Multi-channel Stimulation Threshold Detection Algorithm
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Solution Overview
Problem
Current neurophysiology monitoring techniques require significant time to assess nerve stimulation thresholds over multiple channels, increasing surgery time and associated risks and costs.
Innovation Solution
An algorithm that uses a combination of bracketing and bisection methods to quickly determine stimulation thresholds by electrically stimulating nerve tissue and analyzing muscle activity, reducing the number of necessary stimulations through predictive inference and confirmation steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neurophysiology monitoring is performed over multiple channels to assess nerve stimulation thresholds, then the accuracy and reliability of neural tissue assessment is improved, but the time required to complete monitoring increases
Solution Approach 1:
The algorithm performs preliminary actions by establishing voltage brackets and identifying threshold ranges before conducting all stimulations. It pre-processes data by organizing EMG responses and stimulation voltages into structured formats, allowing for efficient subsequent analysis across multiple channels without redundant processing.
Solution Approach 2:
The algorithm uses predictive inference to create virtual copies of threshold detection results. By inferring stimulation thresholds for channels where no significant EMG response occurs, it avoids performing actual stimulations and measurements on all channels, thereby reducing monitoring time while maintaining assessment reliability.
2Measurement precision
If the number of stimulation trials is increased to ensure accurate threshold detection, then the precision of stimulation threshold determination is improved, but the overall procedure time increases
Solution Approach 1:
The algorithm applies partial action by performing stimulations only on channels where significant EMG responses are detected. For channels without significant responses, it uses predictive inference to determine thresholds without additional stimulations. This selective approach maintains measurement precision for critical channels while reducing overall procedure time.
Solution Approach 2:
The algorithm uses feedback from EMG response analysis to dynamically adjust the monitoring process. By analyzing the magnitude and significance of EMG responses, it determines which channels require further stimulation trials and which can be processed through predictive inference, optimizing the balance between precision and time efficiency.
3Reliability
If comprehensive multi-channel monitoring is performed to reduce surgical risks, then the safety and reliability of surgical procedures is improved, but the surgical time and associated costs increase
Solution Approach 1:
The algorithm performs preliminary data processing and threshold range identification across all channels before detailed analysis. It pre-organizes stimulation parameters and EMG responses, enabling efficient subsequent processing that maintains comprehensive safety monitoring while reducing overall surgical time through optimized computational workflows.
Solution Approach 2:
The algorithm creates inferred threshold values for channels with non-significant EMG responses, serving as virtual copies that reduce the need for extensive actual measurements. This approach maintains comprehensive safety assessment across all monitored channels while significantly reducing the time and resources required for complete multi-channel monitoring.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The algorithm significantly reduces the time required to determine stimulation thresholds for multiple channels, thereby decreasing overall surgery time and risk to the patient while providing efficient neural tissue assessment.
Implementation Method 1
EMG responses can be characterized by a peak-to-peak voltage of Vpp=Vmax−Vmin. Characteristics of the electrical stimulation signal used may vary depending upon several factors including; the particular nerve assessment performed, the spinal target level, the type of neural tissue stimulated
Data Source
AI summary
The present invention relates generally to an algorithm aimed at neurophysiology monitoring, and more particularly to an algorithm capable of quickly finding stimulation thresholds over multiple channels of a neurophysiology monitoring system.