Research Article
Machine Learning Techniques for Neonatal Apnea Prediction
Rudresh D. Shirwaikar,
U. Dinesh Acharya,
Krishnamoorthi Makkithaya,
M. Surulivelrajan and Leslie Edward Simon Lewis
Detection of Apnea Bradycardia from ECG Signals of Preterm Infants Using Layered Hidden Markov Model Annals of Biomedical Engineering |
Predicting responses to mechanical ventilation for preterm
infants with acute respiratory illness using artificial neural
networks International Journal for Numerical Methods in Biomedical Engineering |
Predictive Decision Support Analytic Model for Intelligent Obstetric Risks Management Lecture Notes in Networks and Systems |
Sensitivity study of the economics of a floating offshore wind farm. The case study of the SATH® concrete platform in the Atlantic waters of Europe Energy Reports |
Gold nanoparticle-based aptasensors: A promising perspective for early-stage detection of cancer biomarkers Materials Today Communications |
Spectroscopic and electrical analysis of spray deposited copper oxide thin films Materials Today Communications |
Assessment of mercury contamination and food composition in commercially important marine fishes in the southern South China Sea Regional Studies in Marine Science |
Trace metal concentration in common fishes from the Lagos lagoon, Southwestern Nigeria Regional Studies in Marine Science |
Risk Stratification of Neonates using Machine Learning Techniques 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) |
Predicting Bradycardia in Preterm Infants Using Point Process
Analysis of Heart Rate IEEE Transactions on Biomedical Engineering Vol. 64, Issue 9, 2300, 2017 |
Real-Time Bradycardia Prediction in Preterm Infants Using a Dynamic System Identification Approach Journal of Engineering and Science in Medical Diagnostics and Therapy Vol. 3, Issue 1, , 2020 |
Neonatal Disease Prediction Using Machine Learning Techniques Journal of Healthcare Engineering |