=Paper=
{{Paper
|id=Vol-3058/paper79
|storemode=property
|title=Quantification Of Pain Using EEG And Fuzzy Logic
|pdfUrl=https://ceur-ws.org/Vol-3058/Paper-111.pdf
|volume=Vol-3058
|authors=Amit Kumar,Amod Kumar,Kanika Sharma
}}
==Quantification Of Pain Using EEG And Fuzzy Logic==
https://ceur-ws.org/Vol-3058/Paper-111.pdf
Quantification of Pain using EEG and Fuzzy Logic
Amit Kumar1, Amod Kumar2 and Kanika Sharma3
1,2,3
Department of Electronics and Communications Engineering, NITTTR Chandigarh (India
Abstract
This paper aims to quantify and calculate the pain level of the volunteers experienced during
cold pressure test with the help of electroencephalogram (EEG). The experiment was done on
20 volunteers having age group of 20 to 60 years. The EEG of 20 people was taken in normal
(No Pain) condition and in Pain state. The pain was induced by cold pressure test. The
acquired EEG contains noise and other artifacts which were removed with the help of filters.
Various features of EEG signal were extracted. Three features viz. amplitude (RMS value),
Power Spectral Density (PSD), and Higuchi fractal Dimension (HDF) responded well to pain
and had the ability to separate pain and non-pain signals efficiently. These three parameters
were fed as input to a fuzzy inference system (FIS). Fuzzy rules were made very carefully
after experimentation at different pain levels. The output of FIS is the pain index ranging from
0 to 10, where 0 is for No Pain, 5 is for Medium Pain, and 10 is for High Pain.
Keywords
Pain estimation, EEG, Power Spectral Density (PSD), Higuchi Fractal Dimension (HDF),
Fuzzy Logic, Fuzzy Inference System (FIS)
1. Introduction
The estimation of pain is a major problem for the physicians since many years. It is very difficult
to assess the pain level of deaf, children, patients in ICU, paralyzed patients, semi anaesthetized
patient, and patients who can’t communicate due to some injuries or suffer from mental health
problem. Generally, automated pain intensity detection is very beneficial for low Glasgow comma
scale (GCS) for measurement of consciousness level [1].For patients who cannot communicate about
their pain level with physician, there is urgent need of pain detection device so that doctors can treat
their patient with the right amount of dose [2]. During surgery, analgesic drug is given to the patient to
alleviate pain. But in some cases, surgery takes long time and the patient does feel pain but he cannot
speak out because he is paralyzed. With the help of pain detection device, physician can easily monitor
patient’s pain level and accordingly, analgesic drug can be given whenever required [3]. Similarly,
children or patients with mental problem are also not able to tell how much pain they are feeling [4].
Then there are some criminals who try to fake pain in court to escape hearing of their case. ICU
patients are given sedatives to tolerate the pain level for their comfort [5] [6] but there is no pain
detection device to measure their pain.
The pain is the unpleasant sensation to body due to inflammation or tissue damage [7].1It can be
detected by many methods. First method for pain detection is by verbal communication to the patient
[8]. We can ask the patient about the grade of his pain i.e. very high pain, low pain, or having no pain
at all. But this method fails when the patient is deaf, ICU patient, or children etc. who cannot
communicate. Second method of pain detection is by heart rate variability. It can be done with the help
of electrocardiogram (ECG). When the patient feels pain, it causes variation in the heart rate; this
variation is calculated with the help of the ECG and thus, pain can be detected [9] [10]. But problem
with this method of pain detection is that the heart rate is a fragile parameter, ready to undergo change
International Conference on Emerging Technologies: AI, IoT, and CPS for Science & Technology Applications, September 06–07, 2021,
NITTTR Chandigarh, India
EMAIL: amit.jngec123@gmail.com (A. 1); csioamod@yahoo.com (A. 2); kanikasharma80@yahoo.com (A. 3)
ORCID: 0000-0002-9212-0266 (A. 1);
©2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
on the slight pretext. It cannot be relied upon for diagnosing any non-cardiac event. Third method of
pain detection is by analyzing facial expressions [11]. But problem with this method is that there are
some patients who are in coma or ICU and are not able to give expressions [12].
One of the most effective methods of pain detection is EEG based. Whenever patients feel pain,
its sensation goes directly to brain. The brain signals are reflected in EEG. In this work, pain was
induced in volunteers and EEG signals were acquired. It was filtered using low pass filter to remove
noise and other artifacts [13]. Various time-domain and frequency-domain features were extracted.
Their efficacy was evaluated vis-à-vis pain sensation [14] [15]. Those parameters which showed
excellent correlation with pain were selected to estimate the pain index. These parameters, being
imprecise in value, were given to a fuzzy inference system whose output was a crisp number
corresponding to Pain Index. In place of fuzzy logic we can use KNN (K- Nearest Neighbors), SBS
(Sequential Back Selection), SVM (Support Vector Machine) [16], and Neural Network techniques as
a classifier. However, Fuzzy Logic classifier is easy and efficient method of EEG signal classification
[13].
