DETECTION OF CARDIAC ARREST IN NEWBORN BABIES USING NEURAL NETWORKS
Alt Text: Detection of Cardiac Arrest in Newborn Babies Using Neural Networks
Title: Detection of Cardiac Arrest in Newborn Babies Using Neural Networks
Caption: Utilizing artificial neural networks (ANN) for early detection of cardiac arrest in newborns.
Description: This study presents an advanced detection and severity prediction model based on artificial neural networks (ANN) for identifying cardiac arrest in newborns. Using physiological parameters and imaging techniques such as echocardiography and computed tomography, the model aims to assist medical professionals in the Cardiac Intensive Care Unit (CICU).
Keywords: Cardiac Arrest Detection, Neural Networks, Newborn Health, Medical AI, Early Diagnosis
International Journal of Engineering and Techniques – Volume 10 Issue 3, June 2024
Dr. P. Babu1, K. Chiranjeevi2
1Associate Professor, Department of Computer Science & Engineering, Geethanjali Institute of Science and Technology, Gangavaram, Andhra Pradesh, India.
2Assistant Professor, Department of Computer Science & Engineering, Geethanjali Institute of Science and Technology, Gangavaram, Andhra Pradesh, India.
Abstract
Cardiac arrest in newborn babies is an alarming yet common medical emergency, and early detection is vital for providing optimal care. This study focuses on identifying key indicators and biomarkers of cardiac arrest in newborns and developing accurate diagnostic tools for early detection. Imaging techniques such as echocardiography and computed tomography enhance detection capabilities. We aim to develop a detection and severity prediction model using artificial neural networks (ANN) to monitor neonate physiological parameters in the Cardiac Intensive Care Unit (CICU), improving early diagnosis and patient outcomes.
Keywords
Cardiac Arrest Detection, Neural Networks, Newborn Health, Medical AI, Early Diagnosis
How to Cite
Babu, P., Chiranjeevi, K., “Detection of Cardiac Arrest in Newborn Babies Using Neural Networks,” International Journal of Engineering and Techniques, Volume 10, Issue 3, June 2024. ISSN 2395-1303
Post Comment