Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States

Abstract

The classification of sleep state in preterm infants, particularly in distinguishing between active sleep (AS) and quiet sleep (QS), has been investigated using cardiorespiratory information such as electrocardiography (ECG) and respiratory signals. However, accurately differentiating between AS and wake remains challenging; therefore, there is a pressing need to include additional information to further enhance the classification performance. To address the challenge, this study explores the effectiveness of incorporating video-based actigraphy analysis alongside cardiorespiratory signals for classifying the sleep states of preterm infants. The study enrolled eight preterm infants, and a total of 91 features were extracted from ECG, respiratory signals, and video-based actigraphy. By employing an extremely randomized trees (ET) algorithm and leave-one-subject-out cross-validation, a kappa score of 0.33 was achieved for the classification of AS, QS, and wake using cardiorespiratory features only. The kappa score significantly improved to 0.39 when incorporating eight video-based actigraphy features. Furthermore, the classification performance of AS and wake also improved, showing a kappa score increase of 0.21. These suggest that combining video-based actigraphy with cardiorespiratory signals can potentially enhance the performance of sleep-state classification in preterm infants. In addition, we highlighted the distinct strengths and limitations of video-based actigraphy and cardiorespiratory data in classifying specific sleep states.

Keywords: cardiorespiratory signal; preterm infant; sleep-state classification; video-based actigraphy.

Figure 1

Flowchart for automated sleep classification based on different feature sets. CRI: Cardiorespiratory interaction; 3DRS: 3D recursive search (3DRS) motion estimation algorithm. SMOTE: Synthetic minority oversampling technique. ET: Extremely randomized trees.

Figure 2

Box plots of the top 10 features in sleep-state classification based on Gini importance across subjects. +: The outliers. Feature names and their description can be found in the Supplementary Material.

Figure 3

Confusion matrices in preterm infant sleep-state classification (AS, QS, and CTW) using feature set: (a) Motion; (b) ECG-Resp-CRI; (c) ECG-Resp-CRI-Motion. The aggregated results of all epochs are presented.

Figure 4

ROC curves for different binary classifications using all cardiorespiratory and motion features for each subject and for all subjects (epochs pooled over subjects). FPR: False positive rate; TPR: True positive rate.