## 1 Introduction  
Sleep is an essential aspect of everyone's lives. Good sleep is vital for maintaining one's mental and physical well-being. The rapid development of modern society, high-intensity jobs and education, and irregular lifestyles all have an impact on most people's sleep. Sleep disturbances have recently emerged as a major public health issue [1]. According to the American Academy of Sleep Medicine (AASM) guidelines, sleep is split into five stages: waking (W), non-rapid eye movement (NREM), which includes three stages (N1, N2, and N3), and rapid eye movement (REM), as illustrated in Figure 1[2]. Humans often go from the W stage to the NREM stage and then the REM state. Sensors connected to different parts of the body capture the electrical activity in the brain linked to each sleep statistic. The brain is active in three different ways: delta, theta, and alpha. The occipital region exhibits alpha activity in the W stage. Shallow slumber, low alpha activity, and theta activity are the hallmarks of the N1 stage [3]. True sleep starts in the N2 stage, during which the sleep spindle forms a unique waveform. The N3 stage, which is a deep sleep phase, occurs when delta waves occur [4]. Lastly, the REM stage is distinguished by theta wave activity that is fast and low-voltage. Table 1[5] presents the typical frequency of EEG waves for each stage of sleep. A normal sleep phase cycle includes the following percentages: 50% to 60% of the total amount of time spent sleeping in light sleep (N1, N2), 15% to 20% in deep sleep (N3), 20% to 25% in REM, and 5% or less in W sleep phases [6]. The electroencephalogram (EEG) signal is widely used in sleep staging because it is a bioelectrical signal that may precisely represent brain activity. The classic manual interpretation based on polysomnography can successfully finish the sleep staging, despite its high subjectivity and partiality to personal experience. Furthermore, the manual calibration method is labor- and time-intensive and takes a long time [7]. Tracking sleep quality and identifying sleep disorders depends on the classification of several sleep phases. Medical experts with clinical competence and training manually classify sleep phases for each sleep data segment. It takes time to manually classify the stages of sleep. When compared to manual classification, the algorithm- or model-based automatic classification approach is a more useful tool in clinical applications [8]. Deep learning- and machine learning-based techniques can be employed to classify sleep stages [9]. On the other hand, in sleep stage classification, deep learning techniques have progressively supplanted conventional machine learning techniques in recent years [10]. Because hand-crafted feature selection is necessary for standard machine learning techniques, it takes time and is unreliable when evaluated on unknown data [11]. Deep learning techniques, on the other hand, can overcome these issues by employing neural networks for sufficient and adaptable feature extraction. Convolutional neural network (CNN) architecture is used by most deep learning-based sleep stage classification methods now in use [12]. Consequently, the goal of this research is to use a one-dimensional-convolution neural network (1D-CNN) to develop a strong deep-learning classifier for sleep stage classification with feature selection techniques. Thus, these experimental results entail the outcomes of earlier experiments using deep learning techniques. The findings from previous deep learning approaches are compared with the output from the suggested Sleep-1D-CNN model. This model has gained sufficient experience in classifying sleep stages through training on a wide range of sleep data. Therefore, this study aims to offer a range of advantages for future sleep medicine research connected to artificial intelligence. EEG signals include a large amount of information and not all of aspects may be equally important for classification tasks. Feature selection approaches like ANOVA and