CARDIAC ECG SIGNAL AUTOMATED ANALYSIS USING AI

Authors

  • MuhammadAli Alikulov Student of Tashkent State Medical University, Tashkent, Uzbekistan Author
  • Ulugbek Isroilov Assistant, Department of Biomedical Engineering, Informatics, and Biophysics, Tashkent State Medical University, Tashkent, Uzbekistan Author

Keywords:

ECG analysis, artificial intelligence, deep learning, cardiac arrhythmia detection, convolutional neural networks, recurrent neural networks, automated diagnosis, cardiovascular health.

Abstract

Accurate analysis of electrocardiogram (ECG) signals is crucial for early detection and management of cardiac arrhythmias and other heart conditions. Manual interpretation of ECG data can be time-consuming, prone to errors, and dependent on clinician expertise. Artificial intelligence (AI) algorithms, particularly deep learning models, offer automated, reliable, and efficient solutions for ECG signal analysis. This paper reviews current AI-based methodologies for automated ECG interpretation, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid approaches. Challenges such as data variability, noise, and model interpretability are discussed. The study highlights the potential of AI-driven systems to support clinicians, enhance diagnostic accuracy, and improve cardiovascular patient care.

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Published

2026-01-15

Issue

Vol. 2 No. 1 (2026)

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

CARDIAC ECG SIGNAL AUTOMATED ANALYSIS USING AI. (2026). Modern American Journal of Medical and Health Sciences, 2(1), 103-109. https://usajournals.org/index.php/1/article/view/1819

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