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Brain network connectivity detection method and device

A technology of connectivity detection and detection method, applied in the field of brain network, can solve the problems of difficult separation and long time consumption.

Pending Publication Date: 2022-04-29
SUN YAT SEN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The existing technology mainly collects EEG data through polysomnography. This method takes a long time and needs to collect a large amount of EEG data, and it is difficult to separate the data used to characterize the connection between the left and right hemispheres.

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Embodiment Construction

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0062] It should be noted that the current detection of depression or the diagnosis of some mental diseases is mainly based on the subjective judgment of clinicians and the scores of the score sheet. Polysomnography (PSG) is one of the commonly used methods. Signals such as body position and body position were detected overnight. But this approach has the following disadvantages:

[0063] (1) This method relies on traditional EEG equipment such as polysomnography...

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Abstract

The invention provides a brain network connectivity detection method and device. The method comprises the following steps: collecting brain temporal lobe region electroencephalogram channel data of a user to be detected; wherein the brain temporal lobe region electroencephalogram channel data is resting state data and comprises four-channel data; performing cross-brain region phase synchronization information transfer calculation on the four-channel data through a weighted phase lag index algorithm and a symbol transfer entropy algorithm to obtain a first data set; and obtaining a preset second data set, establishing a connectivity detection model, training the model until convergence, and inputting the first data set into the converged model to obtain a detection result. Compared with the prior art, the resting state data of the brain is adopted, the data acquisition time is shortened, and the method is suitable for multiple application scenes; the function connectivity of the brain can be obtained only through four-channel data, and the data volume needing to be processed is reduced; functional connection states and connection information are quantified, anti-interference performance and robustness are enhanced, and detection accuracy is effectively improved.

Description

technical field [0001] The invention relates to the field of brain networks, in particular to a method and device for detecting connectivity of brain networks. Background technique [0002] Effective connectivity is used to measure the direct or indirect influence of one nervous system on another, including the magnitude and direction of the interaction. Functional connectivity, on the other hand, reflects the temporal correlation of direct or indirect interactions between brain regions. For some patients with psychiatric diseases, their effective connection and functional connection may be abnormal. Compared with normal people, patients show higher global efficiency and lower local efficiency, and the structure of brain network is also more random, and there may be brain organic damage. By detecting the effective connection and functional connection of the brain, it is possible to effectively obtain the executive process of complex functions of the brain and the abnormal ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/372A61B5/00G06K9/00G06K9/62
CPCA61B5/372A61B5/7246A61B5/725G06F2218/12G06F18/2411
Inventor 罗语溪张仰婷韦雨朱永鹏
Owner SUN YAT SEN UNIV
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