Sparse Modeling Brainwave Analysis for Objective Pain Classification
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Solution Overview
Problem
Current methods for objectively evaluating pain are subjective and challenging due to individual differences in pain perception, making it difficult to classify and differentiate pain levels accurately using brainwaves.
Innovation Solution
A method and apparatus utilizing sparse modeling to analyze brainwave data, extracting features, and generating a regression model to objectively classify and differentiate pain levels by determining suitable parameters and coefficients, enabling accurate pain estimation and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If subjective pain evaluation methods are used, then individual pain perception differences are captured, but objective evaluation accuracy deteriorates
Solution Approach 1:
The patent introduces brainwaves as an intermediary mediator between the subjective pain sensation and objective measurement. The brainwave analysis system translates subjective pain experiences into objective, quantifiable signals that can be measured and classified without relying on subjective self-reporting, thereby resolving the contradiction between capturing individual pain perception and achieving objective evaluation accuracy
Solution Approach 2:
The patent replaces the mechanical system of subjective pain reporting with a neurophysiological measurement system. By substituting the subjective evaluation mechanism with objective brainwave analysis, the system maintains sensitivity to individual pain differences while eliminating the subjectivity inherent in traditional pain assessment methods
2Reliability
If pain classification technology is developed, then therapeutic effect observation is improved, but technology complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming complex brainwave data into simplified pain level classifications. By changing the parameters from raw neural signals to categorized pain intensity levels, the system achieves reliable therapeutic effect observation while managing technological complexity through dimensionality reduction and feature extraction
3Device complexity
If sparse modeling with fewer parameters is used, then model simplicity is improved, but information loss increases
Solution Approach 1:
The patent applies the extraction principle by selectively extracting the most informative features from brainwave data for sparse modeling. By taking out only the critical pain-relevant parameters while discarding redundant information, the system achieves model simplicity without significant information loss, as the extracted features capture the essential pain-related neural patterns
Data Source
AI summary
A method is described for making determinations on or classifying the pain of an estimation subject on the basis of the brainwaves of the estimation subject. This method includes: (a) stimulating the estimation subject at a plurality of levels of stimulation intensity; (b) acquiring brainwave data for the estimation subject; (c) extracting a brainwave feature quantity from the brainwave data or the analysis data; (d) for plugging the feature quantity into a Sparse model analysis, making the feature quantity approach a quantitative level and/or a qualitative level for pain, and estimating or making a determination on a pain level. Another method is described including comparing of brainwave data or analysis data from the 2,000 msec following the earliest of an induced brainwave component, an initial-event-related voltage component, and 250 msec after a target stimulus has been applied.