Deep CNN-LSTM Framework for Reliable PVC Detection in Multi-Database ECG Analysis
Keywords:
Arrhythmia detection, Premature Ventricular Contractions, ECG Classification, Deep Learning, CNN-LSTM hybrid Model, MIT-BIH database, INCART databaseAbstract
Premature Ventricular Contractions (PVCs) are early heartbeats that originate in the ventricles, briefly disrupting theheart’s natural rhythm. While often benign and linked to stress, caffeine, or fatigue, frequent PVCs may signal underlying cardiac concerns and should be evaluated
References
Alamatsaz, M. H., Chatterjee, S., & Moradi, M. H (2022). Lightweight CNN-LSTM model for real-time
arrhythmia classification. Biomedical Signal Processing and Control, 72, 103340. https://doi.org/10.1016/j.bspc.2021.103340
He, Y., Sun, Y., & Qiu, H. (2022). SEVGGNet-LSTM: A hybrid deep learning model for ECG arrhythmia classification. Journal of Healthcare En
gineering,
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Published
2024-12-16
How to Cite
Prakash M B, Dr. Harish H M. (2024). Deep CNN-LSTM Framework for Reliable PVC Detection in Multi-Database ECG Analysis . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 5105–5113. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3100
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