Motor unit number estimation (MUNE) for the assessment of neuromuscular function

• US 20080051673A1
• Filed: 08/17/2007
• Published: 02/28/2008
• Est. Priority Date: 08/17/2006
• Status: Active Grant

• Current Assignee: Neurometrix, Inc.
• Original Assignee: Neurometrix, Inc.
• Inventors: Kong, Xuan, Han, Zhixiu, Gozani, Shai
• Granted Patent: US 7,848,797 B2
• Time in Patent Office: Days
• US Class Current: 600/546
• CPC Class Codes: A61B 5/05 Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves impedance plethysmography A61B5/0295; measuring movement of the entire body or parts thereof A61B5/11; detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof A61B5/24
• CPC Class Codes: A61B 5/1106 to assess neuromuscular blockade, e.g. to estimate depth of anaesthesia
• CPC Class Codes: A61B 5/24 Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
• CPC Class Codes: A61B 5/4041 Evaluating nerves condition
• CPC Class Codes: A61B 5/4519 Muscles A61B5/389, A61B5/224 take precedence
• CPC Class Codes: A61B 5/6825 Hand

Abstract

A method for the assessment of neuromuscular function by motor unit number estimation, comprising: (i) determining and controlling stimulation and data acquisition process via pre-configured electrode array so as to acquire stable and less uncertainty MU responses; (ii) pre-processing acquired MUs responses so as to attenuate noise, determine MUs activity region, and improve processing speed and accuracy; (iii) minimizing alternation effects by globally searching and comparing SMUPs; (iv) eliminating alternation effects by identifying alternating MUs directly; and (v) computing and reporting MUNE results, as well as the statistical description of these MUN estimates to evaluate its robustness.

Claims

1. A method for the assessment of neuromuscular function by the estimation of motor unit numbers, comprising:

interfacing an electrode array with the subject for the purpose of delivering one or more controlled stimuli using at least one stimulator electrode and acquiring electric activity signals of one or more motor units using at least one detector electrode;

delivering a stimulus of varying characteristics to the subject;

identifying and eliminating any acquired signals due to alternation; and

automatically estimating motor unit number based on the consolidated acquired signals using an automation algorithm.

2. A method according to claim 1 wherein the electrode array consists of individual electrodes placed independently on the subject according to anatomical landmarks.

3. A method according to claim 1 wherein the electrode array consists of electrodes whose positions, relative to each other, are fixed for some or all electrodes.

4. A method according to claim 1 wherein the electrode array consists of one or more pairs of stimulator electrodes, wherein the relative positions of the stimulator electrodes are selected from the group consisting of:

(i) fixed, and (ii) adjustable.

5. A method according to claim 1 wherein the characteristics of the stimuli are controlled and adjusted according to one selected from the group consisting of:

(i) directly and manually by the operator, and (ii) automatically by a computer algorithm.

6. A method according to claim 5 wherein the characteristics of the stimuli include at least one selected from the group consisting of:

duration, intensity, polarity, frequency, and other features of electric current to be delivered via stimulus electrodes.

7. A method according to claim 5 wherein the control and adjustment of the stimuli is based on the feature space of the responses measured from the detector electrodes.

8. A method according to claim 7 wherein the responses measured from the detector electrodes are analyzed for features, wherein the features include at least one selected from the group consisting of:

the onset, total duration, peak duration, peak amplitude, and peak area.

9. A method according to claim 8 wherein the feature space includes at least one from the group consisting of:

the absolute values of the features, and changes in the absolute values from response to response.

10. A method according to claim 1 wherein the automation algorithm consists of:

(i) pre-processing acquired responses so as to consolidate and enhance the acquired signals;

(ii) identifying plausible new motor unit activation events and corresponding response changes;

(iii) determining representatives of single motor unit potential features, and computing approaches; and

(iv) estimating motor unit number and its distribution statistics.

11. A method according to claim 10 wherein pre-processing the acquired waveforms comprises:

a. estimating background noise level;

b. determining motor unit activity region in the acquired waveform;

c. ordering waveforms according to activity level, and removing noise-only waveforms; and

d. identifying and combining waveforms with insignificant morphological variations in relation to background noise.

