Anesthetic Depth Prediction via PVP Waveform Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current anesthesia depth assessors for pediatric patients are not minimally invasive and rely on unreliable clinical signs, making it challenging to accurately predict the effect of inhaled and infused anesthetics, particularly in pediatric populations.
Innovation Solution
A method using peripheral venous pressure (PVP) waveforms, cleaned and transformed into the frequency domain, to predict hemodynamic states and anesthetic depth through machine learning models such as k-NN, neural networks, and SVM, allowing for real-time, minimally invasive monitoring of anesthetic effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional clinical signs (hypertension, tachycardia, lacrimation) are used to assess anesthesia depth, then the assessment method is simple and non-invasive, but the reliability of the indicator is poor
Solution Approach 1:
The patent replaces traditional mechanical/clinical observation methods with electrical signal processing. Specifically, it uses EEG (electroencephalogram) signals to assess anesthesia depth, substituting the mechanical observation of clinical signs with electrical field detection and spectral analysis, thereby improving reliability while maintaining ease of operation
Solution Approach 2:
The patent introduces spectral edge frequency (SEF) as an intermediary parameter that translates complex EEG signals into a single quantifiable metric. This intermediary enables reliable anesthesia depth assessment by mediating between the raw electrical signals and the clinical interpretation, making the system both reliable and easy to use
2Reliability
If real-time signal processing methods (raw or summated EEG, lower oesophageal contractility) are used, then the assessment is performed in real-time, but the reliability of the indicator remains poor
Solution Approach 1:
The patent applies preliminary signal processing steps including artifact removal, filtering, and spectral transformation before extracting the final SEF metric. These preliminary actions prepare the raw EEG signals in advance, ensuring that the subsequent real-time assessment is both reliable and time-efficient
Solution Approach 2:
The patent transforms the EEG signal from the time domain to the frequency domain through spectral analysis, changing the parameter representation from voltage over time to power spectral density. This parameter transformation enables more reliable extraction of anesthesia depth indicators while maintaining real-time processing capability
3Measurement precision
If dimensionless monotonic index is used as a measure of anesthetic depth, then the measurement is simplified, but the precision of the measurement is reduced
Solution Approach 1:
The patent moves the measurement from a single dimension (dimensionless monotonic index) to multiple dimensions by analyzing the spectral edge frequency across different frequency bands and time windows. This dimensional expansion provides more precise measurement of anesthetic depth while the automated processing keeps system complexity manageable
Data Source
AI summary
Disclosed herein are systems and methods for non-invasively predicting a hemodynamic state and/or an anesthetic depth of a patient, such as a pediatric patient. The method may include receiving a peripheral venous pressure (PVP) waveform from the patient, cleaning the PVP waveform, transforming the PVP waveform into the frequency domain, and automatically predicting the hemodynamic state and/or the anesthetic depth of the patient.