Brain Network Analysis for Anesthesia Depth Monitoring
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Solution Overview
Problem
Current anesthesia and consciousness depth monitoring technologies rely on subjective judgments and are not accurate in distinguishing consciousness and unconsciousness, especially under varying environmental conditions, and are not suitable for immediate medical responses due to time delays and limitations in reliability.
Innovation Solution
A method and apparatus using brain network analysis to acquire and preprocess brain signals, calculate functional connectivity values, and determine anesthesia and consciousness depth through brain network features, providing objective measurements and alerts for transition moments between consciousness and unconsciousness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional anesthesia monitoring technologies (BIS, entropy index) are used, then the market coverage and general reliability are high, but the measurement precision and reliability in sedation anesthesia classification are low
Solution Approach 1:
The patent changes the analysis parameters from conventional single-index approaches (BIS, entropy) to multiple brainwave frequency parameters (delta, theta, alpha, beta, gamma waves) and their spectral features. This parameter transformation enables more precise differentiation of consciousness states, particularly in sedation anesthesia where conventional methods fail to provide reliable classification.
2Measurement precision
If subjective judgment by doctors is used to determine consciousness depth, then flexibility in clinical decision-making is maintained, but measurement precision and objectivity are compromised
Solution Approach 1:
The patent replaces the mechanical/manual system of subjective doctor judgment with an automated computational system that processes brainwave signals through spectral analysis. The system objectively calculates consciousness depth indices based on quantifiable brainwave parameters, eliminating subjective bias while maintaining clinical applicability through automated monitoring.
3Speed
If conventional monitoring technologies are used, then ease of operation is maintained, but the response time for immediate medical intervention is delayed up to 60 seconds
Solution Approach 1:
The patent implements continuous real-time spectral analysis of brainwave signals, continuously updating consciousness depth measurements without the time delays inherent in conventional methods. The system processes brainwave data continuously across multiple frequency bands, providing immediate detection of consciousness transitions and enabling prompt medical intervention when needed.
4Measurement precision
If multiple bio-signals are used for analysis, then measurement precision improves, but device complexity and cost increase making real clinic application difficult
Solution Approach 1:
The patent extracts and focuses specifically on brainwave signals as the primary indicator of consciousness depth, eliminating the need to process multiple bio-signals simultaneously. By concentrating analysis on EEG spectral features across different frequency bands, the system achieves high measurement precision while maintaining operational simplicity and clinical feasibility.
Data Source
AI summary
A method of monitoring an anesthesia and consciousness depth through brain network analysis by a computing device according to the present disclosure includes: acquiring a user's brain signals extracted during anesthesia; performing pre-treatment to the acquired brain signals to be suitable for brain network analysis; calculating a functional connectivity value between two channels for each frequency based on the pre-treated brain signals and performing brain network analysis; determining an anesthesia and consciousness depth of the user based on features of the analyzed brain network; and providing the determined anesthesia and consciousness depth of the user through a user interface.