EEG-Based Neonatal Sleep-Wake Classification Using Multilayer Perceptron Neural Network
Saadullah Farooq Abbasi, Jawad Ahmad, Ahsen Tahir, Muhammad Awais, Chen Chen, Muhammad Irfan, Hafiza Ayesha Siddiqa, Abu Bakar Waqas, Xi Long, Bin Yin, Saeed Akbarzadeh, Chunmei Lu, Laishuan Wang, Wei Chen
Abstract
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.