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Sleep is a period of rest that is essential for functional learning ability, mental health, and even the performance of normal activities. Insomnia, sleep apnea, and restless legs are all examples of sleep-related issues that are growing more widespread. When appropriately analyzed, the recording of bio-electric signals, such as the Electroencephalogram, can tell how well we sleep. Improved analyses are possible due to recent improvements in machine learning and feature extraction, and they are commonly referred to as automatic sleep analysis to distinguish them from sleep data analysis by a human sleep expert. This study outlines a Systematic Literature Review and the results it provided to assess the present state-of-the-art in automatic analysis of sleep data. A search string was organized according to the PICO (Population, Intervention, Comparison, and Outcome) strategy in order to determine what machine learning and feature extraction approaches are used to generate an Automatic Sleep Scoring System. The American Academy of Sleep Medicine and Rechtschaffen & Kales are the two main scoring standards used in contemporary research, according to the report. Other types of sensors, such as Electrooculography, are employed in addition to Electroencephalography to automatically score sleep. Furthermore, the existing research on parameter tuning for machine learning models that was examined proved to be incomplete. Based on our findings, different sleep scoring standards, as well as numerous feature extraction and machine learning algorithms with parameter tuning, have a high potential for developing a reliable and robust automatic sleep scoring system for supporting physicians. In the context of the sleep scoring problem, there are evident gaps that need to be investigated in terms of automatic feature engineering techniques and parameter tuning in machine learning algorithms.

Human sleep is one of the essential indicators that gauge the overall health and well-being. Presently, it is common for people to face issues related to sleep. Various biomedical signals including electroencephalogram (EEG), electrooculography (EMG), and electrooculography (EOG) are utilized in the diagnosis and during the treatment of sleep disorder cases. An automatic classification to diagnose sleep problems can help in the analysis of sleep EEG data. In this current study, an effort is made to classify the sleep stages from a single EEG channel (C4-A1) based on K-nearest neighbors (K-NN) with three alternative distance metrics. The Euclidean distance is the most commonly used distance measure in K-NN, and no prior study of sleep EEG data has inspected the classification performance of K-NN with various distance measures. Therefore, this study aimed to investigate whether the distance function affects the performance of K-NN in the classification of sleep data. Euclidean, Manhattan and Chebyshev distance measures were individually tested with K-NN classification, and their performances were compared based on accuracy, sensitivity, specificity, F-measure, Kappa statistic and computation time for both Rechtschaffen & Kales and American Academy of Sleep Medicine standard labelings of the sleep stages. The experimental results show that the Manhattan distance function with K = 5 was the best choice for classification of the sleep stages, achieving 98.46% and 98.77% correct rates for the two labelings with comparatively rapid computations.

This study presents a method for designing-by a genetic algorithm, without manual intervention-the feature learning architecture for classification of sleep stages from a single EEG channel, when using a convolutional neural network called GACNN SleepTuneNet. Two EEG electrode positions were selected, namely FP2-F4 and FPz-Cz, from two available datasets. Twenty-five generations were involved in diagnosis without hand-crafted features, to learn the architecture for classification of sleep stages based on AASM standard. Based on the results, our model not only achieved the highest classification accuracy, but it also distinguished the sleep stages based on either of the two EEG electrode signals, in both datasets. The results show that our model performed the best with highest overall accuracy rates and kappa statistic (CAP sleep: 95.61% and 0.94; Sleep EDF: 92.51% and 0.90) among other state-of-the-art methods that require no manual intervention. Our model could automatically learn the features for classification of sleep stages, for different raw EEG electrode positions in different datasets, without user-assisted feature extraction.