An automatic sleep staging and migration method based on deep neural network

Abstract

The invention discloses an automatic sleep staging and migration method based on a deep neural network. S1. Collect sleep EEG and oculoelectric signals of subjects as the target data set for migration learning; S2. Select the data set as the source of migration learning Data set; S3, preprocessing the data in the source data set and the target data set; S4, constructing an automatic sleep staging model based on a deep neural network; the present invention proposes an improved automatic sleep based on a deep neural network The staging model and its migration method, after obtaining the source data and target data, first use the preprocessing method to perform noise reduction and other processing on the data, and then use the data in the source data set to pre-train the proposed deep neural network model, and finally put The pre-trained model is migrated on the target data set, and the obtained migrated model can perform automatic sleep staging on the new data on the target domain, and the staging results can achieve high accuracy.

Description

technical field [0001] The invention belongs to the technical field of signal processing and pattern recognition, and in particular relates to an automatic sleep staging and migration method based on a deep neural network. Background technique [0002] Facing the ever-increasing pressure of life and work, more and more people are forced to join a fast-paced lifestyle. Accompanying it is the troubles caused by sleep disorders, and the quality of individual sleep declines sharply. In such an era, it is of great significance to conduct research on sleep activities and improve sleep quality. Sleep staging is a key content in the field of sleep activity research. Through sleep staging, the progression of an individual's sleep activity becomes apparent at a glance. The calculation of sleep quality assessment indicators such as deep sleep duration, total sleep time, and sleep efficiency is also simplified. Sleep staging is a useful adjunct to sleep quality assessment. [0003]...

AI Technical Summary

Problems solved by technology

Even so, these deep learning methods applied to sleep staging still have the following defects: First, the convolutional neural network (CNN) used in these studies does not fit the characteristics of sleep staging well in structure, thus reducing the features they extract. Secondly, these studies have not fully explored the transfer performance of sleep staging models based on deep learning on data sets with different characteristics, but in practical applications, it is usually necessary to apply the model to data with different characteristics and the amount of data in the data set is often small, so it is necessary to improve the migration performance of the model to the small data set

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