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Identification of High Risk and Low Risk Preterm Neonates in NICU: Pattern Recognition Approach

Identification of High Risk and Low Risk Preterm Neonates in NICU: Pattern Recognition Approach
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Author(s): S. Tejaswini (M. S. Ramaiah Institute of Technology, India), N. Sriraam (M.S. Ramaiah Institute of Technology, India) and Pradeep G. C. M. (M. S. Ramaiah Medical College and Hospital, India)
Copyright: 2020
Pages: 22
Source title: Biomedical and Clinical Engineering for Healthcare Advancement
Source Author(s)/Editor(s): N. Sriraam (Ramaiah Institute of Technology, India)
DOI: 10.4018/978-1-7998-0326-3.ch007

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Abstract

Infant cries are referred as the biological indicator where infant distress is expressed without any external stimulus. One can assess the physiological changes through cry characteristics that help in improving clinical decision. In a typical Neonatal Intensive Care Unit (NICU), recognizing high-risk and low-risk admitted preterm neonates is quite challenging and complex in nature. This chapter attempts to develop pattern recognition-based approach to identify high-risk and low-risk preterm neonates in NICU. Four clinical conditions were considered: two Low Risk (LR) and two High Risk (HR), LR1- Appropriate Gestational Age (AGA), LR2- Intrauterine Growth Restriction (IUGR), HR1-Respiratory Distress Syndrome (RDS), and HR2- Premature Rupture of Membranes (PROM). An overall cry unit of 800 (n=20 per condition) was used for the proposed study. After appropriate pre-processing, Bark Frequency Cepstral Coefficient (BFCC) was estimated using three methods. Schroeder, Zwicker and Terhardt; and Transmiller; and a non-linear Support Vector Machine (SVM) Classifier were employed to discriminate low-risk and high-risk groups. From the simulation results, it was observed that sensitivity specificity and accuracy of 91.47%, 91.42%, and 92.9% respectively were obtained using the BFCC estimated for classifying high risk and low risk with SVM classification.

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