2. Material and Method
In the experiment, 20 volunteers in the age group of 20 to 60 years (male +female) were included.
These volunteers were teetotaler and non-smoking. The weight of volunteer ranges from 45 to 80 Kg,
the experiment was performed on 32 channel EEG machine (Recorders and Medicare System,
Chandigarh, India). The data format was 24 bit and sampling rate was 1024 samples/sec. There were 8
electrodes which were placed at different position of brain viz. Fp1, Fp2, C3, C4, P3, P4, Ground, and
Reference. These EEG electrodes were in the shape of cup and made up of silver (Ag) and silver
chloride (AgCl) [17]. The EEG was taken using EEG machine and analyzed on AD Instrument Lab
Chart. The EEG of volunteers was taken first in normal state i.e. No Pain condition. Then EEG of
patient is taken in Pain state. The pain is produced by using cold pressure method [18]. In this test, ice
cube of size 4.5 × 3 × 1.8 cubic centimeters were added to 3 liter water baths and right hand of
volunteer was dipped into it to generate pain. With the passage of time, the volunteer felt pain and thus
EEG in pain condition was recorded. This process was repeated for every volunteer [14].During this,
they were asked the pain level verbally which was marked so that later EEG signals at different level
can be recognized. The schematic of the signal processing is shown in Figure 1. The flowchart appears
in Figure 2.
Figure1: The schematic of signal processing.
• EEG in Pain and Non Pain condition: The EEG of every volunteer is taken in Pain and No-
Pain condition. For inducing pain, the cold pressure method is used.
• Artifact removal and Filtering of EEG signal: Raw EEG recorded with the help of the EEG
machine setup contains noise and other artifacts due to eye blinking, 50-60 Hz electrostatic
interference, muscle contraction or expansion etc. To get clean EEG signal, these unwanted signals
must be removed. Raw EEG signal was passed through a notch filter of 50 Hz and a low pass filter
with a cut-off frequency of 70 Hz. This reduced electrostatic interference and noise significantly. If
EEG signal is badly affected then adaptive filtering can also be used [19].
Figure 2: Flowchart of operation.
• Feature Extraction: The EEG data was divided into epochs of 4seconds each. Various
parameters were calculated for these epochs. Linear discriminant analysis was applied to identify
parameters which were highly related to pain. Table 1 gives the list of these parameters.
Table 1
Calculated EEG parameters
S.No. Calculated Comments
Parameter
1 RMS Value Root mean square value of every epoch
2 Power Spectral Measure of signal irregularities in frequency domain
Density
3 Higuchi Fractal Calculation of fractal dimension of a time series directly in
Dimension time domain
The method of calculation of above parameters is given below:
1. RMS value: RMS is the Root Mean Square value of signal. It can be calculated as:
1
RMS = ∑
N i
xi2 (1)
where, N = the number of measurements and xi = the sample amplitude value.
2. Power Spectral Density: Spectral Density (PSD) describes the distribution of the signal power
with frequency. The significant frequency components of signal which can be considered for further
study is decided by its Power Spectral Density (PSD). It is determined by using Welch’s method. In
this method first periodogram is calculated and then PSD is calculated by taking average of
periodogram as described below in equation 4 and 5.
1
Periodogram = |𝐹𝐹𝐹𝐹𝐹𝐹|2 (2)
𝑀𝑀𝑀𝑀
Where, U is the normalization factor and M is the window hop size. Welch’s estimate of PSD is
given as:
Power Spectral Density (PSD) = S(f)= average (Periodogram) (3)
3. Higuchi Fractal Dimension (HFD): In 1988, Higuchi published a research paper on nonlinear
measurement of magnetic field change which was based on fractal theory [16]. However, this method
is sensitive to noise and other random signals. Therefore, for getting good results, artifacts and other
noise random signals must be removed efficiently from EEG signal. The various steps for
determination of HFD of a time series x(1), x(2), ……. x(N) are:
Step 1: The new time series is generated by using the equation:
𝑘𝑘 𝑁𝑁−𝑚𝑚
𝑥𝑥𝑚𝑚 = �𝑥𝑥(𝑚𝑚), 𝑥𝑥(𝑚𝑚 + 𝑘𝑘), 𝑥𝑥(𝑚𝑚 + 2𝑘𝑘). . . . . . . . 𝑥𝑥(𝑚𝑚 + � �)𝑘𝑘� (4)
𝑘𝑘
m = 1, 2, 3, 4….k
where, m is the initial time and k is the initial time interval respectively.In this way, there will
be k new time series.
