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Classification system of EEG signals in different anesthesia conditions

An EEG signal and anesthesia state technology, which is applied in medical science, sensors, diagnostic recording/measurement, etc., can solve problems such as large sample requirements and complex algorithms, achieve good resolution, less output delay, and improve classification accuracy Effect

Inactive Publication Date: 2018-08-28
UNIV OF SCI & TECH OF CHINA
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Problems solved by technology

However, the algorithm is as complicated as BIS, and the sample requirements are large.

Method used

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  • Classification system of EEG signals in different anesthesia conditions
  • Classification system of EEG signals in different anesthesia conditions
  • Classification system of EEG signals in different anesthesia conditions

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Embodiment Construction

[0033] Such as figure 1As shown, a system for classifying EEG signals under different anesthesia states of the present invention includes: an EEG signal acquisition module, including three electrodes, mainly collecting a pair of differential signals and a reference signal; a front-end signal processing module, including The front three-stage amplification module, the power frequency filter module and the analog-to-digital conversion module connected in sequence; the front three-stage amplification module is used to amplify the weak EEG signal to a detectable range; the power frequency filter module is Remove the 50Hz power frequency interference in the EEG signal; the analog-to-digital conversion module converts the obtained analog EEG signal into a digital EEG signal, which provides the basis for subsequent digital processing; the frequency domain and time domain parameter calculation module includes frequency Domain parameter calculation module and time-domain parameter calc...

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Abstract

The invention discloses a classification system for EEG signals in different anesthesia states. The classification system comprises an EEG signal acquisition module, a front-end signal processing module, a frequency domain and time domain parameter calculation module, an anesthesia depth estimation module and a display module. The characteristics of different anesthesia depths are obtained, and the obtained characteristic values are divided into clear anesthesia, superficial anesthesia, normal anesthesia and deep anesthesia. The invention introduces a brain function index which combines the permutation entropy and the burst suppression ratio to analyze the complex nonlinear random signal of the EEG signal in the frequency domain and the time domain, thereby improving the classification accuracy of the deep anesthesia. The classification method can be used to determine the anesthesia depth of a patient during anesthesia and operation, and provides a reliable basis for the medical staffto perform anesthesia operation on the patient.

Description

technical field [0001] The invention belongs to the technical field of electroencephalogram (EEG) acquisition and processing, and specifically relates to an anesthesia depth state classification system based on brain function index calculation and self-adaptive fuzzy logic reasoning algorithm applied to quickly judge the anesthesia depth state of a patient. Background technique [0002] Brain electrophysiological signal is the signal generated by the electrical activity of brain neurons, and it is an important means for brain function research and brain disease diagnosis. In the research and analysis of brain electrophysiological signals, researchers from all over the world have done a lot of work, proposing and applying a series of very valuable methods and technologies. There have been many advances in the research on feature extraction and recognition technology of EEG signals. In the existing research, the EEG signal analysis methods mainly include time-domain analysis,...

Claims

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Application Information

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IPC IPC(8): A61B5/0476A61B5/00
CPCA61B5/4821A61B5/7203A61B5/7225A61B5/7235A61B5/7257A61B5/7264A61B5/316A61B5/369
Inventor 郑烇王琪徐骏
Owner UNIV OF SCI & TECH OF CHINA
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