A new automatic sleep stage classification model using swarm intelligence-based hybrid transfer learning architecture - Raja, A. Ravi; Polasi, Phani Kumar (Signal, image and video processing, 2024)
Existing automatic sleep stage classification systems have mostly relied on hand-crafted features selected from polysomnographic records. To measure the quality of sleep, the automatic sleep stage classification system is very important. The sleep specialists examine the signals such as Electromyograms, Electroencephalograms (EEG), Electrocardiograms, and Electrooculograms, based on the visual inspection that is assigned every 30 s of the signal at the sleep stage. Hence, this research plans to implement an effective sleep stage classification model for detecting sleep disorder patients. It is performed with filtering approaches together with artifact removal techniques to get the pre-processed EEG signals. This pre-processed signal is used in the signal decomposition phase, where the short-time Fourier transform is involved in decomposing the pre-processed signals. Furthermore, these decomposed EEG signals are utilized in the optimal hybrid transfer learning approach for sleep stage classification using Mobilenet and Densenet techniques. The optimization takes place in the hybrid transfer learning approach with the development of a hybrid optimization strategy Hybrid Coyote Cat and Mouse Optimization Algorithm, to make efficient and accurate classification results. Experimental analysis reveals that the developed approach attains better effectiveness by analyzing various comparative techniques using different performance measures.