Real-time pain detection system using physiological signal models
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Solution Overview
Problem
Current methods for pain detection and management are subjective and unreliable, especially in sedated patients, young children, and individuals with learning difficulties, as they rely on self-reported pain assessments, and fail to predict pain timing and intensity accurately for effective automated analgesic administration.
Innovation Solution
A system and method for real-time pain detection using biomedical signals like heart rate, ECG, and HRV, which transform and analyze data into pain and non-pain models to generate a pain index, enabling the timely administration of analgesics based on objective physiological data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If self-reported pain assessments are used, then ease of operation is improved, but reliability deteriorates in sedated patients, young children, and individuals with learning difficulties
Solution Approach 1:
The system uses automated physiological signal processing to perform pain assessment without requiring patient participation or self-reporting. The physiological signals (ECG, HRV, skin conductance) are automatically collected, processed, and analyzed by the system to generate objective pain indicators, making the assessment self-service oriented and eliminating reliance on patient communication abilities
Solution Approach 2:
The patent replaces the mechanical/subjective method of self-reported pain assessment with an objective physiological signal-based system. Instead of relying on patient verbal or behavioral reports, the system uses biomedical signals processed through algorithms to generate pain indicators, substituting the subjective assessment mechanism with an objective physiological measurement system
2Device complexity
If single physiological parameter measurement is used, then device complexity is reduced, but measurement precision deteriorates for pain detection
Solution Approach 1:
The system merges multiple physiological signal sources (ECG, HRV, skin conductance) into a unified pain assessment framework. By combining these different physiological parameters and analyzing them together through integrated algorithms, the system achieves more precise pain detection than any single parameter could provide alone
Solution Approach 2:
The system uses multiple physiological signals that all relate to pain processing and autonomic nervous system response. Each signal serves multiple functions: ECG provides heart rate and rhythm information, HRV provides autonomic tone information, and skin conductance provides sympathetic activation information. Together they create a multi-functional assessment system that improves measurement precision
3Productivity
If real-time pain prediction is implemented, then productivity of pain management is improved, but device complexity increases for automated analgesic administration
Solution Approach 1:
The system performs preliminary pain prediction by continuously analyzing physiological signals and generating pain indicators before severe pain occurs. This early detection and prediction capability allows for proactive analgesic intervention, improving pain management efficiency by addressing pain before it becomes severe rather than reacting after the fact
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring physiological signals, generating real-time pain indicators, and using these indicators to trigger or adjust analgesic administration. The feedback mechanism allows the system to adapt to changing pain states and optimize analgesic delivery timing and dosage based on actual physiological responses
Data Source
AI summary
The present invention provides a system for real-time pain detection, which comprises a means for acquiring biomedical signals relating to pain in a subject in need thereof, a computing means for transforming the acquired biomedical signals during a given period of time into the signal data for measurement of pain, analyzing the data to divide into two or more models, including at least a pain model which is defined by the data showing a peak-shaped profile and a non-pain model which is defined by the data showing a flat profile, whereby the pain status of the subject is measured based on the results of the analysis, a process means for generating an index of pain using the results of the analysis depending on the subject's demands or sensation, and a display showing the pain status of the subject.