Method for Monitoring the Depth of Anesthesia

• US 20080255469A1
• Filed: 04/12/2007
• Published: 10/16/2008
• Est. Priority Date: 04/12/2007
• Status: Abandoned Application

• Current Assignee: Yuan Ze University
• Original Assignee: Yuan Ze University
• Inventors: Shieh, Jiann-Shing, Fan, Shou-Zen, Chen, Bo-Cun
• Application Number:
  US11/734,478
• Publication Number:
  US 20080255469A1
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/372 Analysis of electroencephalograms
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence

Abstract

A method for monitoring the depth of anesthesia is provided for detecting the conscious state of one being anesthetized in order to facilitate an anesthesiologist to predict exactly the dosage of an anesthetic required. At first, an original electroencephalogram (EEG) is taken from one being tested. Then, the original electroencephalogram is analyzed by approximate entropy to obtain its approximate entropy value. Next, the approximate entropy value is multiplied by 1000/17, and the corrected value is assumed as the predicted value of depth of anesthesia. The predicted value of depth of anesthesia represents degree of the conscious state or the depth of anesthesia for the one being tested. The higher the predicted depth of anesthesia value, the more conscious the one being tested is, i.e., in a shallower depth of anesthesia. On the other hand, the lower the predicted depth of anesthesia value, the less conscious the one being tested is, i.e., in a deeper depth of anesthesia.

Claims

1. A method for predicting the depth of anesthesia, comprising steps of:

step 1;

obtaining original electroencephalogram (EEG) from one being tested;

step 2;

calculating the approximate entropy value from the original electroencephalogram (EEG) using following formula;

Approximat Entropy=Φ

^m(r)−

Φ

^m+1(r);

$\mathrm{wherein}<br><br><br><br>{\mathrm{\Phi <br><br>}}^m<br><br>\left(r\right)={\left(N-m+1\right)}^{-1}·<br><br>\underset{i=1}{\overset{N-m+1}{\sum <br><br>}}<br><br><br><br>\ln <br><br><br><br>C_i^m<br><br>\left(r\right);<br><br>$

C_i^m(r)=(number of x(j) such that d[x(i),x(j)]≦

r)/(N−

m+1);

x(i)=[u(i), . . . , u(i=m−

1)];

x(j)=[u(j), . . . , u(j=m−

1)];

u(i),u(2) . . . u(N) are time sequence data;

wherein N is the length of data cycle;

m is the number of data comparison;

r is a noise filtering coefficient; and

step 3;

multiplying the approximate entropy value obtained in step 2 by 1000/17, and using thus corrected value as the predicting value of depth of anesthesia.

2. A method for predicting the depth of anesthesia as recited in claim 1, wherein the sampling time of the original electroencephalogram (EEG) is 1/256 to 1/128 second/time.

3. A method for predicting the depth of anesthesia as recited in claim 1, wherein the N value is 1024.

4. A method for predicting the depth of anesthesia as recited in claim 1, wherein the m value is 2.

5. A method for predicting the depth of anesthesia as recited in claim 1, wherein the r value is 0.2.

6. A method for predicting the depth of anesthesia as recited in claim 1, wherein the predicting value of depth of anesthesia represents the degree of consciousness state or the depth of anesthesia of one being tested.