METHOD AND APPARATUS FOR DETECTING SEIZURES

• US 20120108999A1
• Filed: 10/17/2011
• Published: 05/03/2012
• Est. Priority Date: 10/15/2010
• Status: Active Grant

• Current Assignee: Novela Neurotechnologies, Inc.
• Original Assignee: Brain Sentinel, Inc.
• Inventors: Leininger, James R., Herring, Russell M., Girouard, Michael R., Cavazos, Jose E.
• Granted Patent: US 8,983,591 B2
• Time in Patent Office: Days
• US Class Current: 600/546
• CPC Class Codes: A61B 2505/07 Home care
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/25 Bioelectric electrodes therefor
• CPC Class Codes: A61B 5/296 for electromyography [EMG]
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/6804 Garments; Clothes
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/6833 Adhesive patches
• CPC Class Codes: A61B 5/6844 Monitoring or controlling distance between sensor and tissue
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence

Abstract

A method of detecting seizures may comprise receiving an EMG signal and processing the received EMG signal to determine whether a seizure characteristic is present in the EMG signal during a time window. An apparatus for detecting seizures with motor manifestations may comprise one or more EMG electrodes capable of providing an EMG signal substantially representing seizure-related muscle activity; and a processor configured to receive the EMG signal, process the EMG signal to determine whether a seizure may be occurring, and generate an alert if a seizure is determined to be occurring based on the EMG signal.

Claims

1. An apparatus for detecting seizures with motor manifestations, the apparatus comprising:

one or more EMG electrodes capable of providing an EMG signal substantially representing seizure-related muscle activity; and

a processor configured to receive the EMG signal, process the EMG signal to determine whether a seizure may be occurring, and generate an alert if a seizure is determined to be occurring based on the EMG signal.

2. The apparatus of claim 1 further comprising one or more of an ECG electrode, a temperature sensor or an accelerometer.

3. The apparatus of claim 1, wherein the one or more EMG electrodes are mounted to one or more of an arm band, adhesive tape, or item of clothing so as to allow positioning of the one or more EMG electrodes over a muscle.

4. The apparatus of claim 1, wherein the one or more EMG electrodes are differential bipolar electrodes.

5. The apparatus of claim 1 comprising two EMG electrodes capable of being associated with an agonist/antagonist muscle pair, wherein one EMG electrode is associated with an agonist muscle, and the other EMG electrode is associated with its antagonist muscle.

6. The apparatus of claim 5 wherein the agonist/antagonist muscle pair comprises the triceps brachii and biceps brachii.

7. The apparatus of claim 1, further comprising a transceiver for transmitting the alert.

8. The apparatus of claim 1, further comprising a base station in communication with the processor for receiving the alert.

9. The apparatus of claim 8, wherein the base station further comprises an I/O device capable of allowing manual adjustment of alert settings and visually displaying the EMG signal or data based thereon.

10. The apparatus of claim 1, wherein the processor is capable of processing the EMG signal to determine whether a seizure may be occurring by detecting the existence of two or more bursts.

11. The apparatus of claim 10, wherein the processor is capable of processing the EMG signal to determine whether a seizure may be occurring by detecting the periodicity of the two or more bursts.

12. The apparatus of claim 10, wherein the processor is capable of processing the EMG signal to determine whether a seizure may be occurring by detecting the regularity of the two or more bursts.

13. The apparatus of claim 1, wherein the processor is capable of processing the EMG signal to determine whether a seizure may be occurring by detecting the existence of a GTC waveform.

14. The apparatus of claim 13, wherein the GTC waveform comprises a frequency-domain bump between approximately 300 Hz and approximately 500 Hz.

15. The apparatus of claim 13, wherein the GTC waveform comprises a frequency-domain bump between approximately 350 Hz and approximately 450 Hz.

16. The apparatus of claim 1 wherein the processor is configured to send the alert to one or more remote receivers.

17. The apparatus of claim 16 wherein one of the remote receivers is a cellular telephone.

18. The apparatus of claim 1, further comprising a remote data storage unit capable of receiving and storing the EMG signal or data based thereon.

19. The apparatus of claim 1, further comprising one or more leads-off detectors configured to indicate whether one or more of the EMG electrodes is sufficiently close to a muscle to provide a substantially accurate EMG signal representing activity of the muscle.

