###### Abstract  
Increasingly prevalent sleep disorders worldwide significantly affect the well-being of individuals. Sleep disorder can be detected by dividing sleep into different stages. Hence, the accurate classification of sleep stages is crucial for detecting sleep disorders. The use of machine learning techniques on physiological signals has shown promising results in the automatic classification of sleep stages. The integration of information from multichannel physiological signals has shown to further enhance the accuracy of such classification. Existing literature reviews focus on studies utilising a single channel of EEG signals for sleep stage classification. However, other review studies focus on models developed for sleep stage classification, utilising either a single channel of physiological signals or a combination of various physiological signals. This review focuses on the classification of sleep stages through the integration of combined multichannel physiological signals and machine learning methods. We conducted a comprehensive review spanning from the year 2000 to 2023, aiming to provide a thorough and up-to-date resource for researchers in the field. We analysed approximately 38 papers investigating sleep stage classification employing various machine learning techniques integrated with combined signals. In this study, we describe the models proposed in the existing literature for sleep stage classification, discuss their limitations, and identify potential areas for future research. classification; EEG; EMG; EOG; deep learning; machine learning; sleep stage +
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during the NREM stage [12; 13]. In a typical sleep cycle, 50-60% of sleep is spent in N1 and N2, which are considered light sleep stages; 15-20% in N3, which is considered a deep sleep stage; 20-25% in the REM sleep stage; and 5% or less in the W stage [14]. The absence of certain sleep stages in a typical sleep cycle may suggest the presence of sleep disorders. The activities mentioned above for sleep stages are recorded by a traditional method called polysomnography (PSG) [15], which is considered as the gold standard for classifying sleep stages following the guidelines established by AASM. The physiological signals recorded during PSG include respiratory effort [16], electroencephalogram (EEG) [17], electrocardiogram (ECG) [18], electrooculogram (EOG) [19], and electromyogram (EMG) [20]. Each physiological signal recording is divided by sleep specialists into 30-second segments. These segments are then classified into W, NREM (including N1, N2, and N3), or REM sleep stages [21]. This classification is based on visual analysis, which is not only time-consuming but also error prone. Furthermore, patients need to be connected to sensors and equipment for extended periods during sleep studies, which can have an impact on the quality of recorded data [22]. Artificial intelligence (AI) has recently been employed in a wide range of clinical medical applications, including surgeries [23], classifications of types of seizure [24], and stage classifications [25]. AI research aims to build intelligent tools that can help medical specialists make clinical decisions in the medical field [26]. Machine learning (ML) is a subfield of AI that employs algorithms and approaches to create models that learn from data and make predictions or decisions based on that learning. The key drawback of these conventional ML techniques is the need for feature engineering methods to extract features from input data. The model's performance may be restricted by the time-consuming process of designing and selecting relevant features from the input data. However, recently introduced deep learning (DL) has overcome the limitations of conventional machine learning algorithms by employing multilayered neural networks to extract and learn key features from raw data [27]. The utilisation of machine learning (ML) techniques for sleep stage classification has been extensively examined in literature reviews [25; 28; 29; 30; 31; 32; 33].