EARLY DETECTION OF CARDIAC ARREST IN NEWBORN BABIES

Authors

  • M.Kavitha,Doma Nandini , Kakularam Akhila , C. Priyambica

Keywords:

Bagging classifiers, Deep Neural Networks, Machine Learning, Deep Learning, Cardiac capture , Babies.

Abstract

Babies with heart arrest may be experiencing a fundamental restorative crisis that need prompt intervention to ensure timely treatment and positive outcomes.Using methods from machine learning and deep learning, we provide a fresh perspective on early discovery in this term paper.

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References

Raghunath, S., Cerna, A. E. U., Jing, L., vanMaanen, D. P., Stough, J., Hartzel, D. N., ... & Fornwalt, B. K. (2019). Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data. arXiv preprint arXiv:1904.07032.

Bose, S. N., Verigan, A., Hanson, J., Ahumada, L. M., Ghazarian, S. R., Goldenberg, N. A., ... & Jacobs, J. P. (2019). Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data. Cardiology in the young, 29(11), 1340-1348.

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Published

2023-12-24

How to Cite

M.Kavitha,Doma Nandini , Kakularam Akhila , C. Priyambica. (2023). EARLY DETECTION OF CARDIAC ARREST IN NEWBORN BABIES . Pegem Journal of Education and Instruction, 13(4), 659–673. Retrieved from https://pegegog.net/index.php/pegegog/article/view/4029

Issue

Vol. 13 No. 4 (2023): PEGEGOG

Section

Article

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