EEG Anesthetic Depth Index Using Modified Shannon Entropy
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Solution Overview
Problem
Conventional anesthetic depth monitoring apparatuses, such as the BIS analyzing apparatus, struggle to provide accurate and timely measurements of anesthetic depth, especially during rapid changes in anesthetic states, and lack flexibility in adapting to individual patient characteristics due to fixed algorithms.
Innovation Solution
A method and apparatus that divide EEG signals into epoch signals, extract CAI, Shannon entropy, and spectra entropy values, and combine these using modified Shannon entropy calculations to generate an anesthetic depth index (MsCAI), with adjustable constants and noise removal techniques for improved accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional BIS analyzing apparatus uses fixed brainwave analyzing algorithms, then the apparatus structure is simple and easy to operate, but the measurement precision of anesthetic depth is insufficient and cannot adapt to individual patient characteristics
Solution Approach 1:
The patent implements dynamic adaptability by allowing the system to automatically adjust algorithm parameters and select different analysis methods based on real-time EEG signal characteristics and patient-specific features, transforming the fixed algorithm into a dynamic, adaptive processing system that improves measurement precision without requiring manual reconfiguration
Solution Approach 2:
The system changes multiple processing parameters including epoch duration, frequency band divisions, entropy calculation windows, and weighting factors based on signal quality and anesthetic state, enabling the apparatus to optimize measurement accuracy for different clinical conditions and patient characteristics while maintaining operational simplicity
2Speed
If conventional anesthetic depth monitoring apparatus uses standard tracking methods, then the device structure is simple, but the speed for tracking rapid changes in anesthetic state is slow
Solution Approach 1:
The patent segments the continuous EEG signal into multiple frequency bands (delta, theta, alpha, beta, gamma) and processes each band separately with optimized algorithms, then combines the results to achieve rapid tracking of anesthetic state changes. This segmentation allows parallel processing and faster detection of state transitions
Solution Approach 2:
The system employs periodic epoch-based analysis with overlapping windows and implements multi-rate sampling where critical transitions are detected at higher rates while stable states use lower rates, enabling rapid response to anesthetic changes while maintaining overall processing efficiency
3Adaptability or versatility
If conventional BIS apparatus uses fixed algorithms that are not disclosed, then the apparatus is easy to operate, but it is not suitable for clinical anesthetic depth study and algorithm error cannot be proven
Solution Approach 1:
The patent creates a multi-functional system that operates in two modes: a simplified automatic mode for routine clinical monitoring that maintains ease of operation, and a research mode that provides access to detailed algorithm parameters, intermediate calculation results, and customizable processing options for clinical studies and algorithm validation
Solution Approach 2:
The system implements feedback mechanisms where the output of each processing stage is monitored and used to adjust subsequent processing parameters, and provides feedback to the user about signal quality, processing status, and confidence levels, enabling both easy operation and research versatility through intelligent automation with transparent reporting
Data Source
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AI summary
The present invention rapidly reacts to changes in the degree of anesthesia to provide an accurate and timely anesthetic depth measurement result, and a method for measuring anesthetic depth comprises the steps of: an epoch dividing portion generating an epoch signal by dividing a EEG signal into a plurality of numbers based on time units, a coefficient portion extracting a CAI calculation value (CAI) by calculating the number of points in an epoch having a value higher than an established critical value, a Shannon entropy calculating portion extracting a Shannon entropy calculation value (ShEn) by conducting a Shannon entropy-calculation from the EEG signal, and a spectra entropy calculating portion extracting a spectra entropy value (SpEn) by conducting a spectra entropy calculation; an improved Shannon entropy extracting portion extracting an improved Shannon entropy calculation value (MshEn) by multiplying the Shannon entropy calculation value (ShEn) and the spectra entropy calculation value (SpEn); and a CAI extracting portion extracting an anesthetic depth index (MsCAI) through logical operating of the improved Shannon entropy calculation value(MshEn) and the CAI calculation value (CAI).