Method for automatic real-time estimating anesthesia depth

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...

AI Technical Summary

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 that anesthesia trend analysis and bispectral analysis have similar effects in anesthesia depth monitoring, but there are few reports about this new method

Although good results have been achieved, the bispectral index still has a large drug difference, and novel and potential anesthesia monitoring methods such as neural network and entropy index analysis still need a lot of research to prove their effectiveness and feasibility. sex

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