deep Learning. Computational Intelligence and Neuroscience, 2021, 2195922. https://doi.org/10.1155/2021/2195922 DOI: 10.1155/2021/2195922
Vu, T., Petty, T., Yakut, K., Usman, M., Xue, W., Haas, F. M., Hirsh, R. A., & Zhao, X. (2023). Real-time arrhythmia detection using convolutional neural networks. Frontiers in Big Data, 6. https://doi.org/10.3389/fdata.2023.1270756 DOI: 10.3389/fdata.2023.1270756
World Health Organization. (2021). Cardiovascular diseases (CVDs). https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Xing, Y., Zhang, L., Hou, Z., Li, X., Shi, Y., Yuan, Y., Zhang, F., Liang, S., Li, Z., & Yan, L. (2022). Accurate ECG Classification Based on Spiking Neural Network and Attentional Mechanism for Real-Time Implementation on Personal Portable Devices. Electronics (Switzerland), 11(12). https://doi.org/10.3390/electronics11121889 DOI: 10.3390/electronics11121889
Zhao, Y., Cheng, J., Zhang, P., & Peng, X. (2020). ECG Classification Using Deep CNN Improved by Wavelet Transform. In Computers, Materials & Continua, 64(3). https://doi.org/10.32604/cmc.2020.09938 DOI: 10.32604/cmc.2020.09938

Download PDF

[ref-1] Ahmed, K., & Hassan, M. (2022). tinyCare: A tinyML-based Low-Cost Continuous Blood Pressure Estimation on the Extreme Edge. 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI), 264–275. https://doi.org/10.1109/ICHI54592.2022.00047 DOI: 10.1109/ichi54592.2022.00047

[ref-2] Amirshahi, A., & Hashemi, M. (2021). ECG classification algorithm based on STDP and R-STDP neural networks for real-time monitoring on ultra low-power personal wearable devices. IEEE Transactions on Biomedical Circuits and Systems, 13(6), 1483–1493. https://doi.org/10.1109/ TBCAS.2019.2948920

[ref-3] BinMakhashen, G. M., Arafat, S. M. A., & Dohrmann, M. L. (2014). CAD detection using neural network fusion of the 12 lead stress ECG system. Information Fusion, 6(7), 8.

[ref-4] Caesarendra, W., Hishamuddin, T. A., Lai, D. T. C., Husaini, A., Nurhasanah, L., Glowacz, A., &

[ref-5] Alfarisy, G. A. F. (2022). An Embedded System Using Convolutional Neural Network Model for Online and Real-Time ECG Signal Classification and Prediction. Diagnostics, 12(4), 1–19. https://doi.org/10.3390/diagnostics12040795 DOI: 10.3390/diagnostics12040795

[ref-6] Cai, J., Zhou, G., Dong, M., Hu, X., Liu, G., & Ni, W. (2021). Real-Time Arrhythmia Classification Algorithm Using Time-Domain ECG Feature Based on FFNN and CNN. Mathematical Problems in Engineering, 2021, 6648432. https://doi.org/10.1155/2021/6648432 DOI: 10.1155/2021/6648432

[ref-7] Chowdhury, D., & Chattopadhyay, M. (2023). An on-device machine learning assisted system for unobtrusive cardiac auscultation. Microprocessors and Microsystems, 101, 104906. https://doi.org/https://doi.org/10.1016/j.micpro.2023.104906 DOI: 10.1016/j.micpro.2023.104906

[ref-8] Condran, S., Bewong, M., Islam, M. Z., Maphosa, L., & Zheng, L. (2022). Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades. IEEE Access, 10, 73786–73803. https://doi.org/10.1109/ACCESS.2022.3188649 DOI: 10.1109/access.2022.3188649

[ref-9] Elmir, Y., Himeur, Y., & Amira, A. (2023). ECG classification using Deep CNN and Gramian Angular Field. Proceedings - IEEE 9th International Conference on Big Data Computing Service and Applications, BigDataService 2023, 137–141. https://doi.org/10.48550/arXiv.2308.02395 DOI: 10.48550/arxiv.2308.02395

[ref-10] Falaschetti, L., Alessandrini, M., Biagetti, G., Crippa, P., & Turchetti, C. (2022). ECG-Based Arrhythmia Classification using Recurrent Neural Networks in Embedded Systems. Procedia Computer Science, 207, 3473–3481. https://doi.org/10.1016/j.procs.2022.09.406 DOI: 10.1016/j.procs.2022.09.406

[ref-11] Farag, M. M. (2023). A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge. Sensors, 23(3), 1–23. https://doi.org/10.3390/s23031365 DOI: 10.3390/s23031365

[ref-12] Fikri, M. R., Soesanti, I., & Nugroho, H. A. (2021). ECG Signal Classification Review. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 5(1), 1–6.

