# Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals
## 1 Introduction
NREM sleep eventually deepens and leads to REM sleep. The REM stage occurs most often while dreaming. At the time, the body goes into a temporary paralysis to prevent it from acting out these dreams. However, during REM sleep the eyes move quickly back and forth. The absence of one of these stages or the overabundance of another can lead to the diagnosis of numerous conditions ranging from sleep apnea, hypersomnia, insomnia, or sleep talking [8]. There are two recognized standards for interpreting sleep stages based on sleep recordings: the Rechtschaffen and Kales (R & K) criteria and the American Academy of Sleep Medicine (AASM) criteria. The R & K recommendations classify sleep into seven discrete stages: wake/wakefulness, S1/drowsiness, S2/light sleep, S3/deep sleep, S4/deep or wave sleep, REM and MT/movement time [9]. The AASM criteria are a modified version of the R & K criteria. Some differences between the AASM and R & K criteria are as follows [9; 10]:  
1. NREM stages in the R & K criteria (S1, S2, S3, and S4) are referred to as stages N1, N2, and N3 in the AASM criteria. 2. In the AASM criteria, deep sleep (N3) is a combination of the S3 and S4 stages of the R & K criteria. 3. Movement time (MT) is eliminated as a sleep stage in the AASM criteria. The stages of sleep can be thought of as a cyclic alternation of non-rapid eye movement (NREM) and rapid eye movement (REM) stages [11]. It has been recognized that NREM sleep consists of four distinct stages: S1, S2, S3, and S4, each with specific characteristics. In S1, the patient is drowsy but still awake. The appearance of sleep spindles, vertex sharp waves, and K complexes mark S2 sleep. Shallow sleep consists of both S1 and S2, while deep sleep consists of S3 and S4 [12]. Conventionally, technicians have interpreted and marked the sleep stages manually. As such, it is a time-intensive process as well as being expensive and dependent on human resources. Because it is time consuming, expensive, and is an enormous process, it is not suitable to hold the large EEG datasets for sleep stages annotation by the human expert [13]. As a result, it has become necessary to develop a sleep stage classification in order to achieve better accuracy. Previous attempts at automated classification of sleep stages have been based on single-channel as well as multi-channel EEG recordings and various other physiological markers. [PERSON] et al. described a single-channel EEG based scheme utilizing an artificial neural network coupled with power spectrum density analysis of EEG recordings [14]. [PERSON] et al. analyzed nine features from single-channel EEG recordings and applied an artificial intelligence technique referred to as a support vector machine (SVM) to perform classification [15]. High classification performance has been reported by [PERSON] by applying short-time Fourier transform to a two-channels recording of forehead EEG signals and a relevance vector machine [16]. In addition, [PERSON] et al. have conducted a comprehensive review of automatic sleep stage classification (AASC) systems, which includes a survey of processing techniques including pre-processing, feature extraction, feature selection, dimensionality reduction, and classification. This study evaluated AASC methods against the sleep-EDF database based on single-channel EEG recordings, and is remarkable for having selected 10 s epochs for its analysis. Their model's performance achieved the highest accuracy in comparison to previous results [17]. [PERSON] et al. had applied low dimensional FFT features on the sleep-EDF database with the usage of eight statistical features from the Pz-Oz EEG channel. The classification performance had reached with the accuracy 90.9%, 91.8%, 92.4%, 94.3%, and 97.1% for all 6- to 2-state sleep stages [18]. In this paper, we present a system of sleep stage classification based on EEG signals.