Devices and methods for the non-invasive detection of spontaneous myoelectrical activity

• US 20040225211A1
• Filed: 10/24/2002
• Published: 11/11/2004
• Est. Priority Date: 10/24/2001
• Status: Active Grant

• Current Assignee: Neurometrix, Inc.
• Original Assignee: Neurometrix, Inc.
• Inventors: Gozani, Shai N., Keller, Steven P.
• Granted Patent: US 7,493,157 B2
• Time in Patent Office: Days
• US Class Current: 600/382
• CPC Class Codes: A61B 5/30 Input circuits therefor
• CPC Class Codes: A61B 5/313 for electromyography [EMG]

Abstract

A method of detecting neuromuscular pathology in an individual is disclosed comprising the steps of placing a detector on the skin surface of the individual substantially adjacent to a skeletal muscle; obtaining signals from the detector; processing the signals; and determining neuromuscular status in response to the signals. A system for detecting neuromuscular disease in an individual is disclosed, the system comprising at least one means for recording a first signal from a skeletal muscle; a filter in communication with the recording means to generate a second signal consisting substantially of spontaneous myoelectrical activity; and a processor in communication with the filter, wherein the first signal from the skeletal muscle is filtered so as to generate spontaneous myoelectrical signals, and further wherein the processor calculates the power spectral density of the filtered signals and determines the neuromuscular status in response to the power spectral density of the filtered signals.

Claims

1. A method of detecting neuromuscular pathology in an individual comprising the steps of:

placing a detector on the skin surface of said individual substantially adjacent to a skeletal muscle;

obtaining signals from said detector;

processing said signals; and

determining neuromuscular status in response to said signals.

2. The method of claim 1 wherein said signals, comprise spontaneous myoelectrical activity.

3. The method of claim 1 wherein said signals comprise a bioelectrical signal.

4. The method of claim 3 wherein said bioelectrical signals comprise a biopotential signal.

5. The method of claim 1 wherein said processing step comprises removing external electrical interference signals from said recorded signals.

6. The method of claim 1 wherein said processing step comprises removing voluntary neurogenic muscle signals from said recorded signals.

7. The method of claim 1 wherein said processing step comprises removing at least one signal generated from at least one muscle other than said skeletal muscle from said recorded signals.

8. The method of claim 1 wherein said processing step comprises determining a power spectral density of said recorded bioelectrical signals.

9. The method of claim 8 wherein said power spectral density is determined over a particular frequency range.

10. The method of claim 9 wherein said range is between 100-500 Hz.

11. The method of claim 9 wherein said range is between 300-500 Hz.

12. The method of claim 1 wherein said determining step comprises comparing at least one characteristic of the processed signals against at least one reference value for each of said at least one characteristic.

13. The method of claim 12 further comprising deriving said at least one characteristic from a power spectral density measurement of the recorded signals.

14. The method of claim 12 wherein a difference in said at least one characteristic in the processed signals as compared to the at least one reference value is an indicator of neuromuscular pathology.

15. The method of claim 8 further comprising displaying the power spectral density of the recorded signals.

16. The method of claim 1 further comprising displaying a relative value quantifying the degree of neuromuscular pathology in response to the step of determining neuromuscular status.

17. The method of claim 1 wherein the neuromuscular disease is selected from the group of diseases consisting of radiculopathies, demyelinating neuropathies, intravertebral disc disease, and carpal tunnel syndrome.

18. The method of claim 1 further comprising comparing the processed signal of the individual at a first point in time to the processed signal of the individual at a second point in time to monitor the progression of the neuromuscular pathology.

19. The method of claim 1 wherein determining neuromuscular pathology further comprises comparing at least one characteristic of the processed signal of the individual with a reference value.

20. A method of detecting neuromuscular disease in an individual comprising the steps of:

measuring a first signal from a detector positioned substantially adjacent to a skeletal muscle;

removing noise to generate a second signal consisting substantially of spontaneous myoelectrical activity;

processing said second signal; and

determining the neuromuscular status in response to said processed second signal.

21. The method of claim 20 wherein said noise comprises voluntary neurogenic myoelectrical activity.

22. The method of claim 20 wherein a given difference in a power spectral density measurement of the second signal as compared to a reference value is indicative of neuromuscular disease.

23. The method of claim 20 further comprising displaying a power spectral density measurement of said second signal.

24. The method of claim 20 further comprising determining and displaying a relative value quantifying the degree of neuromuscular disease in response to the step of determining neuromuscular status.

25. A system for detecting neuromuscular disease in an individual, said system comprising:

at least one means for recording a first signal from a skeletal muscle;

a filter in communication with said recording means to generate a second signal consisting substantially of spontaneous myoelectrical activity; and

a processor in communication with said filter, wherein said first signal from said skeletal muscle is filtered so as to generate spontaneous myoelectrical signals, and further wherein said processor calculates the power spectral density of the filtered signals and determines the neuromuscular status in response to the power spectral density of the filtered signals.

26. A system for detecting neuromuscular disease in an individual, said system comprising:

at least one surface recording electrode for measuring a signal from a skeletal muscle;

a first filter for filtering said signal to generate a first set of filtered signals consisting substantially of spontaneous myoelectrical activity;

at least a second filter for removing noise from the first set of filtered signals so as to generate a second set of filtered signals; and

a processor for calculating the power spectral density of said second filtered signals and for determining the presence of disease in skeletal muscle or the presence of nerve innervating skeletal muscle in an individual in response to the power spectral density of said second filtered signals.

27. The system of claim 26 wherein the noise is in a first frequency range below about 0.1 Hz and in a a second frequency range above about 10,000 Hz.