Composite Nociception Index Using EEG, ECG, and ICG Signals
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Solution Overview
Problem
Current methods for monitoring nociception and analgesia during anesthesia are inadequate, often relying on non-specific autonomic responses and failing to provide objective, real-time measures, leading to potential intraoperative awareness and postoperative psychosomatic dysfunction.
Innovation Solution
A multi-parameter approach combining EEG, facial EMG, HRV, and ICG data to calculate a Composite Nociception Index (CNI) using FFT, Hilbert transform, and Choi-Williams distributions, which provides a more objective and reliable assessment of nociception and analgesia levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single parameter method (e.g., Ramsay scale) is used to assess nociception, then the device complexity is low, but the measurement precision and reliability are insufficient
Solution Approach 1:
The patent combines multiple monitoring parameters (EEG, EMG, ECG, ICG, respiratory rate, blood pressure, oxygen saturation) into a unified nociception assessment system. The processing unit integrates signals from different physiological systems to generate a comprehensive nociception index, thereby improving measurement precision while managing system complexity through systematic integration.
Solution Approach 2:
The monitoring system is designed to perform multiple functions simultaneously: it monitors consciousness level (EEG), muscle activity (EMG), cardiac function (ECG, ICG), respiratory status, and nociception response. This multi-functional approach allows a single system to provide comprehensive anesthesia monitoring without requiring separate specialized devices for each parameter.
2Reliability
If multiple parameters are combined to improve nociception assessment, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The system continuously monitors multiple physiological parameters and provides real-time feedback through the nociception index display. The processing unit analyzes incoming signals from EEG, EMG, ECG, ICG and other sensors, processes them through established algorithms, and immediately updates the nociception assessment, allowing dynamic adjustment of analgesia based on actual patient response.
Solution Approach 2:
The system automatically processes and integrates multiple physiological signals without requiring manual intervention for each parameter. The processing unit autonomously performs signal integration, noise filtering, and nociception index calculation, reducing the operational burden on anesthesia providers while maintaining high reliability through continuous automated monitoring.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method offers a safer and more accurate monitoring of nociception and analgesia, reducing the risk of intraoperative awareness and postoperative complications by providing a continuous, non-invasive, and objective measure of pain and analgesia levels.
Implementation Method 1
means adapted for obtaining a signal containing electroencephalogram (EEG) and facial electromyogram (EMG) from a subjects scalp with three electrodes positioned at middle forehead, left (right) forehead and the left (right) cheek
Implementation Method 2
means adapted for obtaining a three leads electrocardiogram (ECG) signal and adaptations for calculating the R-R interval and the heart rate variability (HRV) from said ECG signal
Implementation Method 3
means adapted for obtaining an impedance cardiography (ICG) signal with four electrodes positioned at the chest of the patient; means adapted for obtaining the plethysmographic from the impedance cardiography (ICG)
Implementation Method 4
adaptations for calculating the Fast Fourier Transform (FFT) and the Choi-Williams distributions for about 1-60 seconds of the EEG signal
Implementation Method 5
adaptations for calculating the Hilbert transform of the ICG signal from which the number of peaks over a certain threshold in the 1st derivative of the Hilbert phase, is estimated
Data Source
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AI summary
Means and methods for measuring pain and adapted for calculating the level of nociception during general anesthesia or sedation from data including electroencephalogram (EEG), facial electromyogram (EMG), heart rate variability (HRV) by electrocardiogram (ECG) and plethysmography by impedance cardiography (ICG). In a preferred embodiment of this invention the parameters derived from the EEG, the HRV, the plethysmographic curve and the analgetics concentrations are either combined into one index on a scale from 0 to 100, where a high number is associated with high probability of response to noxious stimuli, while a decreasing index is associated with decreasing probability of response to noxious stimuli. Zero (0) indicates extremely low probability of response to noxious stimuli. In an alternative embodiment, only features from the EEG and ECG will be used or only features from EEG, ECG and ICG, to define the final index.