Smart IoT-Based Solutions for Neonatal Sleep Stratification: Single-Dual Channel EEG, AdaptiSelect, Multiview Fusion, and Rotational Ensemble Stacking

作者：Muhammad Irfan, Laishuan Wang, Yan Xu, Abdülhamit Subaşı, Chen Chen, Riku Klén, Tomi Westerlund, Wei Chen · 发表于：IEEE Internet of Things Journal · 年份：2025 · DOI：10.1109/jiot.2025.3558235 · 被引用次数：3 · 研究领域：EEG and Brain-Computer Interfaces、Neonatal and fetal brain pathology

A timely diagnosis and treatment of sleep disorders in neonates during their first week of life is crucial. Current methods for staging neonatal sleep rely heavily on multiple electroencephalography (EEG) channels. These channels increase computational complexity, require a large amount of data to be transferred to the cloud, and may cause skin irritation. We propose an innovative automated classification approach that integrates multi-view feature fusion, AdaptiSelect-based feature optimization, the smart cloud data transfer and reconstruction (STREAM) module, and a rotational ensemble stacking model. The data reduction module significantly enhances edge-cloud systems’ performance in IoT-based healthcare environments by reducing data transmission by a factor of 153.6 through efficient feature selection and compact data packet formation. This module ensures minimal bandwidth usage, reduces the computational load on resource-constrained edge devices, and lowers cloud storage requirements while maintaining full data reconstruction. The dataset used in this research combines two large datasets collected over four years from the Children’s Hospital Fudan University, Shanghai. A unique set of 315 features are extracted from each epoch of a single channel using flexible analytical wavelet transform (FAWT), dual-tree complex wavelet transform (DTCWT), enhanced covariance (ECOV), and spectral features based on α, β, θ, and δ brain waves. These features are refined using AdaptiSelect,...