20. The apparatus of claim 1, wherein the one or more EMG electrodes and processor are packaged as a single unit mountable to a human body.

21. The apparatus of claim 20, wherein the single unit is minimally intrusive while sleeping and includes no external wires.

22. The apparatus of claim 1 wherein the processor is configured to send a signal to a base station from which the base station may derive the physical location of the processor.

23. The apparatus of claim 1 further comprising an environmental transceiver associated with one or more environmental objects.

24. The apparatus of claim 23 wherein the environmental transceiver is configured to send a signal to a base station that is dependent upon the relative position of the processor and the environmental transceiver.

25. The apparatus of claim 24 wherein the base station comprises a processor capable of processing signals from the environmental transceiver, selecting a template from at least two template files available to the processor, and using the selected template file in processing the EMG signal.

26. The apparatus of claim 25 wherein the environmental object one of a crib, bed, chair, toilet, door, wall, toothbrush and an item of clothing.

27. The apparatus of claim 1, wherein the one or more electrodes are physically separate from and in wireless communication with the processor.

28. The apparatus of claim 1, further comprising a base station in communication with the processor, and wherein the one or more EMG electrodes and processor are packaged as a single unit mountable to a human body and physically separate from the base station.

29. The apparatus of claim 28, wherein the base station is capable of receiving the alert and the EMG signal, independently processing the EMG signal to determine whether a seizure may be occurring, and generate an alert if a seizure is determined to be occurring based on the EMG signal.

30. The apparatus of claim 29, wherein the base station is only capable of generating an alert if both the processor and base station determine that a seizure is occurring.

31. The apparatus of claim 8, wherein a user may manually adjust the alert settings by changing seizure characteristic thresholds of a template file available to the processor for processing the EMG signal.

32. The apparatus of claim 31, wherein the manual adjustment comprises changing the seizure characteristic thresholds to reduce the occurrence of false positive identification of seizures.

33. The apparatus of claim 31, wherein the manual adjustment comprises changing the seizure characteristic thresholds to increase the accuracy of seizures detection.

34. The apparatus of claim 8, wherein the base station is capable of automatically adjusting the alert settings by changing seizure characteristic thresholds of a template file available to the processor for processing the EMG signal.

35. The apparatus of claim 33, wherein the automatic adjustment is based on the average deviation of a seizure characteristic from a threshold.

36. The apparatus of claim 8, wherein the base station is one of a desktop computer, a laptop computer or a smartphone.

37. The apparatus of claim 1, further comprising an I/O device to allow a patient to indicate to the processor that a seizure is not occurring.

38. The apparatus of claim 37, wherein the I/O device comprises two buttons.

42. The method of claim 40, the method further comprising:

repeating the method steps of claim 1 for a second time window; and

if a second value is stored as a result of the repeating step, then summing the first value and second value;

comparing the value sum to an alarm threshold; and

if the value sum is equal to or greater than the alarm threshold, then sending an alert.

43. The method of claim 42, the method further comprising, before the summing step:

multiplying the first value by a first weighting coefficient; and

multiplying the second value by a second weighting coefficient.

44. The method of claim 43, wherein the first and second weighting coefficients are included in a template file.

45. The method of claim 42 further comprising comparing the first value to the second value to determine how the characteristic has changed from the first time window to the second time window.

39. A method of detecting seizures, the method comprising:

receiving an EMG signal; and

processing the received EMG signal to determine whether a seizure characteristic is present in the EMG signal during a time window.

40. The method of claim 39, the method further comprising, if a seizure characteristic is present, then:

determining at a first value for the characteristic;

comparing the first value to a maximum threshold and a minimum threshold; and

if the value is equal to either the maximum or minimum thresholds, or between the thresholds, then storing the first value.

41. The method of claim 40, wherein the characteristic comprises one of burst amplitude, burst width, burst count, burst periodicity, burst regularity and GTC waveform.

46. The method of claim 41, wherein the characteristic is burst amplitude and the first value for the characteristic is a S/N.

47. The method of claim 41, wherein the characteristic is burst width and the first value for the characteristic is a time difference between a positive change in S/N and a negative change in S/N.

48. The method of claim 41, wherein the characteristic is burst count and the first value for the characteristic is a value equal to the number of bursts within a time window.

