Apparatus for marking data type of electroencephalogram at waking state

Abstract

The invention relates to an apparatus for marking a data type of electroencephalogram at a waking state. The apparatus comprises an electroencephalogram electrode, a reference electrode, an analog-digital converter and a processor, wherein the electroencephalogram electrode and the reference electrode are respectively connected with the analog-digital converter, and connected with the processor by virtue of the analog-digital converter and the filter circuit; the electroencephalogram electrode is used for detecting electroencephalogram of a user in sleep; the analog-digital converter is used for performing analog-digital conversion, and the filter circuit is used for performing the low-frequency filter for the electroencephalogram; the processor extracts an electroencephalogram sample of the user, performing wavelet decomposition for the electroencephalogram sample and performing signal rebuilding according to a wavelet coefficient of a set low frequency band to obtain electroencephalogram; a sample entropy of the electroencephalogram is calculated, and the sample entropy is compared with a threshold value of the sample entropy; when the sample entropy is greater than the threshold value of the sample entropy, the signal type of the electroencephalogram sample is marked as a waking state. By adopting the apparatus, the waking state of the electroencephalogram can be accurately detected, the data type can be effectively marked, and a personal classifier trained by using the marked electroencephalogram sample is more accurate.

Description

technical field [0001] The invention relates to the technical field of sleep aids, in particular to a device for labeling data types of brain electrical signals in an awake state. Background technique [0002] At present, there are some auxiliary devices on the market to assist people to fall asleep, that is, sleep aids, so as to improve the sleep quality of users. Sleep state analysis is an important means for auxiliary devices to understand the user's sleep quality. During this process, the user's sleep state needs to be detected to accurately know whether the user is awake or asleep, and then corresponding intervention measures can be carried out. [0003] Polysomnography (PSG), also known as sleep EEG, is currently the "gold standard" for clinical sleep diagnosis and analysis. Polysomnography uses a variety of vital signs to analyze sleep. Among these signs, EEG is at the core; four rhythms of EEG are used: delta waves (1-3Hz), theta waves (4-7Hz), alpha Wave (8-12Hz),...

AI Technical Summary

Problems solved by technology

[0004] In general, to detect whether the user is awake or not is to use brain waves in four frequency bands (delta wave frequency band, theta wave frequency band, alpha wave frequency band and beta wave frequency band) to train the recognition model (classifier) ​​of the awake state. To identify EEG signals, these recognition models are often general recognition models that are trained using other people’s EEG signals, but due to the strong individual specificity of EEG signals, and the strength of EEG is very weak (EEG is microvolt level, the ECG is at the millivolt level), and it is very easy to be interfered by external signals during signal acquisition

[0005] In this process, when it is necessary to train a personal classifier, it is necessary to mark the data type of the collected personal EEG signal samples, so that self-learning and testing of the marked type of data can be carried out, and a more suitable personal classifier can be trained. When using a general recognition model to detect and mark EEG signals, as mentioned above, because the strength of EEG signals is very weak and easy to be interfered, it is easy to use a general recognition model to mark the types of EEG signal samples. Interference components are mixed in, resulting in low recognition accuracy of the trained personal classifier, which affects the reliability of the later detection results of personal sleep status

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