Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64665
Volume:
10 |
6 |
Jun 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
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.

BioMedical Engineering Department
Holycross Engineering College
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
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.
[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.
Publication Date
6/9/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
5
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...