WO2014091291A1

WO2014091291A1 - A device and method for determining the probability of response to pain and nociception of a subject t

Info

Publication number: WO2014091291A1
Application number: PCT/IB2013/002735
Authority: WO
Inventors: Mathieu JOSPIN; Jose Fernando VALENCIA
Original assignee: MARUGG, James
Priority date: 2012-12-11
Filing date: 2013-12-10
Publication date: 2014-06-19

Abstract

The present invention provides a method for determining the probability of response to pain and nociception (final qNOX) of a subject during different levels of arousal, comprising steps of: (a) receiving electroencephalography (EEG) data and electromyography (EMG) data; (b) defining an index of consciousness (qCON) as a function of the EEG data; (c) defining an index of nociception (initial qNOX) as a function of the EEG data and the EMG data; and, (d) defining a weighing factor alpha a as a function of qCON; wherein, if the initial qNOX > qCON and qCON < k₁ a is defined by the following formula:α = k₂ - k₄ * (qCON - k₃); where k₁, k₂, k₃ and k₄ are predetermined values; if α > k₂, α is defined by the following formula α = k₂; further wherein a final qNOX is defined by the following formula: final qNOX = (1 - α) * initial qNOX + α* qCON.

Description

A DEVICE AND METHOD FOR DETERMINING THE PROBABILITY OF RESPONSE TO PAIN AND NOCICEPTION OF A SUBJECT

FIELD OF THE INVENTION

The present invention generally relates to a device and method for determining the probability of response to pain and nociception of a subject. More particularly, the probability of response to pain and nociception from a nociception and consciousness index.

BACKGROUND OF THE INVENTION

Anesthesia has been defined as a drug induced process of consciousness loss, including sensation of pain and any other stimuli. Furthermore, the patient may be paralyzed. During anesthesia, although the patient does not perceive stimuli, the neurovegetative and somatic responses are not necessarily abolished. However, when administering enough doses of analgesics, the nociceptive stimuli are blocked and the neurovegetative and somatic responses are prevented. This process allows a patient to undergo surgery and other procedures without the distress and pain they would have otherwise experienced.

Anesthesia is a dynamic equilibrium process, where the effects of the anesthetic drugs (mostly hypnotics and analgesics) given to the patient by an anesthesiologist are counteracted by the intensity of the different stimuli he is exposed to. When this equilibrium is broken, the patient could evolve to different anesthetic depths. One of the objectives of modern anesthesia is to ensure adequate level of consciousness to prevent awareness without inadvertently overloading the patients with anesthetics, which might cause increased postoperative complications.

In order to prevent overloading patients with anesthetics as well as to prevent intraoperative awareness the anesthesiologist apply subjective and nonspecific clinical indicators to assess the level of depth of anesthesia. Over the recent years, also some automatic devices appeared on the market to provide an objective measure of the level of consciousness of the patient. Several clinical scales for assessing the level of consciousness exist. For example, a commonly used scale is the Observers Assessment of Alertness and Sedation Scale (OAAS). The main disadvantage of using these scales in the operating room is that they v^aiiiiui us ^uiitiii-uutfaty uocu aiiu tiiat uicv ic uui ciaumc IU jjdi tiii.

The disadvantages of the clinical scales has led to the investigation into automated assessment of the level of consciousness. The most prevailing method is EEG analysis where a scalp EEG is recorded and subsequently processed by an algorithm which maps

±u„ ττυη i- _ffl,„,„,„i,. — .„„t TTC CITTI /i n „ method and an apparatus for providing a measure of the depth of anesthesia based on analyzing beat-to-beat heart rate together with respiration, which does not provide results that could be considered as an objective measure for the level of analgesia of a

>~*_™+ J _™„„-t T TC ΠΛ;1 ;Π „ _™„*1,„ J -f„„ „„!„.. i„+„_f. J„„* <m UCllll l anesthesia as a function of the fractal dimension of a series of time intervals between successive waves of cardiac activity of a patient. This disclosed method is mathematically based on computing correlation dimension for beat-to-beat heart rate τι, .— — 11- . A. „ —__Λι„*; „ J : ;— „ _T .— .. i— „ ~ i UCUiCUmUJi, UiC WiCi<KiUE MiliiCiiSIAJiA «?4 VCi sctjucu c, which leads to large delays in real-time monitoring.

Nociception and the perception of pain define the need for analgesia for obtaining pain relief. The state of analgesia for surgery is reached by the administration of analgesics. The demand of analgesics is individual for each patient, therefore, there is a need for continuous, preferably non-invasive, monitoring of the analgesia of the patient. Autonomic responses such as tachycardia, hypertension, emotional sweating and lacrimation, although non-specific, are regarded as signs of nociception and consequently inadequate analgesia.

