Department of Electronic Engineering, School of Information Science and Technology, Center for Intelligent Medical Electronics, Fudan University, Shanghai, China
Department of Electronic Engineering, School of Information Science and Technology, Center for Intelligent Medical Electronics, Fudan University, Shanghai, China
Department of Electronic Engineering, School of Information Science and Technology, Center for Intelligent Medical Electronics, Fudan University, Shanghai, China
Muhammad Irfan
Department of Electronic Engineering, School of Information Science and Technology, Center for Intelligent Medical Electronics, Fudan University, Shanghai, China
Hafiza Ayesha Siddiqa
Department of Electronic Engineering, School of Information Science and Technology, Center for Intelligent Medical Electronics, Fudan University, Shanghai, China
Abu Bakar Waqas
Department of Electronic Engineering, School of Information Science and Technology, Center for Intelligent Medical Electronics, Fudan University, Shanghai, China
Department of Electronic Engineering, School of Information Science and Technology, Center for Intelligent Medical Electronics, Fudan University, Shanghai, China
Chunmei Lu
Department of Neonatology, Children’s Hospital of Fudan University, Shanghai, China
Department of Electronic Engineering, School of Information Science and Technology, Center for Intelligent Medical Electronics, Fudan University, Shanghai, China
Objective: Classification of sleep-wake states using multichannel electroencephalography (EEG) data that reliably work for neonates. Methods: A deep multilayer perceptron (MLP) neural network is developed to classify sleep-wake states using multichannel bipolar EEG signals, which takes an input vector of size 108 containing the joint features of 9 channels. The network avoids any post-processing step in order to work as a full-fledged real-time application. For training and testing the model, EEG recordings of 3525 30-second segments from 19 neonates (postmenstrual age of 37 ± 05 weeks) are used. Results: For sleep-wake classification, mean Cohen's kappa between the network estimate and the ground truth annotation by human experts is 0.62. The maximum mean accuracy can reach up to 83% which, to date, is the highest accuracy for sleep-wake classification.
