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Signal Processing of Heart Rate for Predicting Sepsis in Premature Neonates Publisher



Mirnia K1 ; Heidarzadeh M2 ; Afjeh SA3 ; Alizadeh P4 ; Kashan AA5 ; Bordbar A6 ; Maghsoudi A7
Authors

Source: Journal of Medical Signals and Sensors Published:2021


Abstract

The heart rate characteristic (HeRO score) is a figure derived from the analysis of premature neonate's electrocardiogram signals, and can be used to detect infection before the onset of clinical symptoms. The United States and Europe accept this diagnostic technique, but we require more tests to prove its efficacy. This method is not accepted in other developed countries so far. The present study aimed to investigate changes in the heart characteristics of two neonates in Akbar Abadi Hospital in Tehran. Experts chose one newborn as a sepsis case, and the other neonate was healthy. The results were analyzed and compared with previous studies. In this research, a group of five neonates was selected randomly from the neonatal intensive care unit, and cardiac leads were attached to them for recording heart rates. We selected two neonates from the five cases, as a case (proven sepsis) and control, to analyze heart rate variability (HRV). Then, we compared the differences in the heart rate of both neonates. Analysis of HRV of these two neonates showed that the pattern of HRV is compatible with reports from US studies. Considering the results of this study, heart rates and their analysis can provide useful indicators for mathematical modeling before the onset of clinical symptoms in newborns. © 2021 Journal of Medical Signals and Sensors Published by Wolters Kluwer-Medknow.
1. Identifying Optimal Features From Heart Rate Variability for Early Detection of Sepsis in Pediatric Intensive Care, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS (2019)
2. Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit Using Convolutional Neural Network Classifiers, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS (2020)
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