NERVE MONITORING DURING ELECTROSURGERY

• US 20100198099A1
• Filed: 01/30/2009
• Published: 08/05/2010
• Est. Priority Date: 01/30/2009
• Status: Active Grant

• Current Assignee: Medtronic Xomed Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Xomed Incorporated (Medtronic Plc)
• Inventors: Mendez, Adnorin Luis, Meyer, John A., Murphy, John Murdock, Hacker, David C., McFarlin, Kevin Lee
• Granted Patent: US 8,989,855 B2
• Time in Patent Office: Days
• US Class Current: 600/546
• CPC Class Codes: A61B 17/32 Surgical cutting instruments A61B18/042 takes precedence; suture cutters A61B17/0467; instruments for ligaturing or cutting A61B17/128; instruments for rupturing the amniotic membrane A61B17/4208; specially adapted knives for eye surgery A61F9/0133
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/7217 of noise originating from a therapeutic or surgical apparatus, e.g. from a pacemaker
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00

Abstract

A signal processing module includes an input module electronically coupled to a sensing probe of a nerve integrity monitoring system. The probe senses electrical signals from a patient during operation of an electrosurgical unit. The input module receives an input signal from the probe. An EMG detection module is coupled to the input module and is adapted to detect conditions in the input signal. The conditions are classified as a function of a level of electromyographic activity. An output module, coupled to the EMG detection module, provides an indication of electromyographic activity in the input signal based on the detected conditions.

Claims

1. A signal processing module for a nerve integrity monitoring system adapted to sense electrical signals from a patient during operation of an electrosurgical unit and/or surgical metal-to-metal artifacts, the signal processing module comprising:

an input module electrically coupled to a sensing probe and adapted to receive an input signal indicative of electromyographic (EMG) activity of the patient and operation of an electrosurgical unit;

an EMG detection module coupled to the input module and adapted to detect conditions in the input signal and classify the conditions as a function of a level of EMG activity received from the patient; and

an output module coupled to the EMG detection module and adapted to provide an output signal indicative of the detected conditions.

2. The signal processing module of claim 1 and further comprising an artifact detection module adapted to detect an artifact in the input signal and provide an indication to suppress the output signal to the output module.

3. The signal processing module of claim 2 wherein the artifact is detected based on an estimate of power in the input signal.

4. The signal processing module of claim 2 wherein the artifact is detected based on an estimate of the frequency content of the input signal.

5. The signal processing module of claim 2 wherein the input module is adapted to receive multiple input signals from multiple sensing probes and wherein the muting module is adapted to selectively suppress at least one of the multiple input signals.

6. The signal processing module of claim 1 and further comprising a direct current filter module adapted to suppress low frequency noise in the input signal.

7. The signal processing module of claim 1 wherein the EMG detection module is adapted to compare a level of EMG activity detected in the input signal to a threshold and provide an indication of the comparison to the output module.

8. The signal processing module of claim 7 wherein the EMG detection module estimates a level of energy in the input signal to determine if the EMG activity is greater than an energy level threshold.

9. The signal processing module of claim 8 wherein the EMG detection module calculates an autocovariance or autocorrelation of the input signal to detect the level of EMG activity.

10. The signal processing module of claim 8 wherein the EMG detection module calculates an autocovariance or autocorrelation of the input signal using multiple samples of the input signal.

11. The signal processing module of claim 9 where the EMG detection module uses a wavelet or sigmoid classification function of the input signal using multiple samples of the input signal.

12. The signal processing module of claim 1 and further comprising an EMG recovery module adapted to filter noise created by the electrosurgical unit and provide to the output module an indication of EMG activity in the patient.

13. The signal processing module of claim 12 wherein the EMG recovery module uses a reference probe to estimate the noise created by the electrosurgical unit.

14. A surgical method, comprising:

attaching a sensing probe to a tissue of a patient;

operating an electrosurgical unit proximate the probe;

receiving an input signal from the sensing probe indicative of electromyographic (EMG) activity of the patient and operation of the electrosurgical unit;

classifying the input signal received from the probe as a function of a level of EMG activity received from the patient; and

providing an output signal indicative of the level of EMG activity.

