Automatic sleep stage classification method based on dual character filtering

Abstract

The invention provides an automatic sleep stage classification method based on dual character filtering. The stage classification method comprises steps of firstly extracting a two-guidance sleep electroencephalogram signal and an one-guidance horizontal electrooculogram signal; performing filtering for the original electroencephalogram signals and electrooculogram signal; extracting multiple characters from the filtered electroencephalogram signals and the filtered electrooculogram signal; selecting the optimal character subsets by use of a dual character filtering method combining the Fisher score method and the sequential forward selection method. Via the dual characteristic filtering method, character dimension is greatly reduced and redundancy among the characters is reduced. At last, a support vector machine classifier is used for identifying the optimal characters, so automatic stage classification of sleep is finished. According to the invention, objectivity, precision and convenience of automatic sleep stage classification can be well increased; the automatic sleep stage classification method is characterized by high precision, low calculation complexity, simple operation and easy popularization; and considerable social and economic benefit can be gained.

Description

technical field [0001] The invention relates to the field of automatic sleep staging, in particular to an automatic sleep staging method based on double feature screening. Background technique [0002] Sleep is a physiological process generally required by all higher organisms, including humans. People spend one-third of their time sleeping, and long-term lack of sleep will cause serious consequences. Long-term lack of sleep will not only endanger people's health, but also have many negative effects on life and society. Therefore, sleep research is receiving more and more attention. Sleep staging is the basis of sleep research and plays a key role in sleep research. It can objectively evaluate sleep quality, provide clinical basis for the treatment and diagnosis of sleep-related diseases, and provide clinical basis for the diagnosis and research of neurological diseases such as epilepsy and dementia. [0003] According to the latest standard of sleep classification of the...

AI Technical Summary

Problems solved by technology

The Fisher score method is to calculate the score of each feature separately according to the Fisher criterion, which ignores the impact of the feature combination on the classification; while the sequential forward method is a heuristic search strategy, and the optimal feature subset obtained by it may only be some The feature combination with a very common individual contribution rate ignores the feature with the largest individual contribution rate, and the calculation process takes a long time

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