12. A method according to claim 11 wherein a background noise level is determined by:

a. using waveform segments known to have no motor unit activity; and

b. estimating the statistics of noise level such as mean absolute deviation, standard deviation, and mean square error.

13. A method according to claim 12 wherein an activity-free segment is identified as:

a. waveforms acquired with zero or negligible stimulation strength; and

/or

b. segments of waveform occurring before the onset of overall CMAP activity, even though the complete waveform may still contain some motor activity.

14. A method according to claim 11 wherein a motor unit activity region is determined by:

a. calculating median value of all acquired waveforms;

b. determining deviation of individual waveforms from the calculated median value for each time instance;

c. constructing an activity profile by averaging the deviations over all waveforms; and

d. finalizing an activity region by comparing the activity profile with background noise level.

15. A method according to claim 11 wherein waveforms are windowed with the activity region, and the windowed waveforms are used for:

a. calculating the power level of each waveform within the activity region as determined in claim 14;

b. ordering all waveforms according to their power levels; and

c. removing noise-only waveforms if their power level is below a noise threshold.

16. A method according to claim 11 wherein waveform variations are quantified by a similarity measure based on at least one selected from the group consisting of:

Euclidian distance, correlation, mean absolute difference, and other comparable measure.

17. A method according to claim 11 wherein waveforms with insignificant morphological variations are:

a. identified when their similarity measures are above a threshold value related to noise level; and

b. combined to yield a combined waveform by means of at least one selected from the group consisting of;

arithmetic averaging, weighted averaging, other linear combination approaches and nonlinear combination approaches.

18. A method according to claim 10 wherein identifying plausible new motor units from a collection of acquired signals comprises:

a. optimizing a global search of motor units; and

/or

b. identifying alternation motor unit responses directly.

19. A method according to claim 18 wherein optimizing a global search of true motor units comprises:

a. establishing a ranking criteria for quantitative evaluation of SMUP waveforms at least one of individually, and in groups;

b. establishing a global search strategy to extract SMUPs; and

c. optimizing the extracted SMUPs to determine the solutions.

20. A method according to claim 19 wherein an SMUP ranking system is established based upon a consideration part of some or all of the features of at least one from the group consisting of:

SMUP responses, CMAP responses, response power, response onset, response end, response duration, response turn, response phase, response area, response amplitude, response peaks, and response peak-distance.

21. A method according to claim 19 wherein a SMUP group is defined as one possible combination set of SMUPs extracted from consolidated MU waveforms.

22. A method according to claim 20 wherein an SMUP is scored by:

a. calculating the offset of a SMUP waveform power over that of the average level of its group;

b. calculating the offset of a SMUP waveform onset over that of the CMAP;

c. calculating the offset of a SMUP waveform duration over that of the CMAP;

d. calculating the offset of a SMUP waveform maximum negative peak location over that of the CMAP;

e. calculating the offset of the waveform numbers of this SMUP group over the total number of consolidated MU waveforms; and

g. weighting, and then summing, all the aforementioned offsets so as to generate the SMUP waveform score.

23. A method according to claim 19 wherein a global search strategy is established by:

a. determining a decision deferment level; and

b. extracting new SMUPs considering consolidated MU waveforms both with alternation and without alternation issues at a pre-determined deferment level.

24. A method according to claim 23 wherein a decision deferment level is determined as a number that represents the maximum number of alternating MUs, with overlapping thresholds.

25. A method according to claim 24 wherein a decision deferment level is determined so as to be a value between (i) one, which represents step-by-step sequential manual processing, and (ii) the number of total consolidated waveforms, which considers all possible alternation cases in the acquired responses.

26. A method according to claim 23 wherein new SMUPs are extracted at pre-set decision deferment level by:

a. considering direct subtraction of sequential consolidated MUs waveforms (no alternation);

or

b. using a MUs waveform to subtract a base waveform (alternation); and

c. repeating through steps a) and b) until all the alternation possibilities are exhausted.

