A Novel NICU Sleep State Stratification: Multiperspective Features, Adaptive Feature Selection and Ensemble Model

The examination of sleep patterns in newborns, particularly premature infants, is crucial for understanding neonatal development. This study presents an automated multi-sleep state classification approach for infants in neonatal intensive care units (NICU) using multiperspective feature extraction methodologies and machine learning to assess their neurological and physical development. The datasets for this study were collected from Children's Hospital Fudan University, Shanghai and consist of electroencephalography (EEG) recordings from two datasets, one comprising 64 neonates and the other 19 neonates. The proposed study involves six major phases: data collection, data annotation, preprocessing, multi-perspective feature extraction, adaptive feature selection, and classification. During the preprocessing phase, noise reduction is achieved using the multi-scale principal component analysis (MSPCA) method. From each epoch of eight EEG channels, a diverse ensemble of 1,976 features is extracted. This extraction employs a combination of stationary wavelet transform (SWT), flexible analytical wavelet transform (FAWT), spectral features based on α, β, θ, and δ brain waves, and temporal features refined through adaptive feature algorithm. In terms of performance, the proposed approach demonstrates significant improvements over existing studies. Using a single EEG channel, the model achieves accuracy of 81.45% and a Kappa score of 71.75%. With four channels, these metrics increase to 83.71% accuracy and a 74.04% Kappa score. Furthermore, utilizing all eight channels, the mean accuracy reaches to 85.62%, and the Kappa score rises to 76.30%. To evaluate the model's effectiveness, a leave-one-subject-out cross-validation method is employed. This thorough analysis validates the reliability of the classification approach. This makes it a promising method for monitoring and assessing sleep patterns in neonates.