Abstract
This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively.
Highlights
Sleep stage classification can be helpful in identifying sleep disorders, such as, snoring, insomnia, sleep apnea, sleep deprivation, narcolepsy, sleep hypoventilation, and teeth grinding, etc. [1,2].Sleep disorders are important healthcare concerns as they are significant contributors to fatigue and drowsiness, especially among drivers, and cause around 10–15% of the total vehicle accidents involving fatalities each year [3,4]
During training, two different optimization techniques: i.e., stochastic gradient descent (SGD) and Adamax are considered for loss function optimization
The proposed framework uses the power spectral density (PSD) of EEG signals collected from three important regions of the brain, i.e., the pre-frontal, central, and occipital, as input to a residual neural network (ResNet)
Summary
Sleep stage classification can be helpful in identifying sleep disorders, such as, snoring, insomnia, sleep apnea, sleep deprivation, narcolepsy, sleep hypoventilation, and teeth grinding, etc. [1,2].Sleep disorders are important healthcare concerns as they are significant contributors to fatigue and drowsiness, especially among drivers, and cause around 10–15% of the total vehicle accidents involving fatalities each year [3,4]. In order to improve road safety and reduce the risk to millions of human lives, it is important that we understand the causes of sleep disorders, which involves, above all, the understanding and identification of different sleep stages [3,5]. The NREM sleep is believed to occur in three stages, i.e., N1, N2 and N3, where each stage progressively turns into deeper sleep. Among these NREM stages, most of our sleep time is spent in the N2 stage [6], whereas the REM sleep first starts 90 minutes after we fall asleep, and is mostly associated with dreaming
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