27. A method according to claim 26 wherein a base waveform is determined as a consolidated MU waveform based on an alternation consideration at the pre-set decision deferment level.

28. A method according to claim 27 wherein the total alternation cases at the pre-set decision deferment level are defined as (the power of 2 with an exponent that equals the pre-set decision deferment level)−

1.

29. A method according to claim 19 wherein SMUPs are optimized by:

a. scoring all the SMUP waveforms at least one of individually and in groups;

b. determining a group of SMUPs with the highest score as the true SMUPs; and

/or

c. allowing several alternative solutions to co-exist.

30. A method according to claim 29 wherein several alternative solutions represent the solutions with SMUP sets that meet the ranking criteria and have high scores.

31. A method according to claim 18 wherein identifying MU alternation directly comprises:

a. searching alternation equations to identify MUs alternation range and avoid direct subtraction of alternating MUs waveforms;

b. extracting potential SMUPs using a combination of the IS method and alternation equations;

c. validating potential SMUPs; and

d. identifying the SMUPs solution.

32. A method according to claim 31 wherein alternation equations are established by:

a. determining a base waveform, from a null signal then first to last consolidated MU waveforms;

b. subtracting the base waveform from all the consolidated MU waveforms at a higher level to obtain residual waveforms; and

c. using any combination of the residual waveforms to search an identical one in the same residual waveform group (i.e., any identified waveforms satisfying an equation, namely alternation equation).

33. A method according to claim 32 wherein an MU waveform at a higher level represents a consolidated MU waveform that partly or fully has larger features, wherein the features comprise at least one from the group consisting of:

power, areas, amplitude, and duration.

34. A method according to claim 32 wherein an identical waveform is measured using the similarity measurement as defined in claim 16.

35. A method according to claim 31 wherein SMUPs are extracted by:

a. identifying alternation equations, if any, until all possibilities are exhausted;

b. selecting smaller waveforms from alternation equations as potential SMUPs;

or

c. using direct subtraction of sequential MUs waveforms as potential SMUPs, beyond an alternation range in the consolidated MU waveform group.

36. A method according to claim 31 wherein SMUPs are validated by:

a. self-checking to identify any MU waveforms that do not satisfy a matching criteria compared with SMUPs'"'"' combinations; and

b. extracting new SMUPs from MU waveforms that do not satisfy the criteria.

37. A method according to claim 36 wherein self-checking uses SMUPs'"'"' combinations to compare with the consolidated MU waveforms, with the criteria being satisfied if a consolidated MU waveform is identical to one SMUPs'"'"' combination;

otherwise, new information (SMUPs) exists in the MU waveforms that do not meet criteria.

38. A method according to claim 36 wherein new SMUPs are extracted from the MU waveforms that do not meet criteria by:

a. using the aforementioned MU waveform to subtract lower level MU waveforms sequentially to obtain extra waveforms; and

b. using an obtained extra waveform for self-checking;

if this extra waveform with the combination of the existing potential SMUPs satisfies the criteria for two or more MU waveforms, it is added to the potential SMUP group.

39. A method according to claim 31 wherein a SMUPs solution is identified by:

a. identifying and removing identical waveforms or any composite SMUP waveforms from the potential SMUP group; and

b. selecting a set of SMUPs from the potential SMUP group, which has minimum number of SMUPs, but satisfies all the consolidated MUs waveform criteria.

40. A method according to claim 10 wherein representative SMUP features are determined to describe the SMUPs'"'"' characteristics, which include SMUPs'"'"' amplitude and/or areas.

41. A method according to claim 10 wherein computing approaches are determined to calculate the size of SMUPs features, which include methods of averaging, and/or weighting SMUPs'"'"' feature values.

42. A method according to claim 10 wherein MUN is estimated by using SMUP representative value divided into the same feature value of the maximal CMAP.

43. A method according to claim 10 wherein MUN statistics, including MUN mean value and standard deviation value, are calculated by using MUNE alternation solutions as defined in claim 30.

