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

A Temporal-Spectral Fused and Attention-Based Deep Model for Automatic Sleep Staging

“…Firstly, the data are preprocessed and filtered to obtain clean and nonimpurity signals, then feature extraction is carried out on the preprocessed signals, and valuable features are selected and input into the classifier to execute sleep staging. These handcrafted features include time-domain features [8], frequencydomain features [9], and time-frequency domain features [10]. In addition, various classifiers are used for sleep stages, such as support vector machines (SVM) [11], random forests (RF) [12], and adaptive boosting (AdaBoost) [13].…”