Several methods for monitoring nociception were previously recited. For example, a monitoring method by skin conductance has been claimed in patent document US 6571124, however, this metho is p^dfiedV^'- r^'-ase 'solely with neonates and does not take a multi -parameter approach. In another example, patent document US 7024234 recites an algorithm that analyzes a photoplethysmographic signal for the detection of the autonomic nervous system activity during sleep related breathing disorders. In yet another example, patent document US 2005143665 recites a method for assessing the level of nociception during anesthesia by plethysmography from which a number of parameters are derived which are used to design a final index using a multiple logistic regression approach. In yet another example, patent document 6685649 recites a method for detection of nociception by analysis of RR intervals achieved either from ECG data or blood pressure data. From the RR intervals the acceleration emphasized RR interval t~. i T« —i„„„+ „~ »„«~.„ι_Λ »„.™₀„4 T onan nm/iT T fv „ .„„ „ is> c ij i icu. In vet (U iuww κΑΛΐι.ψις;, rttciiL uO uu it Ut) i ct,i«¾ a iticu iOu for monitoring the nociception of a patient during general anesthesia by extracting RR intervals from ECG and blood pressure. The method is based on detection of simultaneous increase in HR and BP, defined as a non-baroreflex. However, the

UKSVjiiu&j- iiiciiiuu u iiui auiv cu ucicL-i vvnc ici utc iiuem Jia* yccn uvci u scu vv i ii i analgesia but rather only detect whether the patient responds or not to painful stimuli with a positive predictive value of 30 %. In yet another example, patent document EP 1495715 recites a method for measuring an index of hypnosis as well as index of i * — . t. *

i i i .» caw i umei .

In all the examples only specific measures either for level or analgesia or level of consciousness are recited. However, it is well known that these two measures are affected by each other.

Consequently, there is a long felt and unmet need for a new method and device for determining the probability of response to pain and nociception in reliance to the level of consciousness of the subject. The method should be simple and possible to implement and follow after and during procedures requiring sedation and anesthesia.

SUMMARY

The present invention provides a method for determining the probability of response to pain and nociception (qCON) of a subject during different levels of arousal, comprising steps of: (a) receivin electroencephalography (EEG data and electromyograph (EMG) data; (b) defining an index of consciousness (qCON) as a function of the EEG data; (c) defining an index of nociception (initial qNOX) as a function of the EEG data and the EMG data; and, (d) defining a weighing factor alpha a as a function of qCON; wherein, if the initial qNOX > qCON and qCON < k_l3 a is defined by the following formula:a = k₂ - k * (qCON - k ); where kj, k₂, k₃ and k are predetermined values; if a > k₂, a is defined by the following formula a = k₂; further wherein a final qNOX is It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of determining the values of the qCON, the initial qNOX and the final qNOX in the range of about 0 - about 100.

it IS nOiftci uujc i ui uic ouiiciu. mvcmiuii uiaviu&c uic uiciuvu s uciiTicu in airy w the above, additionally comprising a step determining the predetermined values k_\ and k₃ in the range of about 25 - about 60; and, the predetermined values k₂ and It* in the range of about 0 - about 1.

It is another object of the current invention to disclose the method as deimed in any of the above, additionally comprising a step of extracting EEG frequency ratios (EEG-FR), burst-suppressions (BS), EEG time domain parameters (EEG-TDP) and any combination thereof, from the EEG data.

it is auOuici uujt i ui i>uncut uivcuwu iu uis>^iu¾c mo iiiciuuu a utmit ui cuy* vi the above, wherein the step of extracting the EEG-FR is performed by the applying a fast Fourier transform (FFT) on the EEG data.

It is another object of the current invention to disclose the method as defined in any of iiic aOOVe, auuiuuii uv comprising a ste ui CAUtt Uiig i-uvivj

FR) from the EMG data.

It is another object of the current invention to disclose the method as defined in any of the above, wherein the step of extracting the EMG-FR is performed by applying a fast i uuiici n uiisi i i ii i ±' l ) υιι me Quia.

It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of calculating the EEG-FR and the EMG-FR from at least one range of frequencies of about 0- about 100 Hz.

It is another object of the current invention to disclose the method as deimed in any of the above, additionally comprising a step of deriving the qCON from at least one parameter selected from a group consisting of the EEG-FR, the BS, the EEG-TDP, and any combination thereof.

it IS aiiutuci uuje t ui uio cuiieni uiv cuuuu iu uia iuac uic iiiciuuu aa ufcuiicu iu iiy ui the above, additionally comprising a step of deriving the initial qNOX from a function of at least one parameter selected from the EEG-FR, the EEG-TDP, the EMG-FR and any combination thereof.

it iS ojiuulci u ect ui ujc vui Jli iii vciiu ii iu ia i i>c ujc liio u αώ uciiJicG iu caiy ui the above, additionally comprising a step of receiving electrocardiography (ECG) data; It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of selecting the ECG data from a group consisting of raw ECG data, RR intervals data and any combination thereof.

la tiiiuu iti Oujc i ui mc ^

uii iii liivciuiuu uisciuac iiic lii&iii u a ucimcu iu aiiv ui the above, additionally comprising a step of extracting heart rate variability (HRV) from the ECG data.

It is another object of the current invention to disclose the method as defined in any of uiG auu vc, vvii J Oiii uic sicy ui CAu Uiig ui uic v i¾ pci iuiJiie uv uic

f i fast Fourier transform (FFT) on the ECG data.

It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of defining the qCON as a function of the uaia.

It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of defining the initial qNOX as a function of the ECG data.

It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of applying to the HRV data and the EEG-FR at least one mathematical manipulation selected from a group consisting of mutual information analysis, cross correlation, Fokker-Planck drift, diffusion coefficients and any combination thereof.

It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of defining the qCON by applying at least one mathematical manipulation on data selected from a group consisting of the EEG data,

UiC

uici cOi, uy niv lis ui at icaai uc iassiiici .

It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of defining the by applying at least one mathematical manipulation on data selected from a group consisting of the EEG data, ui t ivi j uaia, uic

inci i, u)¹ uit iis ui ai le st un6 classifier.