49. The method of claim 41 wherein the characteristic is burst periodicity of bursts, the first value for the characteristic represents whether the periodicity is between a minimum uniformity threshold and a maximum uniformity threshold.

50. The method of claim 41 wherein the characteristic is burst regularity, and the first value for the characteristic represents the deviation between a determined burst and a model burst.

51. The method of claim 41, wherein the characteristic is a GTC waveform and the first value for the characteristic is a slump to bump ratio of frequencies.

52. The method of claim 51, wherein the slump to bump ratio of frequencies is calculated between approximately 300 Hz and approximately 500 Hz.

53. The method of claim 51, wherein the slump to bump ratio of frequencies is calculated between approximately 350 Hz and approximately 450 Hz.

54. A method of seizure detection comprising:

receiving EMG output data;

processing the received EMG output data;

executing a first calculation on the processed output data to determine whether a first characteristic of the output data is met during a time window;

executing a second calculation on the processed output data to determine whether a second characteristic of the output data is met during a time window;

wherein each of the first calculation and the second calculation are repeated in successive time windows;

transferring a value to a first accumulation register based on calculations in successive time windows for the first characteristic;

transferring a value to a second accumulation register based on calculations in successive time windows for the second characteristic;

wherein each of the first accumulation register and the second accumulation are adjusted at a predetermined rate between successive time windows; and

executing a supervisory algorithm;

wherein the supervisory algorithm analyzes the content of both the first accumulation register and the second accumulation register to determine the likelihood that a seizure may be occurring.

55. The method of claim 54 wherein the supervisory multiples the content of the first accumulation detection register by a first coefficient that weights the value stored in the first accumulation detection register in detecting a seizure.

56. The method of claim 55 wherein the first coefficient is included in a template file.

57. The method of claim 56 wherein the template file is capable of being adaptively updated to account for patient-specific data.

58. The method of claim 57 wherein adaptation of the template file is based upon data collected while a patient is wearing the one or more EMG electrodes.

59. The method of claim 58, wherein adaptation of the template file is based upon data collected while a patient is wearing the one or more EMG electrodes while in a home setting.

60. The method of claim 54 further comprising collecting in a data archive at least a portion of EMG data from the one or more EMG electrodes.

61. The method of claim 60 further comprising temporally linking the archived data and the data representing the physical status of a patient.

62. The method of claim 61, wherein the data representing the physical status of the patient includes whether the patient was having a seizure.

63. The method of claim 54 further comprising, if the supervisory algorithm determines that a seizure is likely occurring, then alerting the patient of the likely seizure detection.

64. The method of claim 63 further comprising providing the patient an option of reporting that a seizure is not occurring.

65. The method of claim 64, wherein the reporting is achieved by the patient interacting with an I/O device in communication with a processor that processed the received EMG output data.

66. The method of claim 65, wherein the I/O device comprises two buttons.

67. The method of claim 54 further comprising:

storing at least a portion of the collected EMG output data;

receiving data representing the physical status of the patient during periods where an alarm was predicted; and

using the stored output data and the physical status data of the patient to modify the method.

68. The method of claim 67, wherein modifying the method involves adjustment of coefficients included in a template file.

69. The method of claim 68, wherein the template file is stored in memory controlled by a processor that processed the received EMG output data.

70. The method of claim 69 wherein the template file includes a threshold value that is associated with executing the first calculation.

71. The method of claim 69 wherein the template file is adjusted based on varying a threshold value listed in the template file and analyzing detection metrics of the method.

72. The method of claim 71 wherein the detection metrics of the method comprise the number of seizure events that were correctly identified and the number of false positives.

73. The method of claim 69 wherein the template file includes a coefficient that the supervisory algorithm used to adjust the weight of a value stored in either the first accumulation register or the second accumulation register.

74. The method of claim 73 wherein the template file is adjusted based on varying the coefficient listed in the template file and analyzing detection metrics of the method.

75. The method of claim 60 wherein the data stored in the data archive comprises raw EMG data.

76. The method of claim 60 wherein the data stored in the data archive comprises a model form or envelopes of signal bursts after filtering, rectification and peak detection.

77. The method of claim 54 further comprising storing a portion of the collected EMG detector output and performing an analysis of the stored data based on a plurality of template files.

78. The method of claim 77 wherein following execution of the analysis based on the plurality of template files the result of analysis with the plurality of template files is stored.