EEG Approximate Entropy for Anesthesia Depth Monitoring
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Solution Overview
Problem
Current methods for monitoring the depth of anesthesia, such as Bispectrum Index (BIS) and Auditory Evoked Potential (AEP), are expensive, not widely available, and lack full theoretical disclosure, leading to potential for inadequate anesthesia depth monitoring, increasing surgical risks.
Innovation Solution
A method using electroencephalography (EEG) to measure approximate entropy, calculated through specific formulas and displayed on a monitor, to predict the depth of anesthesia, allowing for more accurate dosage determination and conscious state assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional anesthesia monitoring methods (BIS, AEP) are used, then measurement precision of anesthesia depth is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical/electronic monitoring systems (BIS, AEP) with a simplified computational approach using approximate entropy calculation on raw EEG signals. This substitution maintains measurement precision while dramatically reducing device complexity by using straightforward mathematical formulas instead of sophisticated signal processing hardware and algorithms
Solution Approach 2:
The patent creates a simplified model of anesthesia depth monitoring by copying the essential feature from complex systems - using EEG signals as the basis - but implements it through a much simpler approximate entropy calculation rather than replicating the full complexity of BIS or AEP systems
2Productivity
If muscle relaxant is used during anesthesia, then surgical muscle relaxation is improved, but reliability of anesthesia depth monitoring deteriorates
Solution Approach 1:
The patent introduces approximate entropy as an intermediary metric that bridges the gap between raw EEG signals and anesthesia depth assessment. This intermediary is reliable even in the presence of muscle relaxants because it focuses on temporal signal characteristics rather than frequency components that are affected by neuromuscular blocking agents
3Reliability
If comprehensive physiological monitoring is performed, then reliability of anesthesia assessment is improved, but ease of operation deteriorates
Solution Approach 1:
The patent extracts the essential information for anesthesia monitoring from complex physiological data by focusing solely on EEG signals and their temporal characteristics. This extraction eliminates the need for anesthesiologists to interpret multiple physiological parameters simultaneously, greatly simplifying operation while maintaining reliability through the robust approximate entropy metric
Data Source
AI summary
A method for monitoring the depth of anesthesia is provided for detecting the conscious state of one being anesthetized in the recovery phase or induction phase of anesthesia course 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.