It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of selecting a mathematical manipulation from a gi uup uiisiiu ig ui iiiieai i cgi csajuij, iugiau i cgi c¾aiuij, luii) i

vgii^ iaa-vinci , Jic iai network, hybrid between a fuzzy logic system and a neural network such, Adaptive Neuro Fuzzy Inference System (ANFIS) and any combination thereof. It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of selecting the level of arousal of the subject from a group consisting of awake, under sedation, under general anesthesia and any euiuuiiiauuii + IiUei„C„U„.lr.

It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of activating an alarm if the final qNOX is above a predetermined value.

11 is aiiu uoi uujoei ul uic eui i Giit uiveiiiluii tu uiaeiuac uic meuluu s ueiincu. m auv υι the above, additionally comprising a step of displaying the final qNOX.

It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of obtaining EEG input means for receiving uic i_ i_ j uaia, i_<i- Vi input means eumpi ises cic uuucs pusi uucu uii me

scalp.

It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of positioning the electrodes at middle f J -Ci f A A ^„ t e A

lUl CilCttU, iCil lUlCliCUU OliU l iglil lu cncau.

It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step of obtaining EMG input means for receiving the EMG data; the EMG input means comprises electrodes.

it IS ttllUlliCi UUjc l Ul me eui i Clil lilVCllllUll lU UlSeiUSC UIC IliCU UU. U Hi iill) Ul the above, additionally comprising a step positioning the electrodes on the subject's scalp.

It is another object of the current invention to disclose the method as defined in any of uic auuv c, auuiuujjai \

*υιιιμι ΐδΐιΐ£ a stop ui atiaenui

the ECG data.

It is another object of the current invention to disclose the method as defined in any of the above, additionally comprising a step selecting the ECG input means from a group euusiauiig ui J-ioau , j-ic , ΐ -,-itau

aiiu iij Cuiuuiiiatiuii uioicui.

It is another object of the current invention to disclose a device for determining the probability of response to pain and nociception (final qNOX) of a subject during different levels of arousal, comprising at least one processor and at least one computer readable iuCuiGiy CGUplcd tu the prOCcSSOf, at loSSt One COiilpUief readable illodiUiil comprises operations executed by at least one processor, the operations are: (a) receiving electroencephalography (EEG) data and electromyography (EMG) data; (b) defining an index of consciousness (qCON) as a function of the EEG data; (c) defining an index of nociception (initial qNOX) as a function of the EEG data and the EMG data; and, (d) defining a weighing factor alpha a as a function of qCON; wherein, if the

—

* (qCON - k₃); where k_l3 k₂, k₃ and 1¾ are predetermined values; if a > k₂, a is defined by the following formula: a = k₂; further wherein a final qNOX is defined by the following formula: final qNOX = (1 - o) * initial qNOX + a * qCON.

It is another object oi the current invention to disclose the device as defined in any oi the above, wherein the qCON, the initial qNOX and the final qNOX has a value in the range of about 0 - about 100.

It is another object of the current invention to disclose the device as defined in any of uic auuvt, wuciciii uic picuciciiiuueu vaiuts ] n K₃ aie m iuc ituigc ui auOui - about 60; and, the predetermined values k₂ and k4 are in the range of about 0 - about 1. It is another object of the current invention to disclose the device as defined in any of the above, wherein EEG frequency ratios (EEG-FR), burst-suppressions (BS), EEG luut; uuiutim ptu ciiucioi a i Lrr ) anu any uuiUiuuuuu t cicui cu e iiuiii the EEG data.

It is another object of the current invention to disclose the device as defined in any of the above, wherein the extraction of the EEG-FR is performed by the application of fast UUliei

Ucll i.

It is another object of the current invention to disclose the device as defined in any of the above, wherein EMG frequency ratios (EMG-FR) are extracted from the EMG data. It is another object of the current invention to disclose the device as defined in any of uic αυυνί, wuci iii ui6 CAu amuu Oi uic 13 cilGjiiicu uy uuc cujpii uuuii ui

Fast Fourier transform (FFT) on the EMG data.

It is another object of the current invention to disclose the device as defined in any of the above, wherein the EEG-FR and the EMG-FR are calculated from at least one range υ „fι f i.ioqu iici ¾ u„i„ αiυυιιι υ-„ aiuuu *i i nn ττ„

It is another object of the current invention to disclose the device as defined in any of the above, wherein the qCON is derived from at least one parameter selected from a group consisting of the EEG-FR, the BS, the EEG-TDP, and any combination thereof.

J.I id auvjUlCl UUJC l Ul UIC CUi i Ciil UiV CiJUim IU UiSviUOC Ui6 UC Vi C US UCJ iiiC iii Oii^ i the above, wherein the initial qNOX is derived from a function of at least one parameter selected from the EEG-FR, the EEG-TDP, the EMG-FR and any combination thereof It is another object of the current invention to disclose the device as defined in any of the above, wherein the device comprises means for receiving electrocardiography (ECG) data; the means are in communication with the processor.

T* ;„ „ + „w;~~+ -P + ~ .' ;„„Ι~ ~ „ J„» „„ „„ .4„ ,Α ;„ „„. , „-τ lis iiuuiti jt i υι ic uiiciu mvtiiuun ia^iuas; ui ucvi a uumtu m uj ui the above, wherein the ECG data comprises at least one selected from a group consisting of raw ECG data, RR intervals data and any combination thereof.

It is another object of the current invention to disclose the device as defined in any of liic u vc, vviiciciii iicaii l ic vtmoL/iny ixi v » is xiuiu uic uaia.

It is another object of the current invention to disclose the device as defined in any of the above, wherein the extraction of the HRV is performed by the application of fast Fourier transform (FFT) on the ECG data.

is nu icr ODjc i ui ic buuciii mvcii u iu

ucvi c s uciiucu ui aiy ui the above, wherein the qCON is additionally defined as a function of the ECG data.

