Automatic classification of human sleep stages using artificial neural networks

Jaime Barragan, University of Texas at El Paso

Abstract

Sleep disorders are identified in several ways, one of the most common procedures used for diagnosis and treatment evaluation is the polysomnogram. The automation of this process is a crucial point in the addressing sleep related disorders because it can save the specialist's time by avoiding the mechanical phase of sleep stage determination and hypnogram production. A way to reach this automation process is through intelligent systems. This study focuses on one type of intelligent systems, Artificial Neural Networks (ANNs). The ANNs presented in this document show a system, which with a previous training, is able to classify the sleep stages with a high rate of accuracy, in comparison to the human expert. The objective of this research, which has been successfully met, is applying these kinds of tools to provide an efficient and affordable sleep classification. ^

Subject Area

Engineering, Biomedical|Engineering, Electronics and Electrical

Recommended Citation

Barragan, Jaime, "Automatic classification of human sleep stages using artificial neural networks" (2005). ETD Collection for University of Texas, El Paso. AAI1430970.
http://digitalcommons.utep.edu/dissertations/AAI1430970

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