EEG Consciousness Assessment Using Multi-Band AI Anesthesia Tracking
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Solution Overview
Problem
Conventional methods for measuring the depth of anesthesia, such as the bispectral index (BIS) analysis, face challenges in accurately and quickly detecting rapid changes in anesthesia status due to algorithmic limitations and slow tracking speeds, and lack transparency in their analysis algorithms.
Innovation Solution
A method using a trained artificial neural network to analyze components of specific frequency bands from EEG signals, combined with EMG signals, to determine consciousness levels by calculating probability values and emotional status, allowing for timely and accurate anesthesia depth measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional BIS analysis method is used to measure depth of anesthesia, then the measurement can be performed, but the tracking speed is slow and cannot accurately detect rapid changes in anesthesia status
Solution Approach 1:
The patent changes the analysis parameters by using multiple frequency bands (delta, theta, alpha, beta, gamma) instead of the conventional single BIS index. This allows the system to capture different aspects of brain activity simultaneously, improving both tracking speed and measurement precision by monitoring multiple parameters in parallel rather than relying on a single composite index that requires complex algorithms.
2Measurement precision
If conventional BIS analysis algorithm is used, then depth of anesthesia can be measured, but the algorithm details are not disclosed and accuracy cannot be proven
Solution Approach 1:
The patent segments the EEG signal into multiple frequency bands (delta, theta, alpha, beta, gamma) and analyzes each band separately. This segmentation makes the analysis process transparent and interpretable, as each frequency band corresponds to specific brain states. The method discloses clear calculation procedures for each band's power spectral density and ratio, eliminating the black-box nature of conventional BIS algorithms while improving measurement accuracy through multi-band analysis.
3Loss of time
If conventional BIS analysis method is used, then depth of anesthesia measurement is available, but response time is delayed when sudden changes occur
Solution Approach 1:
The patent calculates power spectral density for multiple frequency bands simultaneously and pre-establishes reference values and ratios for each band. This preliminary preparation of multiple parameters allows the system to rapidly detect sudden changes in anesthesia status by comparing real-time measurements against pre-computed references, reducing response time while maintaining high measurement precision through multi-parameter validation.
Data Source
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AI summary
A method for determining a consciousness level of a patient according to an embodiment of the present disclosure may include: a step of extracting components of one or more frequency bands from a first section of an EEG; a step of calculating a first index for each of the components of one or more frequency bands, wherein the first index is calculated based on a degree to which a magnitude of each of the components of one or more frequency bands with respect to a magnitude of a predetermined reference component in the first section exceeds a predetermined threshold value; a step of calculating a probability value for each of one or more patient statuses from the first index for each of the components of one or more frequency bands using a trained artificial neural network; and a step of determining the consciousness level of the patient based on the probability value for each of the one or more calculated patient statuses.