It is another object of the current invention to disclose the device as defined in any of the above, wherein the initial qNOX is additionally defined as a function of the ECG uaiti.

It is another object of the current invention to disclose the device as defined in any of the above, wherein the processor is additionally adapted to apply on the HRV data and the EEG-FR at least one mathematical manipulation selected from a group consisting of . 1 „ .i„*: ττ_^ι,ι. ni i, r, j r. ;„„ itiuiuiu miuuii-iutm ntuyi)ia, uuss cun ciaiiuii, i UAJ^CI -x Hindis, uiiil, uiiiuaioii coefficients and any combination thereof.

It is another object of the current invention to disclose the device as defined in any of the above, wherein the qCON is defined by application of at least one mathematical iiiampui iiuii n uat acic icu iiuiii a giuup v

^uiiaisimg ui uaia, uic uaia, and any combination thereof, by means of at least one classifier.

It is another object of the current invention to disclose the device as defined in any of the above, wherein the qNOX is defined by application of at least one mathematical iauipu auuu

uaia itituitu iiuin a giuu t<»_maisimg ui lOc C uaia, uic uaia, the ECG data, and any combination thereof, by means of at least one classifier.

It is another object of the current invention to disclose the device as defined in any of the above, wherein the mathematical manipulation is selected from a group consisting

„t-- ι,·„„„„ „„„„;.

ι„_Λ;„ „Ι„„„;Λ— „_Λ.._™ι „„ι, », -;,i lineal cgicsaiun, iugiau^ lCgicaai n, ivUiiTy u i i aaiiici, iicuiai ., iivuiiu between a fuzzy logic system and a neural network such as Adaptive Neuro Fuzzy Inference System (ANFIS) and any combination thereof. It is another object of the current invention to disclose the device as defined in any of the above, wherein the level of arousal of the subject is selected from a group consisting of awake, under sedation, under general anesthesia and any combination thereof.

it 15 auuuici uujc t υι uic cuncm utvciiuuu iu uia iuac uic ucv icc s uciiucu m aiiy ui the above, additionally comprising a warning unit configured for activating an alarm if the final qNOX is above a predetermined value.

It is another object of the current invention to disclose the device as defined in any of uic aDu v c, auui uiuui inpi ianig a

ua icu uiapiavuig uic J uiai ψν υΛ.

It is another object of the current invention to disclose the device as defined in any of the above, additionally comprising EEG input means for receiving EEG data; the EEG input means comprises electrodes positioned on the subject's scalp.

it io aiiuuici uujc i ui uic unciit uivcuuuu iu uiaciuac iu6 u v icc aa uciiucu in aiCy 01 the above, wherein the EEG input means comprises three electrodes positioned at middle forehead, left forehead and right forehead.

It is another object of the current invention to disclose the device as defined in any of

UIC tlUU V C, tlUUIuOilililji CUiUpiialUg i_ ivlvj input iiicaiia IUI i C Ct V Iilg J_ 1V1VJ u ia, uic

EMG input means comprises electrodes positioned on the subject's scalp.

It is another object of the current invention to disclose the device as defined in any of the above, additionally comprising ECG input means for receiving ECG data.

it la ίΐιιυιιι6ι Gujcct i inc ctuicm. mvcnuun to uiaciuac uic ucvicc tia uciiucu m tuiy ui the above, wherein the ECG from a group consisting of 3 -lead ECG, 5-lead ECG, 12- lead ECG and any combination thereof.

RPTCT? nccfD TPrrnw r v ΤΓ.ΤΠ vim TO TTC

In the following detailed description of the preferred embodiments, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the invention may be practiced. It is unuci aujuu mat uuici ciiiuuuiincma m y uc aiiu au u tiuai iiangca maj u lu ut without departing from the scope of the present invention. The present invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the

_^« _" . — _{^ ^} j ί , , t u , . » — - .,- _^ . ₊ . , , . ₊ , , ,'_^ .-. _^ .4- iiiv iiuuii liaa uui u ii ucacnucu in uctuii au uiai uic i a in mv cuuun la iiui unnecessarily obscured. Fig. 1 is a schematic flow diagram illustrating a method for determining a consciousness index and a nociception index based ECG, EMG and EEG measures (100);

Fig. 2 is a schematic flow diagram illustrating a method for determining qCON and qNOX based only on EEG and EMG measures (200);

Fig. 3 is a schematic flow diagram illustrating a method for determining qCON based

Fig. 4 is a schematic flow diagram illustrating a method for determining final qNOX based on EEG measures and qCON (400);

Fig. 5 is an illustration of the change in nociception and consciousness in response to the administration of hypnotics, analgesics and noxious stimulus (500);

Fig. 6 is an illustration of the ECG spectrum and the corresponding FFT spectrum from an awake subject and an anesthetized subject; and,

Fig. 7 is a graph describing the dependency of a in initial qNOX.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In the following detailed description of the preferred embodiments, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the invention may be practiced. It is understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present invention. The present invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the present invention is not

The essence of the present invention is to provide a method and device for assessing the probability of response to pain nociception (final qNOX) of a subject during different levels of arousal. More specifically, the invention pertains to a method and device for

qNOX) and a consciousness index (qCON).

