# Classification of Sleep Stage with Biosignal Images Using Convolutional Neural Networks
## 3 Related Works  
Conventional automatic sleep scoring systems are implemented using features in biosignals empirically defined by experts. The features are fed into the system as an input. Typical features consist of the characteristics of the signal or the change in the signal as brain activity. The sleep-stage classifier tries to find unique features from the input. Generally, the accuracy of classifiers relies heavily on the quality of the dominant features. Most features used for sleep-stage classification can be categorized as one of the following [10]:  
* Temporal features: Temporal features explain the changes in brain signals over time. Each sleep stage is related to the brain signal and state. For example, an indicator of SWS is a change in peak amplitude or a change in frequencies. * Spectral features: Frequency features are extracted from the signal using Fourier or wavelet transformation. Sleep stages are characterized by unique frequencies. Most often the brain wave is divided into delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), sleep spindles (12-16 Hz), beta (12-40 Hz), and gamma (40-100 Hz) according to the frequency [5]. * Statistical features: Statistical features can describe the biosignals. The minimum, maximum, and average of the signal or the number of zero-crossing can be general features in the signals. Other statistical features are median, standard deviation, and skewness of the signals. Although many researchers have utilized various combinations of features, the results are similar, and it seems that an optimal combination of features does not exist. The most recent trend to classify sleep stages is machine learning. Machine learning algorithms can retrieve features of a given dataset automatically. There are various, but the convolutional neural network (CNN) [11], which is one of the machine learning algorithms, is used in this paper. The CNN consists of a convolutional kernel in which each entry in the kernel is applied to an input signal composed of neural weights. As backpropagation, these weights in the network learn and adapt to features presented in the input. Each kernel scans the input and activates the region exhibiting the features. The layers can be stacked, with each layer building a more abstract representation from features found in previous layers. This process allows abstract feature representations to be extracted from raw data, such as images. Since CNN shows excellent performance in image feature extraction, biosignals are converted into images and used as input to the CNN in this research. The previous methods that have classified sleep stages using deep learning algorithms are proposed: Deep Neural Networks (DNN) [12; 13], Convolutional Neural Networks (CNN) [14; 15; 16; 17; 18; 19; 20; 21; 22; 23], and Recurrent Neural Networks (RNN) [24; 25]. In most of the studies, CNN and RNN have been used to handle raw PSG recordings. Other methods are based on the combination of CNN and RNN [26; 27]. [PERSON] et al. [14] presented a CNN with numeric EEG data (1-dimension) as input. Their method successfully extracted features in the biosignal and classified sleep stages. [PERSON] and [PERSON] [26] also proposed a complex-valued CNN. Complex values allow researchers to add more dimensions to the decision boundary and can speed up the learning process. [PERSON] et al. [27] used a combination of two CNNs to extract features. In the research, a CNN with larger filters could detect features with low frequency and the other with smaller filters could detect features with high frequency. In this architecture, two bidirectional LSTM layers were stacked. [PERSON] et al. [17] proposed a deep learning method to classify sleep stages using several biosignals, such as EEG, EOG, and EMG. This algorithm consisted of three steps. First, linear spatial filtering was applied to enhance the information contained in the input signals. Second, features were extracted using the rectified linear operator and max-pooling was applied. Finally, the features were fed to the SoftMax layer.