System and method for pain detection and computation of a pain quantification index

1. A method for detecting pain in a subject, comprising the steps of:

inputting, using a processor, quantitative electroencephalogram (qEEG) data generated based on brain wave activity of the subject into a first classifier function A indicating a first probability score that pain is being sensed;

inputting, using the processor, the qEEG data into a second classifier function B indicating a second probability score that pain is not being sensed;

generating, using the processor, a probability P that the subject is experiencing pain as a function of the first and second probability scores;

comparing, using the processor, the probability P to a threshold level to determine a presence of pain when the probability P is at or above the threshold level, wherein the pain is not elicited by a sensory stimulus,

wherein the first classifier function A and the second classifier function B are independently generated from reference data comprising at least one of

(i) population normative qEEG data indicative of brain wave activity of a first plurality of individuals in an absence of pain,

(ii) population reference qEEG data indicative of brain wave activity of a second plurality of individuals generated in response to pain events inflicted on the second plurality of individuals,

(iii) self normative qEEG data indicative of brain wave activity of the subject in an absence of pain, and

(iv) subjective population reference qEEG data indicative of brain wave activity of a third plurality of individuals reporting a sensation of pain; and

generating, using the processor, a pain quantification index (PQI) value based on a comparison of the qEEG data to the reference data, the PQI corresponding to an intensity of pain experienced by the subject when the presence of pain is detected.

2. The method according to claim 1 , wherein the PQI includes values corresponding to one of a plurality of levels of pain and a plurality of types of pain, the types of pain include chronic and acute.

3. The method according to claim 1 , further comprising:

regulating an amount of analgesic administered to the subject by an apparatus as a function of the PQI.

4. The method according to claim 1 , further comprising:

directing, using the processor, a display to display the PQI, wherein the PQI corresponds to a clinical scale of pain comprising absent, sensation of discomfort, mild pain, moderate pain, severe pain and intolerable pain.

5. A device for detecting a pain in a subject, comprising:

a receiving arrangement configured to receive electrical signals from a plurality of electroencephalogram (EEG) electrodes;

a processor configured to generate quantitative EEG (qEEG) data based on brain wave activity of the subject detected by the EEG electrodes; and

a memory storing reference data comprising at least one of

(i) population normative qEEG data indicative of brain wave activity of a first plurality of individuals in an absence of pain,

(ii) population reference qEEG data indicative of brain wave activity of a second plurality of individuals generated in response to pain events inflicted on the individuals,

(iii) self normative qEEG data indicative of brain wave activity of the subject in an absence of pain, and

(iv) subjective population reference qEEG data indicative of brain wave activity of a third plurality of individuals reporting a sensation of pain; and

wherein the processor is coupled to the receiving arrangement and the memory, and configured to input the qEEG data into a first classifier function A indicating a first probability score that pain is being sensed and a second classifier function B indicating a second probability score that pain is not being sensed, the first classifier function A and the second classifier function B being independently generated by the processor from the reference data, to generate a probability P that the subject is experiencing pain as a function of the first and second probability scores, and to compare the probability P to a threshold level to determine a presence of pain when the probability P is at or above the threshold level,

wherein the pain is not elicited by a sensory stimulus, and

wherein the probability P equals:

e

A

(

1

-

e

B

)

.

6. The device according to claim 5 , wherein the receiving arrangement is one of a cable connector and a wireless signal receiver.

7. The device according to claim 5 , wherein the receiving arrangement comprises at least one of an amplifier, an analog-to-digital converter, and a filtering arrangement.

8. A method for detecting pain in a subject, comprising the steps of:

inputting, using a processor, quantitative electroencephalogram (qEEG) data generated based on brain wave activity of the subject into a first classifier function A indicating a first probability score that pain is being sensed;

inputting, using the processor, the qEEG data into a second classifier function B indicating a second probability score that pain is not being sensed;

generating, using the processor, a probability P that the subject is experiencing pain as a function of the first and second probability scores;

comparing, using the processor, the probability P to a threshold level to determine a presence of pain when the probability P is at or above the threshold level, wherein the pain is not elicited by a sensory stimulus, wherein the first classifier function A and the second classifier function B are independently generated from reference data comprising at least one of

(i) population normative qEEG data indicative of brain wave activity of a first plurality of individuals in an absence of pain,

(ii) population reference qEEG data indicative of brain wave activity of a second plurality of individuals generated in response to pain events inflicted on the second plurality of individuals,

(iii) self normative qEEG data indicative of brain wave activity of the subject in an absence of pain, and

(iv) subjective population reference qEEG data indicative of brain wave activity of a third plurality of individuals reporting a sensation of pain; and

determining, using the processor, one of a predicted intensity and a predicted type of pain by comparing the qEEG data to the reference data using a logistic regression, when the presence of pain is detected.