[ref-13] Hizem, M., Bousbia, L., Ben Dhiab, Y., Aoueileyine, M. O. E., & Bouallegue, R. (2025). Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications. Sensors, 25(8), 1–24. https://doi.org/10.3390/s25082496 DOI: 10.3390/s25082496

[ref-14] Khan, F., Yu, X., Yuan, Z., & ur Rehman, A. (2023). ECG classification using 1-D convolutional deep residual neural network. PLoS ONE, 18(4 April). https://doi.org/10.1371/journal.pone.0284791 DOI: 10.1371/journal.pone.0284791

[ref-15] Khanh, N. Van, Khang, T. V., Khai, N. M., & Em, T. V. T. (2022). Realtime Non-invasive Fault Diagnosis of Three-phase Induction Motor. Journal of Technical Education Science, 72B, 1–11.

[ref-16] Khorrami, H., & Moavenian, M. (2010). A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Systems with Applications, 37(8), 5751–5757. https://doi.org/10.1016/j.eswa.2010.02.033 DOI: 10.1016/j.eswa.2010.02.033

[ref-17] Kim, E., Kim, J., Park, J., Ko, H., & Kyung, Y. (2023). TinyML-Based Classification in an ECG Monitoring Embedded System. Computers, Materials & Continua, 75, 1751–1764. https://doi.org/ 10.32604/cmc.2023.031663 DOI: 10.32604/cmc.2023.031663

[ref-18] Kiranyaz, S., Ince, T., & Gabbouj, M. (2016). Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks. IEEE Transactions on Biomedical Engineering, 63(3), 664–675.

[ref-19] Kumar, M. A., & Chakrapani, A. (2022). Classification of ECG signal using FFT based improved Alexnet classifier. PLoS ONE, 17(9 September), 1–22. https://doi.org/10.1371/journal.pone.0274225 DOI: 10.1371/journal.pone.0274225

[ref-20] Laureanti, R., Bilucaglia, M., Zito, M., Circi, R., Fici, A., Rivetti, F., Valesi, R., Oldrini, C., Mainardi, L. T., & Russo, V. (2020). Emotion assessment using Machine Learning and low-cost wearable devices. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2020-July, 576–579. https://doi.org/10.1109/EMBC44109.2020. 9175221 DOI: 10.1109/embc44109.2020

[ref-21] Li, F., Feng, Y., Fu, Y., Wu, Z., & Yuan, J. (2025). Design of a wearable ECG signal monitoring system and research on arrhythmia classification methods. Proceedings of the 2024 5th International Symposium on Artificial Intelligence for Medicine Science, 102–107. https://doi.org/10.1145/ 3706890.3706907.

[ref-22] Liu, C. L., Xiao, B., & Tsai, C. F. (2025). ECG-STAR: Spatio-temporal attention residual networks for multi-label ECG abnormality classification. Information Sciences, 717, 122273. https://doi.org/10.1016/j.ins.2025.122273 DOI: 10.1016/j.ins.2025.122273

[ref-23] Liu, M., Shao, N., Zheng, C., & Wang, J. (2021). Real Time Arrhythmia Monitoring and Classification Based on Edge Computing and DNN. Wireless Communications and Mobile Computing, 2021, 5563338. https://doi.org/10.1155/2021/5563338 DOI: 10.1155/2021/5563338

[ref-24] Makhoul, J. (1980). A Fast Cosine Transform in One and Two Dimensions. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(1), 27–34. https://doi.org/10.1109/TASSP. 1980.1163351 DOI: 10.1109/tassp

[ref-25] Marasović, T., & Papić, V. (2018). A Comparative Study of FFT, DCT, and DWT for Efficient Arrhytmia Classification in RP-RF Framework. International Journal of E-Health and Medical Communications, 9(1), 35–49. https://doi.org/10.4018/IJEHMC.2018010103 DOI: 10.4018/ijehmc.2018010103

