System, method, and computer algorithm for measuring, displaying, and accurately detecting changes in electrophysiological evoked potentials
Inventors
JOHNS, Gregg • O'Brien, Richard • Snow, Robert
Assignees
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Publication Number
US-11197640-B2
Publication Date
2021-12-14
Expiration Date
Abstract
An automated evoked potential analysis apparatus for improved monitoring, detecting and identifying changes to a patient's physiological system is described. The apparatus includes an input device for obtaining electrical potential data from the patient's physiological system after application of stimulation to a patient's nerve and a computing system for receiving and analyzing the electrical potential data. The computing system includes a processing circuit configured to: generate a plurality of evoked potential waveforms (EPs) based on the electrical potential data, measure changes or trends in the generated EPs utilizing a sliding window of analysis, display incremental changes in waveforms between full subsets of EPs, and determine an alert vote for each subset, representative of changes to the physiological system generating the EPs.
Core Innovation
The invention relates to an automated evoked potential analysis apparatus for monitoring, detecting and identifying changes to a patient’s physiological system. Electrical potential data are obtained from the patient’s physiological system after application of stimulation to a nerve, and the computing system generates a plurality of evoked potential waveforms based on the electrical potential data. The computing system calculates and displays a plurality of ensemble average waveforms using first and second subsets that represent a sliding window, and the second ensemble average waveform is displayed to show incremental changes between the first and second subsets of evoked potential waveforms.
The apparatus identifies a characteristic in each ensemble average waveform, the characteristic comprising a latency, an amplitude, or a morphology. For each subset, the apparatus determines an alert vote representative of changes to the physiological system by creating a positive alert vote if the characteristic of a particular ensemble average waveform reaches a change threshold compared to a preceding ensemble average waveform, and by creating a negative alert vote if the characteristic does not reach the change threshold compared to the preceding ensemble average waveform.
After calculating a third ensemble average waveform of a third subset of evoked potential waveforms that does not overlap with the first subset, the apparatus determines whether a ratio of positive alert votes to negative alert votes exceeds a threshold value. Upon determining that the ratio exceeds the threshold value, the apparatus triggers an alert. The disclosure further includes monitoring electrophysiological evoked potential analysis, presenting incremental waveform morphology changes over time, and generating repeated ensemble-average evoked potentials using a sliding window to mitigate noise and bias.
Claims Coverage
The independent claims are 1 and 6. Both claims focus on an automated evoked potential analysis approach that uses sliding-window ensemble averages, characteristic change detection, and positive/negative alert votes whose ratio is compared to a threshold to trigger an alert; claim 6 additionally frames the approach as a method for identifying potential injury to peripheral nerve structures.
Sliding-window ensemble-average EP display for incremental change
Generating a plurality of evoked potential waveforms; calculating a first ensemble average waveform of a first subset; calculating a second ensemble average waveform of a second subset that eliminates at least one initial waveform and includes at least one additional waveform, with the first and second subsets comprising the same number of waveforms and representing a sliding window; and displaying the first and second ensemble average waveforms to show incremental changes.
Latency, amplitude, or morphology characteristic change detection with thresholded votes
Identifying a characteristic in each ensemble average waveform, the characteristic comprising a latency, an amplitude, or a morphology; determining an alert vote for each subset by creating a positive alert vote when the characteristic of a particular ensemble average waveform reaches a change threshold compared to a preceding ensemble average waveform; and creating a negative alert vote when the characteristic does not reach the change threshold compared to the preceding ensemble average waveform.
Positive-to-negative alert vote ratio threshold triggering
Calculating a third ensemble average waveform of a third subset that does not overlap with the first subset; determining whether a ratio of positive alert votes to negative alert votes exceeds a threshold value; and triggering an alert upon determining that the ratio exceeds the threshold value.
Peripheral nerve injury identification using electrodes over a nerve pathway and sliding-window analysis
Stimulating a peripheral nerve with electrical pulses; recording resultant electrical waveforms using electrodes placed over a nerve pathway; measuring changes in the acquired waveforms utilizing a sliding window of analysis; identifying a characteristic comprising latency, amplitude, or morphology in each ensemble average waveform; determining alert votes by positive and negative thresholded change; calculating a third ensemble average waveform from a non-overlapping third subset; determining whether a ratio of positive to negative alert votes exceeds a threshold value; triggering an alert to the user; and alerting the user to the changes.
Across the independent claims, the inventive core is the combination of sliding-window ensemble-average waveform generation with incremental display, characteristic-based positive and negative alert votes using a change threshold, and triggering an alert when a ratio of positive to negative alert votes exceeds a threshold; in claim 6 this is implemented as a method to identify potential injury to peripheral nerve structures.
Stated Advantages
Reducing false positive and false negative alerts by mitigating noise and bias.
Documented Applications
Monitoring electrophysiological evoked potentials to detect and identify changes to a patient’s physiological system in an automated evoked potential analysis apparatus.
Automatically identifying potential injury to peripheral nerve structures by electrically stimulating a peripheral nerve and analyzing recorded evoked potential waveforms.
Integration in a surgical environment by sending information to an operating room table to reposition a patient to ameliorate or mitigate physiological changes.
Using the apparatus to alert a user to changes in evoked potential characteristics including latency, amplitude, or morphology.
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