System, method, and computer algorithm for characterization and classification of electrophysiological evoked potentials
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.
1 . An automated evoked potential (EP) analysis apparatus for automatic monitoring, detecting, and identifying of changes (adverse or recovering) to a physiological system generating EP waveforms, wherein the apparatus is adapted to:
analyze the signal-to-noise ratio of acquired EP waveforms;
update the baseline using EP waveforms that are analyzed to have an adequate signal-to-noise ratio;
exclude each EP waveform having an inadequate signal-to-noise ratio;
track the number of inadequate EP waveforms; and
generate an alert if the number of inadequate EP waveforms exceeds a threshold.
2 . The apparatus of claim 1 , wherein the analyzing the signal-to-noise ratio of acquired EP waveforms is achieved using likelihood estimation-based averaging.
3 . The apparatus of claim 1 , wherein the apparatus is further adapted to automatically acquire and establish the baseline using real-time EP waveforms.
4 . The apparatus of claim 1 , wherein the apparatus is further adapted to characterize and classify the EP waveforms and create alerts of the changes (adverse or recovering) to the physiological system generating the EP waveforms if the acquired EP waveforms change significantly in latency, amplitude or morphology relative to a baseline.
5 . The apparatus of claim 1 , further comprising a system to integrate the apparatus into other devices in a surgical environment.
6 . The apparatus of claim 1 , wherein said apparatus is configured to feed information to other devices in a surgical environment to thereby allow the other devices to manually or automatically ameliorate or mitigate the changes and improve subsequently acquired EP waveforms.
7 . The apparatus in claim 1 , wherein said apparatus is configured to obtain information from an anesthesia or blood pressure machine to calculate when changes in the EP waveforms are due to anesthesia or blood pressure changes.
8 . The apparatus of claim 1 , wherein the apparatus comprises memory and a processor, wherein the processor is configured to execute instructions stored on the memory, that, when executed by the processor, cause the processor to perform operations comprising:
stimulating, via electrical pulse electrodes, a peripheral nerve structure;
recording, via electrodes placed at the neck or head, signals forming a resultant electrical (EP) waveform generated by the nervous system in response to the stimulation;
measuring a waveform change in the recorded EP waveform relative to a baseline response, the waveform change comprising a Euclidean distance, a pseudo-correlation, a cross-correlation, and an energy ratio between the recorded EP waveform and the baseline response;
comparing the waveform change with one or more threshold change values to classify the recorded EP waveform; and
determining whether a current state of the peripheral nerve structure has changed based on the classification of the recorded EP waveform by determining whether a count of EP waveforms in a classification exceeds a threshold count value.
9 . The apparatus of claim 8 , wherein the operations further comprise:
alerting a user to the waveform changes; and
providing the user with an option to decide if data associated with the waveform changes is accurate.
10 . The apparatus of claim 8 , wherein the operations further comprise:
transmitting the current state to an automated operating room table; and
automatically or semi automatically readjusting a patient position through adjustment of the table to ameliorate or avoid injury.
11 . The apparatus of claim 8 , wherein the waveform change comprises a Euclidean distance and at least one from a group consisting of: a pseudo-correlation, a cross-correlation, and an energy ratio between the recorded EP waveform and the baseline response.
12 . The apparatus of claim 8 , wherein the operations further comprise classifying the recorded EP waveform into four possible categories: good, bad, undetermined, and unreliable based on the change relative to the baseline response.
13 . The apparatus of claim 12 , wherein the threshold change value for a good classification relates to how similar the recorded EP waveform should be to the baseline response.
14 . The apparatus of claim 8 , wherein a threshold change value relates to how similar the recorded EP waveform should be to the baseline response.
15 . The apparatus of claim 8 , wherein determining whether the count of EP waveforms in the classification exceeds the threshold count value comprises:
counting a number of EP responses that are classified in a given classification;
comparing the current count to the threshold count value; and
if the current count exceeds the threshold count value, changing the state of the peripheral nerve structure to the state that corresponds with the exceeded threshold count value.
16 . The apparatus of claim 8 , wherein the operations further comprise generating an alert if the current state of the peripheral nerve structure has changed.