SYSTEM AND METHOD FOR PAIN MONITORING AT THE POINT-OF-CARE

• US 20110087125A1
• Filed: 10/09/2009
• Published: 04/14/2011
• Est. Priority Date: 10/09/2009
• Status: Abandoned Application

• Current Assignee: Brainscope SPV LLC
• Original Assignee: Brainscope SPV LLC
• Inventors: CAUSEVIC, Elvir
• Application Number:
  US12/576,521
• Publication Number:
  US 20110087125A1
• Time in Patent Office: Days
• US Class Current: 600/544
• 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 5/24 Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/372 Analysis of electroencephalograms
• CPC Class Codes: A61B 5/4824 Touch or pain perception evaluation A61B5/4821 takes precedence
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• 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
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method and apparatus for providing objective assessment of pain using a field portable device is described. The method includes placing an electrode set coupled to a handheld base unit on the subject'"'"'s head, acquiring brain and/or peripheral nervous system electrical signals from the subject through the electrode set, processing the acquired brain electrical signals using a feature extraction algorithm stored in a memory of the base unit, classifying the processed signals into pain categories, determining an objective quantification of the pain level, and indicating the pain category and/or pain scale on the handheld base unit. The memory of the base unit stores a reference database for classification of the processed signals, or the base unit is configured to wirelessly access the reference database from a remote data storage unit.

Claims

1. A method of assessing pain experienced by a subject comprising the steps of:

providing at least one electrode channel;

acquiring electrical signals from at least one of the brain and the peripheral nervous system of the subject using the at least one electrode channel;

providing at least one processor, the at least one processor performing the steps of;

extracting non-linear quantitative features from the acquired signals;

classifying the extracted features into one or more pain states; and

providing a quantitative value indicative of the level of pain perception.

2. The method of claim 1, wherein the step of classifying the extracted features is performed using a reference database stored in a memory device.

3. The method of claim 2, wherein the reference database comprises brain electrical activity data from a plurality of individuals in the presence or absence of pain.

4. The method of claim 3, wherein the reference database comprises the subject'"'"'s own brain electrical activity data in the presence or absence of pain.

5. The method of claim 1, wherein the non-linear features are combined with linear signal features.

6. The method of claim 1, wherein the quantitative value is used to generate an objective pain index.

7. The method of claim 1, further comprising the steps of:

amplifying the acquired brain electrical signals linearly or non-linearly;

digitizing the amplified brain electrical signals with the at least one processor; and

denoising the digitized signals.

8. The method of claim 1, further comprising the step of stimulating the subject with a stimulus generator to obtain evoked potentials.

9. The method of claim 1, wherein the electrical signals from the brain comprises spontaneous electrical activity.

10. The method of claim 1, wherein the electrical signals from the brain comprises spontaneous high-frequency electrical activity.

11. The method of claim 1, wherein the electrical signals from the brain comprises evoked potentials.

12. The method of claim 1, wherein the electrical signals from the brain comprises spontaneous electrical activity and evoked potentials.

13. The method of claim 1, wherein the step of feature extraction is performed using diffusion geometric analysis.

14. The method of claim 1, wherein the step of feature extraction is performed using wavelet packet transformation.

15. The method of claim 1, wherein the step of classifying the extracted features is performed using linear discriminant analysis.

16. The method of claim 1, wherein the step of classifying the extracted features is performed using a non-linear classifier.

17. The method of claim 1, wherein the step of classifying the extracted features is performed using voting strategy to combine multiple classifiers.

18. The method of claim 1, wherein the assessment of pain experienced by a subject is performed using a portable, handheld device.

19. The method of claim 18, wherein the device can be operated in a calibration mode to correlate brain electrical activity to stimulus levels and to subjective ratings reflecting individual pain experience based on either externally derived pain levels or by providing a pre-set pain stimulus.

20. The method of claim 18, wherein the device can be operated as a feedback controller for closed-loop administration of analgesic drugs to a subject.

21. The method of claim 18, wherein the device can be used to predict responsiveness to medication from a pre-medication baseline.

22. The method of claim 18, wherein the device can be used to predict medication for a particular type of pain and its severity.

23. A device for assessing pain experienced by a subject, comprising:

a headset comprising at least one electrode for acquiring brain electrical signals;

a base unit;

wherein

the base unit comprises a processor configured to utilize one or more operating instructions stored in a memory to perform non-linear feature extraction from the brain electrical signal and classification of the extracted signal features.

24. The device of claim 23, wherein the processor is configured to output an objective measurement of pain.

25. The device of claim 23, wherein the processor is configured to output a result indicating a pain category.

26. The device of claim 23, further comprising a display wherein a result of one or more operations performed by the processor is displayed.

27. The device of claim 23, wherein the display is operatively connected to the processor;

and

wherein the display can be integrated into the base unit, or can be external to the base unit.

28. The device of claim 25, wherein the base unit communicates wirelessly with an external display.

29. The device of claim 23, wherein the headset communicates wirelessly with the base unit.

30. The device of claim 23, wherein the headset comprises non-linear, adaptive electronic systems.

31. The device of claim 23, wherein the base unit comprises a stimulus generator to apply stimuli to the subject.

32. The device of claim 29, wherein the processor is configured to process spontaneous brain electrical signals and evoked potentials generated in response to the applied stimuli.

33. The device of claim 23, wherein the headset and the base unit are configured to reside on a single platform to be connected to the subject.

34. The device of claim 23, wherein the memory stores reference data for classification of the extracted signal features.

35. The device of claim 34, wherein the reference data is stored in an external data storage device.

36. The device of claim 35, wherein the data from the external storage device is accessed wirelessly by the processor.

37. The device of claim 23, wherein a result of one of more operations performed by the processor is stored in the memory.

38. The device of claim 37, wherein the result is stored in an external storage device.

39. The device of claim 23, wherein the brain electrical signals recorded from a subject are stored in an external storage device.

40. The device of claim 23, further comprising at least a second electrode to acquire electrical signals from the peripheral nervous system of the subject.