Step 2: Calculate average length of each new time series Lm (k ) which can be calculated as:
N − m
1
k
N − 1
Lm (k ) = ∑ x(m + ik ) − x(m + (i − 1)k ) (5)
k i =1 N − m
k
k
This process is repeated for almost all values of k from 1 to k max . N is the number of samples. In
some research papers, it was taken as 11 or 17 or 25 but in this study the value of Kmax is taken 6and N
≥125 because at this value accurate and faster algorithm for fractal dimension is obtained [20].
Step 3: The mean length of the actual time series is calculated as the average of Lm (k ) by the
equation:
Mean length=Average [ Lm (k ) ] (6)
The Fractal dimension of the signal is obtained as the slope of the curve ln(Mean length) vs. ln(1/k)
by using the polynomial curve fitting method [21].
• Classification of pain
Once all the above features are calculated for various EEG epochs, the next step is to feed these
parameters as input to the classifier. There are many classifiers available viz. KNN, SBS (Sequential
Backward Search), SVM (Sequential Vector Machine), Neural Networks, Fuzzy Inference System
(FIS), and PKSVM (Polynomial Kernel Support Vector Machine)) etc [22]. All these classifiers
except Fuzzy Inference System need large amount of data. As we have EEG data of only 20 patients
so we made use of Fuzzy Inference System (FIS). FIS is easy to use and gives reliable and accurate
results. In this work, “Mamdani” type of FIS is used with trapezoidal (trapmf) membership functions.
The FIS containing three inputs viz. RMS, Power Spectral Density and Higuchi Fractal Dimension is
schematically shown in Figure 3. All these inputs are processed by the fuzzy rule set mentioned in
Table 2.
Figure 3: Fuzzy Inference System with Input, Mamdani, and Output state.
The design of rules was done as follows:
Rule 1: If two or more input parameters show a high pain value then it is considered as high pain.
Rule 2: If two or more input parameters show a medium pain value then it is considered as
medium pain.
Rule 3: If two or more input parameters show a low pain value then it is considered as low pain.
Rule 4: If all parameters show different pain levels then it is taken as medium pain.
It must be kept in mind that the RMS and Power Spectral Density are directly correlated with
pain level while HFD is inversely correlated with pain.
Table 2
The rule base used in fuzzy inference system (FIS).Calculated EEG parameters.
Rule RMS Value Power Higuchi Output
No. Spectral Fractal Pain
Density Dimension Index
(PSD) (HFD)
1 Low RMS Low PSD Low HFD Low Pain
2 Medium RMS Low PSD Low HFD Medium Pain
3 High RMS Low PSD Low HFD High Pain
4 Low RMS Medium PSD Low HFD Medium Pain
5 Medium RMS Medium PSD Low HFD Medium Pain
6 High RMS Medium PSD Low HFD High Pain
7 Low RMS High PSD Low HFD High Pain
8 Medium RMS High PSD Low HFD Medium Pain
9 High RMS High PSD Low HFD High Pain
10 Low RMS Low PSD Medium HFD Low Pain
11 Medium RMS Low PSD Medium HFD Medium Pain
12 High RMS Low PSD Medium HFD Medium Pain
13 Low RMS Medium PSD Medium HFD Medium Pain
14 Medium RMS Medium PSD Medium HFD Medium Pain
15 High RMS Medium PSD Medium HFD Medium Pain
16 Low RMS High PSD Medium HFD Medium Pain
17 Medium RMS High PSD Medium HFD Medium Pain
18 High RMS High PSD Medium HFD High Pain
19 Low RMS Low PSD High HFD Low Pain
20 Medium RMS Low PSD High HFD Low Pain
21 High RMS Low PSD High HFD Low Pain
22 Low RMS Medium PSD High HFD Low Pain
23 Medium RMS Medium PSD High HFD Medium Pain
24 High RMS Medium PSD High HFD Medium Pain
25 Low RMS High PSD High HFD Low Pain
26 Medium RMS High PSD High HFD Medium Pain
27 High RMS High PSD High HFD Low Pain
There are a total of 27 rules that were made to quantify the pain level of the patient. IF part of the rule
is the premise and THEN part is the consequent. Membership functions for RMS value are shown in
Figure 4 with Low, medium, and high values ranging from 0.05 to 0.15, 0.14 to 0.25, and 0.23 to 0.5
respectively. Figure 5 shows the membership functions for PSD having low, medium, and high value
of PSD ranging from 0.001 to 0.014, 0.015 to 0.02, and 0.02 to 0.08 respectively. Figure 6 shows the
low, medium, and high value of HFD ranging from 1.25 to 1.4, 1.38 to 1.48, and 1.45 to 1.6
respectively. These values are taken based on the experiment done and parameters calculated. Figure
7 shows the membership functions of the output i.e. the Pain Index. The crisp Pain Index shows the
pain in the range 0 to 10, 0 for the No Pain, and 10 for High Pain (extreme). Figure 8 shows Pain
rating scale
Figure 4: Membership functions for RMS value Figure 5: Membership functions for PSD
Figure 6: Membership functions for HFD Figure 7: Membership functions for Pain Index
Figure 8: Pain rating scale.