The term "about" refers hereinafter to +/- 25% above or below the value stated after the about term. For example, about 100 refers to any value in the range of 75-125. The term "nociception" refers hereinafter to the neural processes of encoding and processing noxious stimuli. More specifically the term describes the afferent activity produced in the peripheral and central nervous systems by stimuli that have the potential to damage tissue. This activity is initiated by nociceptors (also called pain receptors), that can detect mechanical, thermal or chemical changes above a set threshold. Once stimulated, a nociceptor transmits a signal along the spinal cord, to the brain. Nociception triggers a variety of autonomic responses and may also result in a subjective experience ot pain in sentient beings.

The term "level of arousal" refers hereinafter to the level of consciousness of a subject. The different levels of arousal are awake, asleep under different levels of sedation, under different levels of general anesthesia, etc.

The term "electroencephalography (EEG)" refers hereinafter to the recording of electrical activity along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain. In clinical contexts, EEG refers to the recording of the brain's spontaneous electrical activity as recorded from multiple electrodes placed on the scalp. Diagnostic applications generally focus on the spectral content of EEG, that is, the type of neural oscillations that can be observed in EEG signals.

The term "electromyography (EMC)" refers hereinafter to a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG is performed using an instrument called an electromyograph, to produce a record called an electromyogram. An electromyograph detects the electrical potential generated by muscle cells when these cells are electrically or neurologically activated. The signals can be analyzed to detect medical abnormalities, activation level, or recruitment order or to analyze the biomechanics of human or animal movement.

The term "electrocardiography (ECG)" refers hereinafter to is a transthoracic (across the thorax or chest) interpretation of the electrical activity of the heart over a period of time, as detected by electrodes attached to the surface of the skin and recorded by a device external to the body. The recording produced by this noninvasive procedure is termed an electrocardiogram. An ECG picks up electrical impulses generated by the depolarization of cardiac tissue and translates into a waveform. The waveform is then used to measure the rate and regularity of heartbeats, as well as the size and position of the chambers, the presence of any damage to the heart, and the effects of drugs or devices used to regulate the heart, such as a pacemaker.

The term "fast Fourier transform (FFT)" refers hereinafter to an algorithm to compute the discrete Fourier transform (DFT) and its inverse. A Fourier transform converts time (or space) to frequency and vice versa; an FFT rapidly computes such transformations. As a result, fast Fourier transforms are widely used for many applications in engineering, science, and mathematics.

The term "frequency ratios" refers hereinafter to the result of fast Fourier transform (FFT) carried out on EEG or EMG data in a specific range of frequencies. The FFT can be applied on different ranges of frequencies of EEG and EMG data. In a preferred embodiment the frequency ratios can be calculated to more than one frequency range, in another preferred embodiment the range of EMG frequencies is calculated to 60-80 Hz or 0-80 Hz.

The term "burst suppression" refers hereinafter to an electroencephalogram pattern observed in states of severely reduced brain activity, such as general anesthesia, hypothermia and anoxic brain injuries. The burst suppression ratio (BSR), defined as the fraction of EEG spent in suppression per epoch, is the standard quantitative measure used to characterize burst suppression. Chemali J.J., A state-space model of the burst suppression ratio, Conf Proc IEEE Eng Med Biol Soc. 2011;2011:1431-4 is incorporated here as a reference.

The term "time domain parameter" refers hereinafter to a parameter defined by the generalization of the Hjorth parameters (activity, mobility and complexity) Time Domain Parameters are studied under two different conditions. The first setting is defined when no data from a subject is available. In this condition our results show that Time Domain Parameters outperform all band power features tested with all spatial filters applied. The second setting is the transition from calibration (no feedback) to feedback, in which the frequency content of the signals can change for some subjects. We compare Time Domain Parameters with logarithmic band power in subject-specific

D nua ixu suuw uiai ica l aLui^o i auvauiag uuo ui una auuauuu aa w^n. V luduiiu

C. et al., Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces Neural Netw. 2009 Nov;22(9): 1313-9 is incorporated here as a reference. The term "RR intervals" refers hereinafter to the time elapsing between two consecutive R waves in the electrocardiogram. More specifically the term relates to the interval from the peak of one QRS complex to the peak of the next as shown on an electrocardiogram. It is used to assess the ventricular rate.

The term "heart rate variability (HRV)" refers hereinafter to the physiological phenomenon of variation in the time interval between heartbeats. It is measured by the variation in the beat-to-beat interval. The HRV can be calculated from either ECG raw uaia ui n uiii ινιν ιΏιει ναΐΰ uaia. muai

uacu mcuiuua iui analysing i xi v ai Q grouped under time-domain, frequency-domain and non-linear methods

The term "linear regression" refers hereinafter to an approach to model the relationship between a scalar dependent variable y and one or more explanatory variables denoted X.

The term "logistic regression" refers hereinafter to a type of probabilistic statistical classification model. It is also used to predict a binary response from a binary predictor, used for predicting the outcome of a categorical dependent variable (i.e., a class label) empirical values of the parameters in a qualitative response model. The probabilities describing the possible outcomes of a single trial are modeled, as a function of the explanatory (predictor) variables, using a logistic function. Frequently (and

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tuι—v problem in which the dependent variable is binary— that is, the number of available categories is two— and problems with more than two categories are referred to as multinomial logistic regression or, if the multiple categories are ordered, as ordered logistic regression.

The term "fuzzy logic classifier" refers hereinafter to the process of grouping elements into a fuzzy set (Zadeh 1965) whose membership function is defined by the truth value of a fuzzy propositional function.

