Near-Infrared Spectroscopy for Neonatal Sleep Classification.

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.