15. The method of claim 14 and further comprising:

detecting an artifact in the input signal and providing an indication to suppress the output signal.

16. The method of claim 15 wherein the artifact is detected based on an estimate of power in the input signal.

17. The method of claim 15 and further comprising:

receiving multiple input signals from multiple sensing probes indicative of EMG activity of the patient and operation of the electrosurgical unit;

detecting an artifact in at least one of the input signals; and

selectively suppressing said at least one of the multiple input signals.

18. The method of claim 14 and further comprising:

suppressing a direct current component or low frequency noise in the input signal.

19. The method of claim 14 and further comprising:

comparing a level of EMG activity in the input signal to a threshold and providing an indication of the comparison.

20. The method of claim 19 and further comprising:

estimating a level of energy in the input signal to determine if the level of EMG activity is greater than an energy level threshold.

21. The method of claim 20 and further comprising:

calculating an autocovariance or autocorrelation of the input signal to detect the level of EMG activity.

22. The method of claim 21 wherein an artifact is detected based on a high energy signal with a very low autocovariance or autocorrelation.

23. The method of claim 20 and further comprising:

classifying the input signal as EMG activity based on a wavelets or sigmoid classification function.

24. The method of claim 21 wherein calculating the autocovariance or autocorrelation of the input signal includes using multiple samples of the input signal.

25. The method of claim 14 and further comprising:

filtering noise created by the electrosurgical unit and providing an indication of EMG activity in the patient.

26. The method of claim 23 and further comprising:

using a reference probe to estimate the noise created by the electrosurgical unit.

27. The method of claim 14 and further comprising:

filtering a fundamental frequency of a noise signal generated by the electrosurgical unit.

28. A nerve integrity monitoring system for use during operation of an electrosurgical unit, comprising:

a sensing probe adapted to generate an input signal indicative of electromyographic (EMG) activity of the patient and operation of the electrosurgical unit;

an input module electrically coupled to the sensing probe and adapted to receive the input signal;

an EMG detection module coupled to the input module and adapted to detect conditions in the input signal and classify the conditions as a function of a level of EMG activity received from the patient; and

an output module coupled to the EMG detection module and adapted to provide an output signal indicative of the detected conditions.

29. The nerve integrity monitoring system of claim 28 and further comprising an artifact detection module adapted to detect an artifact in the input signal and provide an indication to suppress the output signal to the output module.

30. The nerve integrity monitoring system of claim 29 wherein the artifact is detected based on an estimate of power in the input signal.

31. The nerve integrity monitoring system of claim 29 wherein the artifact is detected based on an estimate of frequency content of the input signal.

32. The nerve integrity monitoring system of claim 29 wherein the input module is adapted to receive multiple input signals from multiple sensing probes and wherein the muting module is adapted to selectively suppress at least one of the multiple input signals.

33. The nerve integrity monitoring system of claim 28 and further comprising a direct current filter module adapted to suppress low frequency noise in the input signal.

34. The nerve integrity monitoring system of claim 28 wherein the EMG detection module is adapted to compare a level of EMG activity detected in the input signal to a threshold and provide an indication of the comparison to the output module.

35. The nerve integrity monitoring system of claim 34 wherein the EMG detection module estimates a level of energy in the input signal to determine if the EMG activity is greater than an energy level threshold.

36. The nerve integrity monitoring system of claim 35 wherein the EMG detection module calculates an autocovariance or autocorrelation of the input signal to detect the level of EMG activity.

37. The nerve integrity monitoring system of claim 36 wherein the EMG detection module calculates an autocovariance or autocorrelation of the input signal using multiple samples of the input signal.

38. The nerve integrity monitoring system of claim 28 and further comprising an EMG recovery module adapted to filter noise created by the electrosurgical unit and provide to the output module an indication of EMG activity in the patient.

39. The nerve integrity monitoring system of claim 38 wherein the EMG recovery module uses a reference probe to estimate the noise created by the electrosurgical unit.

40. The nerve integrity monitoring system of claim 28 wherein the probe includes a filter to filter a fundamental frequency of a noise signal generated by the electrosurgical unit.

41. The nerve integrity monitoring system of claim 39 wherein the EMG recovery module uses a reference probe to cancel or filter the noise created by the electrosurgical unit.