The term "neural network" refers hereinafter to computational models inspired by animal central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected "neurons" that can compute values from inputs by feeding information through the network. The term "Adaptive Neuro Fuzzy Inference System (ANFIS)" refers hereinafter to a kind of neural network that is based on Takagi-Sugeno fuzzy inference system. Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the «„««i« ~f „*i, :„ » „ ;„„ i„ f— τ*„ ;„^„„,„„₌ —„ „ uciiciiij ui uuui in a aiiigic ii ttmc vvui ft.. us iuicidi / s^ aitiii oui its uiiu_> lO a aci ui fuzzy IF-THEN rules that have learning capability to approximate nonlinear functions. [1] Hence, ANFIS is considered to be a universal estimator.

The term "mutual information analysis" refers hereinafter to the measure of the mutual dependence of the two random variables. The most common unit of measurement of mutual information is the bit.

The term "cross correlation" refers hereinafter to the measure of similarity of two

The term "Fokker-Planck drift and diffusion coefficients" refer hereinafter to coefficients extracted from an EEG frequency band. The diffusion coefficients are constant when representing additive noise, whereas for multiplicative noise, the

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Reference is now made to Fig. 1, which illustrates a schematic flow diagram of a method for determining consciousness index and nociception index based on ECG, EMG and EEG measures (100). In a preferred signal from one channel EEG (110) and EMG (120) is recorded using three surface electrodes positioned middle forehead, left and right forehead. However, signals can be recorded from other EEG, EMG or combined EEG/EMG recorders, utilizing different number of electrodes. In addition, in the preferred embodiment, ECG signals (130) are recorded with three surface electrodes jjuaiuuucu uii me iicsi in sianu i u puoiuuii.

Reference still made to Fig.l. Burst suppression (BS) (111) data and EEG frequency ratios (EEG-FR) (112) are extracted from the EEG data. EEG-FR is extracted by applying to the EEG data an FFT. EMG frequency ratios (EMG-FR) (121) are extracted f„_™ -„ . , ι» .;„„ +

Uiii UiC X_,iVX\J U ui u ttUUXj iilg IU il Oil X . i iWH l tUO V ai i UlliiJi ^X XX V ) ^ /i ii iJ iin i¾ extracted from ECG raw data and/or from RR interval data (130A) by applying to it an FFT. On the EEG-FT and HRV a correlation manipulation is carried out to give an EEG-FR HRV correlation result (140). The correlation function is at least one selected from mutual information, Fokker-Planck drift, diffusion coefficients and cross correlation. To receive the index of consciousness (qCON) (150) an ANFIS is carried uui u_ a ^iaasiiici uu uic ^ iv ^ */ aiiu utc -i i\ l ilv v 0ΟΓΐ£ί ΐίΟΗ result (140). To receive the index of nociception (qNOX) (160) and an ANFIS is carried out by a classifier on the HRV (131), EMG-FR (121) and the EEG-FR HRV correlation result (140) to receive the index of nociception (initial qCON).

Reference is now made to Fig. 2, which illustrates a schematic flow diagram of an alternative embodiment of the invention (200) using only EEG (210) and EMG (220) data for determining the index of nociception and the index of consciousness. BP (211) and EEG-FR (212) are calculated from the EEG data (210) and EMG-FR (221) is calculated from the EMG data (220). The index of consciousness (230) is calculated by applying an ANFIS by a classifier on BP (211) and EEG-FR (212) and the index of nociception (240) is calculated by applying an ANFIS on the EMG-FR (221) and EEG

Reference is now made to Fig. 3, which illustrates a schematic flow diagram of an alternative embodiment of the invention for calculating the index of consciousness (300) using only EEG data (310). From the EEG data time domain parameters (311), /1tt\ „-,,4 rtlli HID 1f\ I„,,1 j_>± jjiij aii\x j \j"i iv in it l aiig a υι lauu^ ^ ₎ - _^ i*, _* ) i iwui i^u. i ii index of consciousness (320) is then calculated by applying to the calculated parameters an ANFTS by a classifier.

Reference is now made to Fig. 4, which illustrates a schematic flow diagram of an alternative embodiment of the invention for calculating the index of nociception (400) using EEG data (210) and a pre-calculated consciousness index (420). From the EEG data time domain parameters (411), BP (412) and EEG-FR in n ranges of ratios (413A, 413B, 413C) are calculated. The index of nociception (430) is then calculated by applying to the calculated parameters and the pre-calculated consciousness (420) index an ANFIS by a classifier.

In a preferred embodiment of the invention the index of nociception and consciousness are presented graphically in a coordinate system where the x-axis is time, while the y- axis presents unitless values of the index of consciousness and the index of nociception. The scale for both indices in the 0 to 100 range. Reference is now made to Fig. 5, which is an example of the behavior of the index of nociception and the index of consciousness. The index of consciousness is high until a dose of hypnotics is administered, where after the index decreases. Similar, the index of

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index of nociception to increase if the effect of the analgesia is not sufficient to ensure that the patient will not have a nociceptive response to the stimulus. A further administration of analgetics will cause the index to decrease more.

In a preferred embodiment, the HRV is calculated by performing an FFT on both the complete ECG signal and the RR intervals, over at least two different time windows. In most previous works the FFT has been carried out on the RR-intervals only, instead of on the raw ECG. The FFT on the ECG is considered a more noisy signal but it is also a more complete signal than the RR, therefore more information (features) can be extracted from that analysis.

Reference is now made to Fig. 6, showing an ECG spectrum and the corresponding FFT spectrum (600). The left shows the ECG spectrum of a subject under anesthesia having a relatively constant frequency constant (70 b/min) (61 OA). Therefore, the FFT spectrum is narrow around the heart rate (main) frequency (610B). The right spectrum shows the ECG (620A) and the FFT spectrum (620B) from an awake subject, showing a higher degree of variation around the main frequency.

