Automated Sleep Staging Analysis using Sleep EEG signal: A Machine Learning based Model

Sleep is essential for people health and well-being. However, numerous individuals face sleep problems. These problems can lead to several neurological and physical disorder diseases, and therefore, decrease their overall life quality. Artificial intelligence methods for automated sleep stage classification (ASSC) are a fundamental approach to evaluate and treat this public health challenge. The main contribution of this paper is to present the design and development of an ASSC. This study supports the recognition of sleep stages and provides relevant information on the sleep process according to American Academy of Sleep Medicine manuals. The proposed method includes a two-step execution process. On the one hand, the sleep records are extracted through electroencephalogram signals. Three different health condition subjects of distinct gender and different age groups have been analyzed. On the other hand, different session recordings of sleep processes from two additional nights are considered. The proposed work uses a single channel for two-state sleep stage classification. This study uses a public dataset and incorporates data pre-processing, data extraction, and feature selection. The entire experiment was executed on different medical conditioned subjects using a support vector machine (SVM). The reported results signify that the proposed model achieved the best classification accuracy of 97.73% with subgroup-I subject using SVM classification models, respectively.