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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM64665

AI-Based ECG Signal Classification for Arrhythmia Detection

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64665

Volume:

10 |

6 |

Jun 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

C. Deiva Ragavi

C. Deiva Ragavi

BioMedical Engineering Department

Holycross Engineering College

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Abstract

The use of ECG is common for diagnosing heart disorders including arrhythmia. Physicians can manually interpret traditional ECG data but this process takes a considerable amount of time. In this study, we propose an Artificial Intelligence based method which uses Machine learning and Deep learning to automatically classify the types of ECG signal patterns. Our proposed approach will be able to improve the efficiency in detecting arrhythmia.

KEYWORDS

ECG signal Arrhythmia Detection Artificial intelligence Machine Learning Deep Learning.

Cite This Article

C. Deiva Ragavi, "AI-Based ECG Signal Classification for Arrhythmia Detection," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-4, Jun 2026.

Full Article/Conclusion

The suggested AI based ECG signal classification system uses deep learning techniques to successfully identify arrhythmia. When compared to conventional techniques, the CNN model increases the speed and accuracy of ECG analysis. Early diagnosis and real-time monitoring are supported by the system. Cloud-based healthcare monitoring and wearable device integration are potential future enhancements.

References

[1] S. Kiranyaz, T. Ince, “Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 3, pp. 664–675, 2016 [2] P. Raj Purkar et al., “Cardiologist-Level Arrhythmia Detection with Deep Neural Networks,” Nature Medicine, vol. 25, no. 1, pp. 65–69, 2019. [3] G. B. Moody and R. G. Mark, “The MIT-BIH Arrhythmia Database,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45–50, 2001. [4] U. R. Acharya et al., “A Deep Convolutional Neural Network Model for Automated Diagnosis of Arrhythmia Using ECG Signals,” Information Sciences, vol. 415, pp. 190–198, 2017. [5] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.[6] O. Faust, U. R. Acharya, H. Adeli, and A. Adeli, “Wavelet-Based ECG Processing for Computer-Aided Diagnosis,” Journal of Medical Systems, vol. 35, no. 4, pp. 1–12, 2011. [7] M. Zubair, J. Kim, and C. Yoon, “An Automated ECG Beat Classification System Using CNN,” Biomedical Signal Processing and Control, vol. 47, pp. 1–10, 2019. [8] T. Ince, S. Kiranyaz, “A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 5, pp. 1415–1426, 2009. [9] A. L. Goldberger et al., “Physio Bank, Physio Toolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000. [10] H. He, E. A. Garcia, “Learning from Imbalanced Data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263–1284, 2009.

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Publication Date

6/9/2026

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Double-Blind Peer Review

Review Rounds: 2

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