Example 1

To calculate the index of nociception, qNOX. The EEG is recorded and frequency ratios are extracted together with time domain parameters for example Burst Suppression. The output of the classifier, most likely an Adaptive Neuro Fuzzy Inference System (ANFIS), is the preliminary version of the index of nociception.

The final index of nociception (qNOX) is compensated with the index of consciousness (qCON). The reason is that a patient who is under a deep anesthesia where the hypnotic effect is high, is very unlikely to respond to noxious stimulation. Therefore, the nociception index can be defined with more precision when the knowledge to the level of consciousness is included in the calculation, hence the index of nociception is dependent on the index of consciousness.

The following formula is applied: final qNOX = (1 - a) * initial qNOX + a * qCON If initial qNOX > qCON and qCON < kl, then a = k2 - k4 * (qCON - k3) and if a > k2 then a = k2

where kl and k4 is in the range 25-60 , while k2 and k3 in the range 0-1

For example, if initial qNOX > qCON And qCON < 50 Then

a = 0.5 - 0.05 * (qCON - 40), if a > 0.5 Then a = 0.5 Final qNOX = (1 - a) * initial qNOX + a * qCON

Reference is now made to Fig. 7, which illustrates the dependency of a in initial qNOX. In this example, when the qCON is less than 40 and less than initial qNOX then 50 % of the weight of the final qNOX will come from the qCON. When the qCON is above 50 uic wt gui is υ.

Claims

1. A method for determining the probability of response to pain and nociception (final qNOX) of a subject during different levels of arousal, comprising steps of: a. receiving electroencephalography (EEG) data and electromyography (EMG) data;

b. defining an index of consciousness (qCON) as a function of said EEG defining an index of nociception (initial qNOX) as a function of said

EEG data and said EMG data; and,

defining a weighing factor alpha a as a function of qCON;

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following formula:

(i) a = k₂ - k₄ * (qCON - k₃);

;

(ii) a = k₂;

further wherein a final qNOX is defined by the following formula:

(iii) final qNOX = (1 - a) * initial qNOX + a * qCON.

The method of claim 1, additionally comprising a step of determining the values of said qCON, said initial qNOX and said final qNOX in the range of about 0 - about 100.

The method of claim 2, additionally comprising a step determining said predetermined values k] and k₃ in the range of about 25 - about 60; and, said predetermined values k₂ and ki in the range of about 0 - about 1.

The method of claim 1, additionally comprising a step of extracting EEG frequency ratios (EEG-FR), burst-suppressions (BS), EEG time domain parameters (EEG-TDP) and any combination thereof, from said EEG data.

said EEG-FR is performed

6. The method of claim 1, additionally comprising a step of extracting EMG frequency ratios (EMG-FR) from said EMG data.

7. The method of claim 6, wherein said step of extracting said EMG-FR is performed by applying a fast Fourier transform (FFT) on said EMG data.

8. The method of claims 4 and 6, additionally comprising a step of calculating said EEG-FR and said EMG-FR from at least one range of frequencies of about 0- about 100 Hz.

9. The method of claim 4, additionally comprising a step of deriving said qCON

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BS, said EEG-TDP, and any combination thereof.

10. The method of claim 4 and 6, additionally comprising a step of deriving said initial qNOX from a function of at least one parameter selected from said EEG- FR, said EEG-TDP, said EMG-FR and any combination thereof.

11. The method of claim 1, additionally comprising a step of receiving electrocardiography (ECG) data.

12. The method of claim 11, additionally comprising a step of selecting said ECG data from a group consisting of raw ECG data, RR intervals data and any combination thereof.

13. The method of claim 11, additionally comprising a step of extracting heart rate variability (HRV) from said ECG data.

14. The method of claim 13, wherein said step of extracting of said HRV is performed by the application of fast Fourier transform (FFT) on said ECG data.

15. The method of claim 11, additionally comprising a step of defining said qCON as a function of said ECG data.

16. The method of claim 11, additionally comprising a step of defining said initial qNOX as a function of said ECG data.

17. The method of claim 13, additionally comprising a step of applying to said HRV data and said EEG-FR at least one mathematical manipulation selected from a group consisting of mutual information analysis, cross correlation, Fokker-Planck drift, diffusion coefficients and any combination thereof.

18. The method of claim 1, additionally comprising a step of defining said qCON by applying at least one mathematical manipulation on data selected from a group consisting of said EEG data, said ECG data, and any combination thereof, by means of at least one classifier.

19. The method of claim 1, additionally comprising a step of defining said by applying at least one mathematical manipulation on data selected from a group consisting of said EEG data, said EMG data, said ECG data, and any combination thereof, by means of at least one classifier.

20. The method of claim 18 or 19, additionally comprising a step of selecting a mathematical manipulation from a group consisting of linear regression, logistic regression, fuzzy logic classifier, neural network, hybrid between a fuzzy logic system and a neural network such, Adaptive Neuro Fuzzy Inference System (ANF1S) and any combination thereof.

21. The method of claim 1, additionally comprising a step of selecting said level of arousal of said subject from a group consisting of awake, under sedation, under general anesthesia and any combination thereof.

22. The method of claim 1, additionally comprising a step of activating an alarm if said final qNOX is above a predetermined value.

