Electrode Disconnect Detection with Pearson Noise-Spectrum Correlation
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Solution Overview
Problem
Neuromonitoring systems face challenges in detecting and correcting electrode disconnections during surgical procedures, which can lead to incorrect signal detection and potential nerve damage due to the difficulty in identifying detached electrodes, especially when monitoring is performed remotely.
Innovation Solution
A method and system that utilize a Pearson correlation coefficient to compare the power spectral density of electrode signals with known noise, alerting users to disconnections and implementing remediation measures, such as disregarding or reattaching electrodes, to ensure accurate neuromonitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electrodes are adhered to the patient's skin for neuromonitoring, then nerve responses can be monitored during surgery, but electrodes can be accidentally removed or fall off resulting in incorrect signals
Solution Approach 1:
The system continuously monitors electrode signals and provides feedback about electrode connection status. By analyzing signal characteristics and comparing them against expected patterns, the system detects when electrodes become disconnected and alerts the surgical team, ensuring continuous reliable monitoring throughout the procedure.
Solution Approach 2:
The patent replaces manual visual inspection of electrode connections with an automated electronic detection system. The neuromonitoring system automatically analyzes electrical signals to determine electrode status, substituting mechanical observation with electronic signal processing and automated diagnosis.
2Measurement precision
If the surgical team manually monitors electrode connections, then disconnections can be detected, but the individual performing neuromonitoring may be some distance away from the patient affecting their ability to readily see electrode status
Solution Approach 1:
The system introduces an intermediary automated detection mechanism between the distant monitor and the patient's electrodes. Instead of requiring direct visual access to electrode connections, the system uses electrical signal analysis as an intermediary method to convey connection status information to the remote surgical team.
Solution Approach 2:
The patent replaces the mechanical requirement of visual inspection with electronic signal processing. The system automatically detects electrode status through electrical characteristics, eliminating the need for the monitor to be physically close to the patient or to manually inspect connections.
3Reliability
If many recording sites are monitored, then comprehensive nerve coverage is achieved, but the identification of incorrect signals in a timely manner becomes difficult during surgery
Solution Approach 1:
The system segments the monitoring task by analyzing signals from each recording site independently through automated algorithms. Each electrode channel is evaluated separately for connection status and signal quality, allowing comprehensive monitoring of multiple sites while maintaining manageable complexity through systematic individual assessment.
Solution Approach 2:
The monitoring system performs self-service by automatically detecting and identifying incorrect signals without requiring manual intervention. The automated analysis continuously evaluates all recording sites, identifying disconnections or abnormal signals independently, which simplifies the overall system operation despite the large number of monitored channels.
4Loss of time
If detached electrodes are not detected, then the surgical team may take unnecessary steps to save the nerve, but detecting disconnections requires continuous monitoring and analysis of electrode signals
Solution Approach 1:
The system replaces energy-intensive continuous visual monitoring with efficient automated electronic signal processing. By using computational algorithms to analyze electrical characteristics, the system detects disconnections with minimal energy consumption while preventing unnecessary surgical interventions through accurate real-time detection.
Data Source
AI summary
Disclosed examples include those directed to detecting and remediating detachment of electrodes from a patient. In an example, a system calculates a Pearson correlation coefficient between: (1) power spectral density of the noise and (2) power spectral density of a recorded signal (e.g., from an electrode being operated in free-run EMG mode). If the recorded signal correlates with the noise, then the system notifies the user of presence of noise (e.g., the fallen electrode). Otherwise, the recorded signal is considered as the signal of interest (e.g., a valid EMG signal).