44. Apparatus for the assessment of neuromuscular function by the estimation of motor unit numbers, comprising:

stimulator apparatus for delivering electrical stimuli to a subject;

detector apparatus for acquiring the electrical signals from one or more motor units;

alternation elimination apparatus for automatically eliminating acquired electrical signals due to alternation; and

automatic estimation apparatus for automatically estimating motor unit number based on the remaining acquired signals.

45. Apparatus according to claim 44 wherein the alternation elimination apparatus utilizes a global search process.

46. Apparatus according to claim 44 wherein the alternation elimination apparatus utilizes a direct alternation identification process.

47. Apparatus according to claim 45 wherein the global search process comprises:

a. establishing a ranking criteria for quantitative evaluation of SMUP waveforms contained within the acquired electrical signals;

b. establishing a global search strategy to extract SMUPs from the acquired electrical signals; and

c. optimizing the extracted SMUPs to determine if a SMUP represents a newly fired motor unit or an alternation event.

48. Apparatus according to claim 47 wherein an SMUP ranking system is established based upon a consideration of features of at least one from the group consisting of:

SMUP responses, CMAP responses, response power, response onset, response end, response duration, response turn, response phase, response area, response amplitude, response peaks, and response peak-distance.

49. Apparatus according to claim 47 wherein the evaluation of a SMUP waveform is conducted in a group, wherein the group is defined as one possible combination set of SMUPs extracted from consolidated MU waveforms.

50. Apparatus according to claim 47 wherein a SMUP is ranked by:

a. calculating the offset of a SMUP waveform power over that of the average level of its group;

b. calculating the offset of a SMUP waveform onset over that of the CMAP;

c. calculating the offset of a SMUP waveform duration over that of the CMAP;

d. calculating the offset of a SMUP waveform maximum negative peak location over that of the CMAP;

e. calculating the offset of the waveform numbers of this SMUP group over the total number of consolidated MU waveforms; and

g. weighting, and then summing, all the aforementioned offsets so as to generate the SMUP waveform score.

51. Apparatus according to claim 47 wherein the global search strategy is established by:

a. determining a decision deferment level; and

b. extracting new SMUPs, considering consolidated MU waveforms with and without alternation events, using the pre-determined decision deferment level.

52. Apparatus according to claim 51 wherein the decision deferment level is determined as a number that represents the maximum number of alternating MUs, with overlapping thresholds.

53. Apparatus according to claim 52 wherein the decision deferment level is determined so as to be a value between (i) one, which represents step-by-step sequential manual processing, and (ii) the number of total consolidated waveforms, which considers all possible alternation cases in the acquired responses.

54. Apparatus according to claim 51 wherein new SMUPs are extracted at the pre-determined decision deferment level by:

a. considering direct subtraction of sequential consolidated MUs waveforms when there is no alternation; and

b. using a MUs waveform to subtract a base waveform when there is alternation; and

c. repeating steps a) and b) until all the alternation possibilities are exhausted.

55. Apparatus according to claim 54 wherein a base waveform is identified as a consolidated MU waveform based on an alternation consideration at the pre-determined decision deferment level.

56. Apparatus according to claim 55 wherein the total alternation cases at the pre-determined decision deferment level are defined as (the power of 2 with an exponent that equals the pre-determined decision deferment level)-1.

57. Apparatus according to claim 47 wherein SMUPs are optimized by:

a. ranking all the SMUP;

b. determining a group of SMUPs with the highest score as the true SMUPs; and

c. allowing several possible determinations to co-exist.

58. Apparatus according to claim 57 wherein several possible determinations represent the solutions with SMUP sets that meet the ranking criteria and have high scores.

59. Apparatus according to claim 46 wherein the direct alternation identification process comprises:

a. searching alternation equations to identify MU alternation range and to avoid direct subtraction of alternating MU waveforms;

b. extracting potential SMUPs using a combination of the IS method and alternation equations;

c. validating potential SMUPs; and

d. determining if a SMUP represents a newly fired motor unit or an alternation event.

60. Apparatus according to claim 59 wherein alternation equations are established by:

a. determining a base waveform, from an empty signal and then first to last consolidated MU waveforms;

b. subtracting the base waveform from all the consolidated MU waveforms at a higher level to obtain residual waveforms; and

c. using any combination of the residual waveforms to search an identical one in the same residual waveform group (i.e., any identified waveforms satisfying an alternation equation).

