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.
Highlights
Sleep is an important human function, which is identified by the sequence of brain alterations
The testing results give the highest accuracy of 82.53 ± 1.63% for sleep-wake classification using 5-layer multilayer perceptron (MLP) neural network
In this study, we proposed a low cost, efficient, and simple deep MLP neural network for sleep wake classification using multichannel EEG signals. 8-time domain and 4-spatial domain features were extracted from neonatal EEG recordings and combined to form an input to the neural network of size 108
Summary
Sleep is an important human function, which is identified by the sequence of brain alterations. They spend most of their time resting in a sleep state. Sleep ontogenesis is an active process for brain maturation and the central nervous system. Sleep-wake cycling (SWC) is the main hallmark of brain development in neonates [1], [2]. In a neonatal intensive care unit (NICU), neonatal sleep should be protected and promoted. Polysomnography (PSG) is considered as the gold standard to monitor sleep and diagnose sleep disorders [3]. Many studies have demonstrated the feasibility of automated sleep staging algorithms with PSG signals, among
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