Marking equipment for data type of electroencephalogram signals in sleeping state

Abstract

The invention relates to marking equipment for the data type of electroencephalogram signals in a sleeping state, comprising an electroencephalogram electrode, a reference electrode, an analog-digital converter, a filter circuit and a processor, wherein the electroencephalogram electrode and the reference electrode are respectively connected with the analog-digital converter and sequentially connected to the processor through the analog-digital converter and the filter circuit; the electroencephalogram electrode is used for detecting electroencephalogram signals of a user in sleeping; the analog-digital converter converts the electroencephalogram signals into digital signals; the filter circuit carries out low-frequency filtration on the electroencephalogram signals and then inputs into the processor; the processor is used for carrying out wavelet decomposition on the electroencephalogram signal sample, reconstructing low-frequency electroencephalogram signals, and extracting brain waves from the low-frequency electroencephalogram signals; detecting K complex waves and delta waves from the brain waves and counting the number; and when the number exceeds a preset number threshold, marking the data type of the electroencephalogram signal sample as sleeping. The marking equipment for the data type of electroencephalogram signals in a sleeping state can eliminate interference components mixed in the electroencephalogram signal sample and have higher marking accuracy.

Description

Technical field [0001] The present invention relates to the technical field of sleep assistance, in particular to a marking device for brain electrical signal data types in a sleep state. Background technique [0002] There are already some devices on the market to help people fall asleep and improve sleep quality. Sleep state analysis is an important means to understand the quality of users' sleep. Polysomnography (PSG), also known as sleep electroencephalogram, is currently the "gold standard" for sleep diagnosis and analysis in clinical practice. Polysomnography uses a variety of vital signs to analyze sleep. Among these signs, EEG is at the core; four rhythms of brain waves are used: delta wave (1-3Hz), θ wave (4-7Hz), α The frequency characteristics of wave (8-12Hz) and β wave (14-30Hz). Because the strength of the EEG signal is very weak (the EEG signal is at the microvolt level, and the ECG signal is at the millivolt level), it is very easy to be interfered by external s...

AI Technical Summary

Problems solved by technology

[0003] At present, the EEG signal data for detecting sleep state is generally recognized by training the recognition model, that is, the EEG signal collected by other people is used to pre-train a classifier (also called a general classifier). Among them, 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 can be performed on the marked type of data, and a personal classification that is more suitable for individuals can be trained However, when using a general-purpose classifier to label data, as mentioned above, because the strength of the EEG signal is very weak, using this method to label the type of the EEG signal is easy to mix in interference components, resulting in the training of individuals. The recognition accuracy of the classifier is low, which affects the reliability of the individual sleep state detection results in the later stage

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