• Verification of Result
After processing the inputs and defuzzyfying the output, crisp value of “Pain Index” is obtained.
The Pain Index has a range of values from 0 to 10. Output 0 is for Low Pain, 5 is for Medium Pain,
and 10 is for High Pain. The result obtained can be verified by comparing calculated result to the
actual verbal pain estimation.
3. Result and Discussion
The raw EEG is obtained by using EEG machine. The electrostatic noise is removed by passing it
through a notch filter of 50 Hz as can be seen in Figure 9.
Figure 9: Raw and notch-filtered EEG signal. Figure10: EEG signal after passing through LPF
Passing signal through a low pass filter, clean EEG output is obtained which is shown in Figure
10. All the parameters viz. RMS value, PSD, and HFD were calculated in both pain and no-pain states.
All these parameters are shown below in Figure 11-13.
Figure 11: RMS value of one of volunteer EEG. Figure12: PSD of one of the volunteer’s EEG
Figure 11 shows the typical RMS values of the EEG signal. The hard line shows Pain Condition
and dotted line shows Normal or No-Pain state. The maximum value achieved is 0.5 (High Pain) and
lowest value achieved is 0.05 (Low Pain). It can be seen that RMS parameter of EEG has great ability
to separate the Pain and No-Pain signals from each other. Low values show low pain while high
values show the high pain states. Figure12 shows the PSD of one of the volunteers. The continuous
line shows the Pain Condition and dotted line shows the Normal or No-Pain Condition. The maximum
achieved value of PSD is 0.08 corresponding to Normal or No-Pain Condition and 0.001 for High Pain
condition.
Figure 13 shows HFD parameter values of the EEG. The hard line) shows the Pain condition
of the volunteer and dotted line shows the Non Pain or Normal Condition of the patient. It is clear that
Pain and No-Pain signals are clearly separated by HFD parameter. In this case, the lowest value
obtained is 1.25 (High Pain) while highest value is 1.6 (Low Pain).One caution that must be kept in
mind is that Higuchi’s method only works at very low noise. Large noise signals may not give desired
results. However, it is considered one of the best parameters for the analysis of biomedical signals.
Based on the above-mentioned parameters, a fuzzy rule base was made. All these input parameters are
given to FIS. The FIS processes them on the basis of fuzzy rule base.
Figure 13: HFD of one of the volunteer’s EEG.
The fuzzy rule check is shown in Figure 14. The output Pain Index has three ranges- High Pain,
Medium Pain, and Low Pain. In Figure14, when RMS parameter has a value of 0.392 (High Value),
PSD has a value of 0.01966 (Medium value) and HFD has a value of 1.31(Low value), the firing of
rules and the resulting defuzzified output can be seen. After defuzzification was done by the Centroid
Method, the crisp output Pain Index obtained is 8 (High Pain). The output obtained for the FIS was got
confirmed by patients at different levels of pain.
Figure 14: Fuzzy Rule check for output Pain Index.
4. Conclusion
At present, there is no scientifically proven pain estimation technique. By conventional methods,
pain of a patient is not determined with accuracy and still there are some irregularities and limitations
in these methods. The EEG based method is very promising for the pain detection. High accuracy is
achieved by this technique because the extracted pain defining parameters are highly sensitive to pain.
One point to be kept in mind is that to achieve high accuracy, noise and artifacts must be removed to
the maximum possible extent from the EEG signal.
Fuzzy inferencing is probably the best classification technique when the data is limited. In fact,
more accurate classification techniques like neural networks or tree based methods would not achieve
better results here because there is no Gold standard method available for pain measurement which
can help learning of these classification methods. Impreciseness of the pain scale having rating from 0-
10 from no pain to high pain goes well with Fuzzy inferencing. The designed system can be used in
Operation Theatre (OT) so that underdose or overdose of analgesic dose can be avoided. It may also
be used by the physicians to diagnose and treat diseases associated with pain. Subjects with chronic
pain should be separately investigated since EEG recording in the non-pain state is not accessible in
such cases.
Further work is required in this area to investigate the efficacy of the technique in case of
different pain sources. Before testing the system clinically, it must be evaluated by simulating pain
externally through different methods e.g. with the help of a pin, by giving thermal or mechanical stress
to the human body, etc.
5. Acknowledgment
The authors would like to thank Director, National Institute of Technical Teachers Training and
Research Chandigarh, India for his inspiration and support throughout this research work.
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