61. Apparatus according to claim 60 wherein an MU waveform at a higher level represents a consolidated MU waveform if it has a larger feature, wherein the feature comprises at least one from the group consisting of:

power, areas, amplitude, and duration.

62. Apparatus according to claim 60 wherein an identical waveform is measured using the similarity measurement, wherein the similarity measurement is based on at least one selected from the group consisting of:

Euclidian distance, correlation, mean absolute difference, and other comparable measure.

63. Apparatus according to claim 59 wherein SMUPs are extracted by:

a. identifying alternation equations, if any, until all possibilities are exhausted;

b. selecting smaller waveforms from alternation equations as potential SMUPs; and

c. using direct subtraction of sequential MU waveforms as potential SMUPs, beyond an alternation range in the consolidated MU waveform group.

64. Apparatus according to claim 59 wherein SMUPs are validated by:

a. self-checking to identify any MU waveforms that do not satisfy a matching criteria compared with SMUPs'"'"' combinations; and

b. extracting new SMUPs from MU waveforms that do not satisfy the criteria.

65. Apparatus according to claim 64 wherein self-checking uses SMUP combinations to compare with the consolidated MU waveforms, with the criteria being satisfied if a consolidated MU waveform is identical to one SMUP combination.

66. Apparatus according to claim 65 wherein new SMUPs are extracted from the MU waveforms that do not meet criteria by:

a. using the aforementioned MU waveform to subtract lower level MU waveforms sequentially to obtain extra waveforms; and

b. using an obtained extra waveform for self-checking—

if this extra waveform with the combination of the existing potential SMUPs satisfies the criteria for two or more MU waveforms, it is added to the potential SMUP group.

67. Apparatus according to claim 59 wherein a SMUPs solution is identified by:

a. identifying and removing identical waveforms or any composite SMUP waveforms from the potential SMUP group; and

b. selecting a set of SMUPs from the potential SMUP group, which has a minimum number of SMUPS, but satisfies all the consolidated MUs waveform criteria.

68. Apparatus according to claim 57:

wherein the alternation elimination apparatus operates by;

(i) pre-processing acquired responses so as to consolidate and enhance the acquired signals;

(ii) identifying plausible new motor unit activation events and corresponding response changes;

(iii) determining representatives of single motor unit potential features, and computing approaches; and

(iv) estimating motor unit number and its distribution statistics;

and further wherein representative SMUP features are determined to describe the SMUPs'"'"' characteristics, which include SMUPs'"'"' amplitude and/or areas.

69. Apparatus according to claim 57 wherein the alternation elimination apparatus operates by:

(i) pre-processing acquired responses so as to consolidate and enhance the acquired signals;

(ii) identifying plausible new motor unit activation events and corresponding response changes;

(iii) determining representatives of single motor unit potential features, and computing approaches; and

(iv) estimating motor unit number and its distribution statistics;

and further wherein computing approaches are determined to calculate the size of SMUPs features, which include methods of averaging, and/or weighting SMUPs'"'"' feature values.

70. Apparatus according to claim 57 wherein the alternation elimination apparatus operates by:

(i) pre-processing acquired responses so as to consolidate and enhance the acquired signals;

(ii) identifying plausible new motor unit activation events and corresponding response changes;

(iii) determining representatives of single motor unit potential features, and computing approaches; and

(iv) estimating motor unit number and its distribution statistics;

and further wherein MUN is estimated by using SMUP representative value divided into the same feature value of the maximal CMAP.

71. Apparatus according to claim 57 wherein the alternation elimination apparatus operates by:

(i) pre-processing acquired responses so as to consolidate and enhance the acquired signals;

(ii) identifying plausible new motor unit activation events and corresponding response changes;

(iii) determining representatives of single motor unit potential features, and computing approaches; and

(iv) estimating motor unit number and its distribution statistics;

and further wherein MUN statistics, including MUN mean value and standard deviation value, are calculated by using MUNE alternation solutions.