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How to Administer Near-Infrared Spectroscopy in Critically ill Neonates, Infants, and Children
Published on: August 19, 2020
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Near-Infrared Spectroscopy for Neonatal Sleep Classification
Naser Hakimi1, Emad Arasteh1, Maren Zahn2,3
1Department of Neonatology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands.
Sensors (Basel, Switzerland)
|November 9, 2024
Summary
This study shows that near-infrared spectroscopy (NIRS) can accurately classify sleep states in preterm infants using a deep convolutional neural network (CNN), aiding neurological development monitoring. This NIRS-based method offers a portable solution for neonatal sleep assessment.
Area of Science:
- Neonatal neurology and sleep science.
- Biomedical engineering and signal processing.
Background:
- Sleep states, active sleep (AS) and quiet sleep (QS), are critical for brain development in preterm infants.
- Monitoring neonatal sleep is essential for promoting neurological maturation and well-being, especially in high-risk infants.
- Respiratory rate (RR) and heart rate (HR) are key indicators in neonatal sleep assessment systems.
Purpose of the Study:
- To introduce a comprehensive sleep classification approach for preterm infants using high-frequency near-infrared spectroscopy (NIRS) signals.
- To evaluate the performance of a deep convolutional neural network (CNN) model for classifying active sleep (AS) and quiet sleep (QS) states.
- To compare the CNN model's efficacy against benchmark machine learning classifiers.
Main Methods:
- High-frequency NIRS signals (100 Hz) were recorded from nine preterm infants in a neonatal intensive care unit.
- Eight features, including HR, RR, motion parameters, and neural activity proxies, were extracted from NIRS signals.
- A deep convolutional neural network (CNN) was trained and validated using two cross-validation approaches to classify sleep states.
Main Results:
- The CNN model achieved high performance metrics, including 88% accuracy, 94% balanced accuracy, 91% F1-score, 95% Kappa, and 96% AUC-ROC in data pooling cross-validation.
- Random Forest (RF) and XGBoost (XGB) classifiers showed comparable accuracy to the CNN model.
- The study confirmed the feasibility of extracting NIRS-based HR and RR for effective neonatal sleep state assessment.
Conclusions:
- High-frequency NIRS data, combined with extracted HR and RR, provides a viable method for assessing neonatal sleep states, even in intensive care settings.
- The user-friendly, portable, and less complex NIRS approach has potential applications beyond the NICU.
- This research offers a promising advancement in neonatal sleep assessment, contributing to improved infant health and developmental outcomes.
