Evoked Potential Classification for Real-Time Nerve Injury Alerts
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Solution Overview
Problem
Existing somatosensory evoked potential monitoring during surgery is limited by the availability and cost of highly trained personnel, and existing systems fail to effectively address positioning effects outside the operating room due to variations in waveform amplitude, frequency, and shape caused by anesthesia and electrical interference.
Innovation Solution
A computer algorithm for characterizing and classifying electrophysiological evoked potentials (EPs) that automates the analysis of EP waveforms, using ensemble averaging and pattern recognition to minimize false positives and negatives, and provides real-time feedback to detect nerve injuries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly trained technologists and neurologists are used to monitor and interpret EP waveforms, then measurement precision and reliability are improved, but device complexity and cost increase significantly
Solution Approach 1:
The system enables self-service monitoring by automating the EP waveform analysis through computer algorithms that automatically detect, characterize, and classify evoked potentials without requiring expert personnel interpretation. The algorithm independently processes the signals and generates monitoring results.
Solution Approach 2:
The patent replaces the mechanical system of human expert interpretation with an automated computer-based algorithm. The algorithm substitutes the cognitive analysis performed by technologists and neurologists with computational signal processing and pattern recognition methods.
2Measurement precision
If expert personnel are fully engaged in interpreting EP waveforms, then measurement precision is improved, but productivity and accessibility decrease due to rationing of services
Solution Approach 1:
The automated algorithm performs self-service monitoring by independently analyzing EP waveforms without requiring expert personnel engagement for each case. This enables the system to handle multiple patients simultaneously, dramatically increasing service availability and eliminating the need to ration monitoring based on expert personnel availability.
3Reliability
If traditional monitoring systems are used, then reliability is maintained in controlled settings, but adaptability decreases for use outside operating rooms where positioning effects occur
Solution Approach 1:
The automated monitoring system is designed with universal applicability across multiple clinical settings. The algorithm can process EP waveforms regardless of whether they are recorded in operating rooms, recovery areas, or other clinical environments, making the system adaptable to various locations where positioning effects may occur.
Solution Approach 2:
The system adapts to different clinical environments by dynamically adjusting analysis parameters based on the recorded signal characteristics. The algorithm can accommodate variations in waveform quality, amplitude, and frequency that occur in different settings, maintaining reliable detection across diverse conditions.
Data Source
AI summary
An automated EP analysis apparatus for monitoring, detecting and identifying changes (adverse or recovering) to a physiological system generating the analyzed EPs, wherein the apparatus is adapted to characterize and classify EPs and create alerts of changes (adverse or recovering) to the physiological systems generating the EPs if the acquired EP waveforms change significantly in latency, amplitude or morphology.