A HYBRID CNN–LSTM MODEL WITH COMPUTATIONAL  OPTIMIZATION FOR EARLY DETECTION OF CARDIAC  ARRHYTHMIAS FROM ECG SIGNALS

Dr. Sudhakar K,Dr. Venkata Reddy Adama,Dr. Archna G ,K VENUGOPAL RAO

A HYBRID CNN–LSTM MODEL WITH COMPUTATIONAL OPTIMIZATION FOR EARLY DETECTION OF CARDIAC ARRHYTHMIAS FROM ECG SIGNALS

Authors

Dr. Sudhakar K,Dr. Venkata Reddy Adama,Dr. Archna G ,K VENUGOPAL RAO

Keywords:

Cardiac Arrhythmia, Electrocardiogram, Deep Learning, CNN-LSTM, Healthcare Analytics, Computational Optimization, Biomedical Signal Processing, Early Diagnosis.

Abstract

Cardiac arrhythmias are among the leading causes of cardiovascular morbidity and mortality worldwide,necessitating accurate and timely diagnosis for effective clinical intervention. Electrocardiography (ECG) remainsthe most widely used non-invasive diagnostic tool for monitoring cardiac electrical activity and identifying rhythm abnormalities. However, manual interpretation of long-term

References

World Health Organization, “Cardiovascular diseases (CVDs),” WHO Fact Sheet, Geneva, Switzerland, 2021.

J. G. Webster, Medical Instrumentation: Application and Design, 4th ed. Hoboken, NJ, USA: Wiley, 2009.

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Published

2024-08-20

How to Cite

Dr. Sudhakar K,Dr. Venkata Reddy Adama,Dr. Archna G ,K VENUGOPAL RAO. (2024). A HYBRID CNN–LSTM MODEL WITH COMPUTATIONAL OPTIMIZATION FOR EARLY DETECTION OF CARDIAC ARRHYTHMIAS FROM ECG SIGNALS. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8984–8993. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5593