Analysis of the Relationship between Electrodermal Activity and

Heart Rate with Pain in Individuals with a Shoulder Pathology

M. Oliveira

, C. Quintão

1,2

, R. Vigário

1,2

, B. Mendes

, C. Caldeira

, F. Rodrigues

and C. Quaresma

1,2

Departamento de Física, Faculdade de Ciências e Tecnologias, Universidade Nova de Lisboa,

2829-516 Monte da Caparica, Portugal

Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física,

Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Monte da Caparica, 2892-516, Caparica, Portugal

Área de Medicina Física e Reabilitação, Hospital Curry Cabral, Centro Hospitalar Lisboa Central, Portugal

Keywords: Electrodermic Signal, Heart Rate, Pain, Shoulder, Signal Processing.

Abstract: Currently, pain analysis in a clinical environment is not common and is at fault for being subjective and

always dependent on a personal response. Therefore, it is imperative to use physiological signals to quantify

pain and make diagnosis more objective. This article aims to study the relationship between pain, through its

analog scale, with the electrodermal and cardiac signals of individuals characterized by having a shoulder

pathology that gives rise to recurrent pain. This study was carried out on 21 patients from Hospital Curry

Cabral, who were part of the Occupational Therapy department’s care in the area of Physical Medicine and

Rehabilitation, and 18 individuals without any pathology, thus serving as a control group. All participants

followed an experimental protocol consisting in the measurement of electrodermal and cardiac signals and

pain level when performing two different movements. The results suggest that there is indeed a relationship

between the two measured signals and pain. The greater the pain experienced by the individual, the greater

the amplitude of the electrodermic signal and heart rate appears to be.

1 INTRODUCTION

Over the years, due to various technological

advances, a new ability to obtain and process

physiological signals has emerged. It is thanks to

these technological advances that it is possible to

deepen the knowledge on numerous pathologies and

consequently improve the clinical diagnosis. Several

innovations in technology are responsible for this

improvement, from new physiological signal

acquisition devices to new signal processing tools.

However, there are still many pathologies that lack

an easy and objective clinical diagnosis, as is the case

of orthopedic diseases. This is a consequence of the

diagnosis not being made by collecting and analyzing

physiological signals. Many of these pathologies can

originate in the way people live their daily lives, from

the physical activity performed to their job. Although

physical exercise has great benefits for both physical

and mental health (Warburton et al., 2006), excessive

or incorrectly performed physical activity can lead to

orthopedic injuries (Gabbett, 2016). Jobs that require

repetitive movements or high physical strain, such as

jobs in construction or factories using the assembly

line system, can lead to such pathologies, particularly

in the shoulder (Mitchell et al., 2005), having already

been a concern performing a rotation of workers in

different positions on the assembly lines, so as to vary

the type of movements made by them. It is the

difficulty in diagnosing these pathologies that

motivates the development of new techniques and

technologies in order to make the diagnosis easier

and more objective. These conditions often make a

healthy and pain-free life impossible, so it is

imperative that solutions be found for their correct

diagnosis. However, the quantification of pain is

subjective, since it uses analog scales and depends on

each individual's perception of pain. This leads to

subjective and inaccurate diagnoses (Kandel et al.,

2000). Thus, the correct quantification of pain, in a

non-subjective way and based on physiological signs,

becomes essential. The Nervous System of an

110

Oliveira, M., Quintão, C., Vigário, R., Mendes, B., Caldeira, C., Rodrigues, F. and Quaresma, C.

Analysis of the Relationship between Electrodermal Activity and Heart Rate with Pain in Individuals with a Shoulder Pathology.

DOI: 10.5220/0008945001100117

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 1: BIODEVICES, pages 110-117

ISBN: 978-989-758-398-8; ISSN: 2184-4305

individual after experiencing pain, namely the

Sympathetic Nervous System, produces a change in

sweat excretion and heart rate, and these changes are

translated into the electrodermal signal (EDA)

(Ströfer et al., 2015) and the electrocardiogram

(ECG) (Shaffer et al., 2014), respectively.

Thus, this study aims to collect these signals and

correlate them with an analog pain scale, always

aiming to make the quantification of pain more

objective.

2 MATERIALS AND METHODS

The study was approved by the Portuguese Ethics

Committee of Hospital Curry Cabral, in Portugal.

Each participating subject was informed about the

procedures and the objectives of the study, prior data

collection, and signed a consent form with this

information.

All data was collected, during 3 months, from a

cohort of patients, with a shoulder pathology that gives

rise to recurrent pain, attending the occupational

therapy department’s care in the area of Physical

Medicine and Rehabilitation in Hospital Curry Cabral.

This study was carried out on 21 patients and 18

individuals without any pathology, among the

student population of FCT-NOVA, and thus serving

as a control group.

Characterization of the Sample

The sample is easily divided in two different groups:

the patients group (P) and the healthy individuals

group (H). The P group is composed by 21 patients,

15 female and 6 male, with an average age of 64 ± 12

years old. The H group consists in 18 healthy

individuals, 11 male and 7 female, with an average

age of 24 ± 3 years old. Although the two groups have

different ages, as the data was processed separately

for the two samples, the authors consider that the

results remain valid.

