System and method for measuring analgesic state and response to harmful or painful stimuli

The SS-ERP and KE-ERP methods address the limitations of existing pain measurement technologies by accurately extracting ERPs from noisy EEG data, facilitating individualized pain assessment during surgery.

JP2026510188APending Publication Date: 2026-04-02THE GENERAL HOSPITAL CORP
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Patent Information

Authority / Receiving Office
JP · JP
Patent Type
Applications
Current Assignee / Owner
Filing Date
2023-10-30
Publication Date
2026-04-02

AI Technical Summary

Technical Problem

Current methods for measuring pain and nociception during surgery lack a direct and objective means, relying on unreliable indicators like heart rate and blood pressure, and existing ERP systems are ineffective due to low signal-to-noise ratio and practical compliance issues, making it difficult to track individual patient responses.

Method used

A system and method using a state-space ERP (SS-ERP) and kernel-expanded ERP (KE-ERP) processing technology to model background vibrations, allowing for accurate extraction of ERPs even in the presence of large noise, enabling individualized pain measurement and analgesia assessment.

Benefits of technology

The method significantly improves ERP processing accuracy by 150 times, enabling effective pain and analgesia evaluation during general and local anesthesia, allowing for precise pain measurement in clinical settings with fewer stimulus events.

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Abstract

This disclosure provides a system and method for quantitatively evaluating pain and analgesic status. The system and method provide direct and objective evaluation of pain and pain reduction with multiple different drugs and therapies. The system and method thus provide feedback for tailoring drugs or therapies to target endpoints and can be used in the process of discovering or developing new drugs or therapies. The system and method includes a novel state-space evoked response potential (SS-ERP) model for extracting ERP signals from electroencephalogram (EEG) data from patients. The system and method disclosed hereby enable personalized pain measurement and pain treatment effectiveness.
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