Closed-Loop Anesthesia Control via EEG Indices
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Solution Overview
Problem
Current anesthesia methods lack reliable, continuous monitoring of analgesic and hypnotic effects during surgical procedures, leading to potential intraoperative awareness and postoperative complications due to inadequate control of anesthetic drug administration.
Innovation Solution
A system comprising a bio-signal monitor and control device that computes indices of consciousness (qCON) and nociception (qNOX) from EEG signals, allowing for real-time adjustment of anesthetic agent infusions to maintain desired effect levels, using PKPD models and Hill equations to optimize drug delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring of anesthetic effects is implemented, then reliability of anesthesia control is improved, but device complexity increases
Solution Approach 1:
The system continuously monitors EEG signals and computes indices (qCON for consciousness, qNOX for nociception) that reflect the patient's anesthetic state. These indices are fed back to the control device, which automatically adjusts the infusion pump settings to maintain target effect levels, creating a closed-loop feedback system that improves reliability without requiring complex manual intervention
Solution Approach 2:
The system replaces manual clinical assessment and mechanical adjustment of anesthesia with automated computational algorithms that process EEG signals and generate control recommendations. The control device uses mathematical models (PKPD models, Hill equations) to calculate optimal infusion rates, substituting complex mechanical adjustment processes with automated computational control
2Reliability
If real-time adjustment of anesthetic agents is performed, then safety against awareness and complications is improved, but measurement precision requirements increase
Solution Approach 1:
The system divides the complex EEG signal into distinct frequency bands (delta, theta, alpha, beta, gamma) and processes each band separately through dedicated algorithms. This segmentation allows the system to extract specific physiological information from the EEG signal without requiring the entire signal to be processed with extreme precision, thereby reducing overall measurement precision requirements while maintaining safety
Solution Approach 2:
The system introduces intermediate computational indices (qCON and qNOX) as mediators between the raw EEG signal and the final control decisions. These indices simplify the complex relationship between EEG features and anesthetic effects, allowing real-time adjustment without requiring direct precise measurement of all underlying physiological parameters
3Adaptability or versatility
If multiple anesthetic agents are administered simultaneously, then adaptability to different surgical needs is improved, but device complexity increases
Solution Approach 1:
The control device is designed as a universal system that can simultaneously manage multiple anesthetic agents (e.g., hypnotics like propofol and analgesics like remifentanil) through a single integrated interface. The same computational algorithms and display mechanisms work for all agents, allowing the system to handle different surgical needs without increasing operational complexity
Solution Approach 2:
The system adds a new dimension of control by simultaneously monitoring and displaying multiple indices (qCON for hypnotic effect, qNOX for analgesic effect) on the same display interface. This allows the practitioner to control multiple agents through a unified dimensional approach rather than requiring separate control mechanisms for each agent
Data Source
AI summary
A system for administering anesthetic agents to a patient (P) comprises a bio-signal monitor (5) for measuring at least one biological signal on the patient (P). an arrangement of infusion devices (31-33) for infusing at least a first anesthetic agent and a second anesthetic agent to the patient (P). and a control device (2) configured to compute. based on the at least one biological signal. a first index relating to a first effect caused by the first anesthetic agent and a second index relating to a second effect caused by the second anesthetic agent. The control device (2) is configured to compute, based on the first index and the second index. a first setting parameter for adjusting an infusion of the first anesthetic agent and a second setting parameter for adjusting an infusion of the second anesthetic agent.