Method for automatic real-time estimating anesthesia depth

A deep and automatic technology for anesthesia, applied in computing, medical science, special data processing applications, etc. It can solve the problems of few and rely on anesthetics, and achieve the effect of strong anti-noise, simple concept and fast calculation speed.

Inactive Publication Date: 2009-06-10
李小俚
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Problems solved by technology

However, the study found that BIS has obvious limitations as an anesthesia depth monitoring technology, that is, it is obviously dependent on the use of anesthetics and is sensitive to patient differences.
[0006] ④ Anesthesia trend (Narcotrend, NT) is a quantitative index that uses Kugler multi-parameter statistics and divides the depth of anesthesia into 6 stages and 14 levels (A, B0-2, C0-2, D0-2, E0, 1, F0, 1), studies have found

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  • Method for automatic real-time estimating anesthesia depth
  • Method for automatic real-time estimating anesthesia depth
  • Method for automatic real-time estimating anesthesia depth

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Embodiment Construction

[0035] figure 1 It is a schematic diagram of the workflow of the present invention. First is step 101, collecting EEG signals. In this embodiment, EEG signals were collected from 19 patients undergoing surgery, including gynecology, general surgery, and plastic surgery. In this embodiment, the sampling frequency is 100 Hz. Using Entropy TM The company's combination electrodes record EEG signals on the forehead and temples, where the skin is washed and dried before measurements are taken. Figure 2A is the collected 10-minute raw EEG signal. Figure 2B for Figure 2A Concentration of sevoflurane anesthetic drug inhaled by patients.

[0036] In step 102, the collected EEG signal data is segmented and processed using the moving window technique.

[0037] In order to track the effects of anesthetics on the brain in real time, EEG signals need to be segmented and processed. Segment the above EEG data, and the segment length can be selected as follows:

[0038] The predicti...

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Abstract

The invention discloses a method for automatically real-time estimating anesthesia depth based on ordering entropy, at first acquiring original electrobrain signal in real time; then sectioning the electrobrain data by a movable window technology; then recognizing the brain state and preprocessing the electrobrain signals in different states by different methods; at last calculating ordering entropies of each data section and estimating the anesthesia depth based on the ordering entropy values. The ordering entropy is a novel method for evaluating dynamics system complexity, the conception is simple, the computation speed is fast, the anti-interference performance is strong and the precision is high. The present invention provides an accurate real-time method for monitoring the influence of the anaesthetic dose to the electrobrain activity and provides objective evidences for the anesthesia physician to take appropriate measure.

Description

technical field [0001] The invention relates to a method for automatically estimating the depth of anesthesia in real time based on electroencephalogram signals, in particular to a method for calculating the sorting entropy of electroencephalogram sequences. Background technique [0002] Anesthesia depth monitoring is one of the essential steps in surgical procedures. Anesthesia refers to the disappearance of the whole body or local sensation (especially pain) and the state of memory amnesia produced by means of drugs and other methods. The intake of anesthesia drugs should be controlled to avoid shock and premature awakening. EEG signals (or electroencephalogram: EEG) directly reflect the activity of the cerebral cortex, and the estimation of anesthesia depth based on EEG signals is currently in a dominant position in the research of anesthesia depth monitoring. The methods for estimating the depth of anesthesia based on EEG signals mainly include: time-domain analysis, f...

Claims

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Application Information

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IPC IPC(8): A61B5/048A61B5/0476G06F17/00A61B5/374
Inventor 李小俚崔素媛欧阳高翔
Owner 李小俚
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