# Classification of Sleep Stage with Biosignal Images Using Convolutional Neural Networks
## 1 Introduction  
Sleep is a period of rest for the mind and body. During sleep, volition and consciousness are in partial or complete abeyance and bodily functions are suspended. Sleep is a behavioral state marked by characteristic immobile posture and diminished response to external stimuli [1]. While the mechanism of sleep has not yet been fully elucidated, sleep helps not only to refresh the neurobehavioral domain but also to form immunological long-term memories [2]. Sleep periods of all mammals vary from 2 to 20 h, and humans need 7.5 sleep hours on average per night [3]. Sleep quality is closely associated with many health-related issues, such as obesity, hypertension, coronary heart disease, diabetes, stroke, cancer, and mortality [4].  
Polysomnography (PSG) is prescribed to measure sleep quality in clinics. The sleep stage, which is the depth of sleep, is measured on the PSG. These sleep stages are defined by associated patterns in the electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG). The common process for sleep staging is that a somnologist labels the sleep stage for each 30 s epoch of the biosignal during sleep, followed by the classification guideline. The sleep-stage scoring process is tedious and time-consuming as trained somnologists perform it manually [5].  
The sleep-stage scoring process has the following problems: (1) The process is time-consuming and expensive because it takes a trained somnologist around 30-90 min for an 8-h sleep recording; and (2) there are a few consensuses of scoring between somologists for the same datasets. Even the intra-rater reliability is only around 0.76 (Cohen's kappa) [6]. This provides a significant reason for errors in sleep-stage classification. The reliability of manual sleep-stage classification is acceptable, but its accuracy is poor, and its reliability needs to be improved. Despite advances in machine learning in recent decades, approaches to automatizing sleep-stage classification have a gap between clinical and commercial procedures. The main reasons for the unreliability are the high costs of the classifier [7].  
In this paper, we will propose a sleep-stage classification method using an artificial neural network. The method uses a deep learning algorithm to analyze sleep quality from biosignal data without the intervention of experts. To extract features automatically, the biosignal images of EEG and EOG are used as input to the convolutional neural network (CNN). Additionally, whether the use of EEG and EOG is sufficient for the accuracy of sleep-stage classification shall be examined. The proposed method is verified using a Sleep-EDEx dataset, which is an open sleep dataset [8].  
The main contributions of this paper are as follows:  
1. We propose a method for sleep staging classification, with only EEG and EOG signals, with good accuracy.
2. A CNN framework is proposed for the classification. The EEG and EOG signals are converted into images of the time domain and frequency domain, and the images are fed to the CNN as an input.
3. We demonstrate that the proposed method shows experimentally good performance on publicly available datasets: