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EEG-Based Neonatal Sleep Stage Classification Using Ensemble Learning

Saadullah Farooq Abbasi1,2, Harun Jamil3, Wei Chen2,*

1 School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
2 Center for Intelligent Medical Electronics, Department of Electronic Engineering, Fudan University, 200433, China
3 Department of Computer Engineering, Jeju National University, Jejusi, Jeju Special Self-Governing Province, 63243, Korea

* Corresponding Author: Wei Chen. Email: email

(This article belongs to this Special Issue: Advancements in Lightweight AI for Constrained Internet of Things Devices for Smart Cities)

Computers, Materials & Continua 2022, 70(3), 4619-4633.


Sleep stage classification can provide important information regarding neonatal brain development and maturation. Visual annotation, using polysomnography (PSG), is considered as a gold standard for neonatal sleep stage classification. However, visual annotation is time consuming and needs professional neurologists. For this reason, an internet of things and ensemble-based automatic sleep stage classification has been proposed in this study. 12 EEG features, from 9 bipolar channels, were used to train and test the base classifiers including convolutional neural network, support vector machine, and multilayer perceptron. Bagging and stacking ensembles are then used to combine the outputs for final classification. The proposed algorithm can reach a mean kappa of 0.73 and 0.66 for 2-stage and 3-stage (wake, active sleep, and quiet sleep) classification, respectively. The proposed network works as a semi-real time application because a smoothing filter is used to hold the sleep stage for 3 min. The high-performance parameters and its ability to work in semi real-time makes it a promising candidate for use in hospitalized newborn infants.


Cite This Article

S. Farooq Abbasi, H. Jamil and W. Chen, "Eeg-based neonatal sleep stage classification using ensemble learning," Computers, Materials & Continua, vol. 70, no.3, pp. 4619–4633, 2022.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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