23. The method of claim 1, additionally comprising a step of displaying said final qNOX.

24. The method of claim 1, additionally comprising a step of obtaining EEG input means for receiving said EEG data; said EEG input means comprises electrodes positioned on said subject's scalp.

25. The method of claim 24, additionally comprising a step of positioning said electrodes at middle forehead, left forehead and right forehead.

26. The method of claim 1, additionally comprising a step of obtaining EMG input means for receiving said EMG data; said EMG input means comprises electrodes.

27. The method of claim 26, additionally comprising a step positioning said electrodes on said subject's scalp.

28. The method of claim 11, additionally comprising a step of attaching ECG input means for receiving said ECG data.

29. The method of claim 28, additionally comprising a step selecting said ECG input means from a group consisting of 3 -lead ECG, 5 -lead ECG, 12-lead ECG and any combination thereof.

30. A device for determining the probability of response to pain and nociception (final qNOX) of a subject during different levels of arousal, comprising at least one processor and at least one computer readable memory coupled to the processor, said at least one computer readable medium comprises operations executed by said at least one processor, said operations are:

a. receiving electroencephalography (EEG) data and electromyography (EMG) data;

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data;

c. defining an index of nociception (initial qNOX) as a function of said EEG data and said EMG data; and,

d. defining a weighing factor alpha ct as a function ofqCON; wherein, if said initial qNOX > qCON and qCON < ki, a is defined by the following formula:

(i) a = k₂ - k4 * (qCON - k₃);

where ki, k₂, k₃ and k4 are predetermined values;

if a > k₂, a is defined by the following formula:

(ii) a = k₂;

further wherein a final qNOX is defined by the following formula:

(iii) final qNOX = (1 - a) * initial qNOX i a * qCON.

31. The device of claim 30, wherein said qCON, said initial qNOX and said final qNOX has a value in the range of about 0 - about 100.

32. The device of claim 31, wherein said predetermined values k \ and k₃ are in the range of about 25 - about 60; and, said predetermined values k₂ and kj are in the range of about 0 - about 1.

33. The device of claim 30, wherein EEG frequency ratios (EEG-FR), burst- suppressions (BS), EEG time domain parameters (EEG-TDP) and any combination thereof are extracted from said EEG data.

34. The device of claim 33, wherein said extraction of said EEG-FR is performed by the application of fast Fourier transform (FFT) on said EEG data.

35. The device of claim 30, wherein EMG frequency ratios (EMG-FR) are extracted from said EMG data.

36. The device of claim 35, wherein said extraction of said EMG-FR is performed by the application of fast Fourier transform (FFT) on said EMG data.

37. The device of claims 33 and 35, wherein said EEG-FR and said EMG-FR are calculated from at least one range of frequencies of about 0- about 100 Hz.

38. The device of claim 33, wherein said qCON is derived from at least one parameter cf>]( tf*r fr/vm ci crrrvnrt rvT mir!

ΪΛΤΛΛ any combination thereof.

39. The device of claims 33 and 35, wherein said initial qNOX is derived from a function of at least one parameter selected from said EEG-FR, said EEG-TDP, said EMG-FR and any combination thereof.

40. The device of claim 30, wherein said device comprises means for receiving electrocardiography (ECG) data; said means are in communication with said

41. The device of claim 40, wherein said ECG data comprises at least one selected from a group consisting of raw ECG data, RR intervals data and any combination thereof.

42. The device of claim 40, wherein heart rate variability (HRV) is extracted from said ECG data.

43. The device of claim 42, wherein said extraction of said HRV is performed by the

44. The device of claim 40, wherein said qCON is additionally defined as a function of said ECG data.

45. The device of claim 40, wherein said initial qNOX is additionally defined as a function of said ECG data.

46. The device of claim 42, wherein said processor is additionally adapted to apply on said HRV data and said EEG-FR at least one mathematical manipulation selected from a group consisting of mutual information analysis, cross correlation, Fokker- Planck drift, diffusion coefficients and any combination thereof.

47. The device of claim 30, wherein said qCON is defined by application of at least one mathematical manipulation on data selected from a group consisting of said EEG data, said ECG data, and any combination thereof, by means of at least one classifier.

48. The device of claim 30, wherein said qNOX is defined by application of at least one mathematical manipulation on data selected from a group consisting of said EEG data, said EMG data, said ECG data, and any combination thereof, by means of at least one classifier.

49. The device of claim 47 or 48, wherein said mathematical manipulation is selected from a group consisting of linear regression, logistic regression, fuzzy logic classifier, neural network, hybrid between a fuzzy logic system and a neural network such, Adaptive Neuro Fuzzy Inference System (ANFIS) and any combination thereof.

50. The device of claim 30, wherein said level of arousal of said subject is selected from a group consisting of awake, under sedation, under general anesthesia and any combination thereof.

51. The device of claim 30, additionally comprising a warning unit configured for activating an alarm if said final qNOX is above a predetermined value.

52. The device of claim 30, additionally comprising a display adapted for displaying said final qNOX.

53. The device according to claim 30, additionally comprising EEG input means for receiving EEG data; said EEG input means comprises electrodes positioned on said subject's scalp.

54. The device of claim 53, wherein said EEG input means comprises three electrodes positioned at middle forehead, left forehead and right forehead.

55. The device of claim 30, additionally comprising EMG input means for receiving EMG data; said EMG input means comprises electrodes positioned on said subject's scalp.

56. The device of claim 40, additionally comprising ECG input means for receiving ECG data.

57. The device of claim 56, wherein said ECG from a group consisting of 3 -lead ECG, 5-lead ECG, 12-lead ECG and any combination thereof.