9. The method according to claim 8 , wherein the first and second classifier functions, A and B, respectively, are discriminant functions.

10. The method according to claim 8 , wherein the threshold level is at least 0.95.

11. The method according to claim 8 , wherein the threshold level is at least 0.99.

12. The method according to claim 8 , wherein at least one of the first classifier function A and the second classifier function B is generated by the processor using a neural network.

13. The method according to claim 8 , wherein the reference data further comprises at least one of medical histories, physical examination results and neurological examination results.

14. The method according to claim 8 , wherein the qEEG data is generated from the brain wave activity of the subject detected by a plurality of EEG electrodes removably connected to at least one of a scalp and a forehead of the subject.

15. The method according to claim 14 , wherein the plurality of EEG electrodes detect a series of voltage oscillations corresponding to the brain wave activity of the subject.

16. The method according to claim 8 , wherein the subject is human.

17. The method according to claim 8 , wherein the subject is one of a cat, dog, horse and nonhuman primate.

18. The method according to claim 8 , further comprising:

generating, using the processor, an output signal directing an adjustment to delivery of an analgesic or an adjustment to treatment via a neurofeedback paradigm, when the presence of pain is detected.

19. A method for detecting pain in a subject, comprising the steps of:

inputting, using a processor, quantitative electroencephalogram (qEEG) data generated based on brain wave activity of the subject into a first classifier function A indicating a first probability score that pain is being sensed;

inputting, using the processor, the qEEG data into a second classifier function B indicating a second probability score that pain is not being sensed; and

generating, using the processor, a probability P that the subject is experiencing pain as a function of the first and second probability scores,

comparing, using the processor, the probability P to a threshold level to determine a presence of pain when the probability P is at or above the threshold level, wherein the pain is not elicited by a sensory stimulus,

wherein the first classifier function A and the second classifier function B are independently generated from reference data comprising at least one of

(i) population normative qEEG data indicative of brain wave activity of a first plurality of individuals in an absence of pain,

(ii) population reference qEEG data indicative of brain wave activity of a second plurality of individuals generated in response to pain events inflicted on the second plurality of individuals,

(iii) self normative qEEG data indicative of brain wave activity of the subject in an absence of pain, and

(iv) subjective population reference qEEG data indicative of brain wave activity of a third plurality of individuals reporting a sensation of pain, and

wherein the probability P equals:

e

A

(

1

-

e

B

)

.

20. A device for detecting a pain in a subject, comprising:

a receiving arrangement configured to receive electrical signals from a plurality of electroencephalogram (EEG) electrodes;

a processor configured to generate quantitative EEG (qEEG) data based on brain wave activity of the subject detected by the EEG electrodes;

a memory storing reference data comprising at least one of

(i) population normative qEEG data indicative of brain wave activity of a first plurality of individuals in an absence of pain,

(ii) population reference qEEG data indicative of brain wave activity of a second plurality of individuals generated in response to pain events inflicted on the individuals,

(iii) self normative qEEG data indicative of brain wave activity of the subject in an absence of pain, and

(iv) subjective population reference qEEG data indicative of brain wave activity of a third plurality of individuals reporting a sensation of pain,

wherein the processor is coupled to the receiving arrangement and the memory, and configured to input the qEEG data into a first classifier function A indicating a first probability score that pain is being sensed and a second classifier function B indicating a second probability score that pain is not being sensed, the first classifier function A and the second classifier function B being independently generated by the processor from the reference data, to generate a probability P that the subject is experiencing pain as a function of the first and second probability scores, and to compare the probability P to a threshold level to determine a presence of pain when the probability P is at or above the threshold level,

wherein the pain is not elicited by a sensory stimulus; and

a display,

wherein the processor is further configured to generate a pain quantification index (PQI) value based on a comparison of the qEEG data to the reference data, the PQI corresponding to an intensity of pain experienced by the subject when the presence of pain is detected, and

wherein the display is configured to display the PQI, the PQI corresponding to a clinical scale of pain comprising absent, sensation of discomfort, mild pain, moderate pain, severe pain and intolerable pain.

21. The device according to claim 20 , wherein the PQI includes values corresponding to one of a plurality of levels of pain and a plurality oft, pes of pain, the types of pain include chronic and acute.