Introduction to deep sleep stage8/17/2023 Westchester: American Academy of Sleep Medicine 2007. The AASM Manual for the Scoring of Sleep and Associated Events. Iber C, Ancoli-Israel S, Chesson AL, Quan SF. Sleep and circadian rhythm disruption in psychiatric and neurodegenerative disease. In: Proceedings of the IEEE Long Island Systems, Applications and Technology (LISAT) Conference. Farmingdale, NY, USA 2–. ![]() Efficient sleep stage classification based on EEG signals. The proposed multi-level fusion method showed the most improved performance in classifying N1 stage, where existing methods had the lowest performance. We used public datasets, Sleep-EDF, to measure performance we confirmed that the proposed multi-level fusion method yielded a higher accuracy of 87.2%, respectively, compared to single-level fusion method and more existing methods. Then, after obtaining each classified result using the fused feature data, the sleep stage was derived using a decision-level fusion method that fused classified results. First, we used feature-level fusion to fuse the extracted features using a convolutional neural network for multi-signal data. We propose a multi-level fusion method to increase the accuracy of the sleep stage classification when using multi-signal data consisting of electroencephalography and electromyography signals. Fusion methods include data-level fusion, feature-level fusion, and decision-level fusion methods. The fusion method is used to process multi-signal data. ![]() There have been many studies to classify sleep stages automatically using multiple signals to improve the accuracy of the sleep stage classification. Sleep efficiency can be calculated by analyzing the results of the sleep stage classification. Sleep efficiency is a factor that can determine a person’s healthy life.
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