[ref-26] Mborgerding, M. (2021). A fast Fourier transform (FFT) library that tries to keep it simple [Computer software]. Github. https://github.com/mborgerding/kissfft

[ref-27] Nayuki. (2018). Fast discrete cosine transform algorithms. Nayuki. https://www.nayuki.io/ page/fast-discrete-cosine-transform-algorithms

[ref-28] Nguyen, V. K., Tran, V. K., Pham, H., Nguyen, V. M., Nguyen, H. D., & Nguyen, C. N. (2023). A multi-microcontroller-based hardware for deploying Tiny machine learning model. International Journal of Electrical and Computer Engineering, 13(5), 5727–5736. https://doi.org/10.11591/ ijece.v13i5.pp5727-5736

[ref-29] Panwar, A., Narendra, M., Arya, A., Raj, R., & Kumar, A. (2025). Integrated portable ECG monitoring system with CNN classification for early arrhythmia detection. Frontiers in Digital Health, 7, 1–13. https://doi.org/10.3389/fdgth.2025.1535335 DOI: 10.3389/fdgth.2025.1535335

[ref-30] Pham, M. H. (2024). The global burden of cardiovascular disease over the past 40 years: our challenges and actions. Journal of Vietnamese Cardiology, 107S(1). https://jvc.vnha.org.vn/tmh/article/view/785

[ref-31] Saadatnejad, S., Oveisi, M., & Hashemi, M. (2020). LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices. IEEE Journal of Biomedical and Health Informatics, 24(2), 515–523. https://doi.org/10.1109/JBHI.2019.2911367 DOI: 10.1109/jbhi.2019.2911367

[ref-32] Song, Y., Chen, J., & Zhang, R. (2023). Heart Rate Estimation from Incomplete Electrocardiography Signals. Sensors, 23(2), 1–20. https://doi.org/10.3390/s23020597 DOI: 10.3390/s23020597

[ref-33] Sun, B., Bayes, S., Abotaleb, A. M., & Hassan, M. (2023). The Case for tinyML in Healthcare: CNNs for Real-Time On-Edge Blood Pressure Estimation. Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 629–638. https://doi.org/10.1145/3555776.3577747 DOI: 10.1145/3555776.3577747

[ref-34] Thilagavathy, R., Srivatsan, R., Sreekarun, S., Sudeshna, D., Lakshmi Priya, P., & Venkataramani, B. (2020). Real-Time ECG Signal Feature Extraction and Classification using Support Vector Machine. 2020 International Conference on Contemporary Computing and Applications, IC3A 2020, 44–48. https://doi.org/10.1109/IC3A48958.2020.233266 DOI: 10.1109/ic3a48958.2020.233266

[ref-35] Ullah, W., Siddique, I., Zulqarnain, R. M., Alam, M. M., Ahmad, I., & Raza, U. A. (2021). Classification of Arrhythmia in Heartbeat Detection Using Deep Learning. Computational Intelligence and Neuroscience, 2021, 2195922. https://doi.org/10.1155/2021/2195922 DOI: 10.1155/2021/2195922

[ref-36] Vu, T., Petty, T., Yakut, K., Usman, M., Xue, W., Haas, F. M., Hirsh, R. A., & Zhao, X. (2023). Real-time arrhythmia detection using convolutional neural networks. Frontiers in Big Data, 6. https://doi.org/10.3389/fdata.2023.1270756 DOI: 10.3389/fdata.2023.1270756

[ref-37] World Health Organization. (2021). Cardiovascular diseases (CVDs). https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)

[ref-38] Xing, Y., Zhang, L., Hou, Z., Li, X., Shi, Y., Yuan, Y., Zhang, F., Liang, S., Li, Z., & Yan, L. (2022). Accurate ECG Classification Based on Spiking Neural Network and Attentional Mechanism for Real-Time Implementation on Personal Portable Devices. Electronics (Switzerland), 11(12). https://doi.org/10.3390/electronics11121889 DOI: 10.3390/electronics11121889

[ref-39] Zhao, Y., Cheng, J., Zhang, P., & Peng, X. (2020). ECG Classification Using Deep CNN Improved by Wavelet Transform. In Computers, Materials & Continua, 64(3). https://doi.org/10.32604/cmc.2020.09938 DOI: 10.32604/cmc.2020.09938