2.1 Instruments

For data collection, the Biosignalsplux equipment, was

used. From the available sensors, an EDA sensor was

used to measure the electrodermal signal, an ACC

(accelerometer) sensor to assist in timing and an ECG

sensor for heart rate estimation. The ECG sensor has 3

channels and the EDA sensor has 2 where the

electrodes are attached after their fixation on the

individual. It is through these set channels / electrodes

that EDA and ECG are collected. Solid gel disposable

ECG electrodes with an easy contact with the skin

were used. The recording device collects the

physiological signals simultaneously, with a 16-bit

resolution and sampling frequencies of 1000 Hz. All

data is transmitted, via Bluetooth, from Biosignalsplux

to the computer for processing (Plux, 2019).

All signals were processed using program Matlab

R2017a.

2.2 Procedure

The team composed by biomedical engineers and

some occupational therapists at Hospital Curry

Cabral identified the movements as well as all the

steps to be performed during the protocol. The

experimental protocol always follows 3 sequential

steps: explanation of the experimental protocol;

electrode placement; acquisition of EDA, ACC and

ECG signals; signal analysis.

1) Explanation of the Experimental Protocol

Initially it is always explained to the participants the

purpose of the study and how data collection will be

performed in order to obtain the informed consent. If

the participant agrees to their collaboration in the

study, the informed consent is signed. After this first

step it is needed to fill out a short form designed to

characterize the individual (age, profession,

medication, etc.).

2) Electrode Placement

After filling in the form, follows the placement of the

electrodes. The two EDA sensor electrodes are

placed on the front of the hand, as shown in Figure 1.

The hand where the electrodes are placed will always

be the opposite of the arm that will make the

movements. Thus, the required movements do not

interfere with the measured signal, since the hand in

which it is recorded, was as static as possible. The 3

ECG electrodes are also placed, two on the chest and

one on the right foot next to the talus bone, as shown

in Figure 2. In the case of healthy individuals, the arm

that performs the movements always corresponds to

the dominant hand.

3) Acquisition of EDA, ACC and ECG Signals

A video with the exact duration of the collection was

created to assist in data acquisition. The video shows

which movements to perform and the moments in

which participants have to execute them. In the first

phase the participants are sitting at rest. After 1

minute and 30 seconds they perform the first

movement - shoulder flexion with elbow extension -

followed by a further period of 1 minute and 30

seconds at rest. After this second rest period the

Analysis of the Relationship between Electrodermal Activity and Heart Rate with Pain in Individuals with a Shoulder Pathology

111

participants perform the second movement - internal

rotation of the shoulder with elbow flexion. Both

movements are represented in Figure 3.

Figure 1 Example of placing the electrodes corresponding

to the EDA signal on the front side of a participant's hand

moments before a collection.

Figure 2: Example of placement of the electrodes

corresponding to the ECG signal on a participant's chest

and talus moments before a collection.

Figure 3: Representation of the two movements made

during data collection. On the left - shoulder flexion with

elbow extension, and on the right - internal shoulder

rotation with elbow flexion.

This process continues until there are 6 shoulder

flexions with elbow extension and 5 shoulder internal

rotations with elbow flexion, ending with a rest

period. After each movement, the participant is asked

the intensity of the pain they felt, using the Numerical

Pain Rating Scale (Hjermstad et al., 2011, and the

answer is added to the participant's form. This scale

is a subjective measure and consists of eleven equal

parts, numbered successively from 0 to 10

(Hjermstad et al., 2011). The patient is asked to make

the equivalence between the degree of pain and the

numerical score, with 0 corresponding to “no pain at

all” and 10 to “worst imaginable pain”.

Participants are also asked to exert a little more

effort on the last three movements to cause a slight

increase in pain. The timing of all the movements is

set by the ACC sensor. An up-down sensor rotation

indicates the beginning of a movement and a down-

up rotation indicates the end of a movement.

4) Signal Analysis

After collecting all data, the processing phase begins.

The first step is to smooth out the EDA signal as it

has some noise. For this purpose a sliding average

filter is applied. The window of this filter is 5 points

and the method used was the Savitzky-Golay

(Savitzky and Golay, 1964). The instants of the

beginning and ending of each movement are also

drawn in the same graph, using the values from the

ACC sensor. In Figure 4 is shown the result of this

processing.

Figure 4: Electrodermic signal (μS) of patient 03 and

moments of the beginning, marked in red, and the end,

marked in black, of the movements after the use of the

sliding average filter.

Figure 5: Representation of the features extracted from

EDA signal (Dawson et al., 2016).

The skin conductance responses were analyzed

using the following features: amplitude, rise time and

half recovery time. These features were extracted

through the determination of the maximum value of

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the stimulus response and the baseline level (Figure

5). The maximum value of the stimulus response is

always the highest amplitude value measured after

the movement and the baseline is the upward facing

concavity point always found on the left of the

highest amplitude point. The amplitude is the

difference between the maximum response value and

the baseline value. Rise time is calculated as the

difference between the time indices of the maximum

amplitude points and the baseline. The half recovery

time requires more calculation as it is necessary to

calculate the amplitude point at half height. This

point will have an amplitude equal to the difference

between the maximum value and half the amplitude

of the stimulus response and is always on the right of

the stimulus response. The half-recovery time is then

calculated by making the difference in time indices

between this point now found and the point of

maximum amplitude.

In order to carry out a study of the average EDA

responses of all sample, it was necessary to normalize

the collected signals. For each individual, the

amplitude of each response was divided by the

highest amplitude recorded. Thus obtaining for all

individuals amplitudes between 0 and 1.

For processing the ECG signal R-waves were

detected to create a graph showing heart rate as a

function of time, since heart rate is the inverse of the

time interval between consecutive R waves. Figure 6

shows a portion of an electrocardiogram collected

during the performance of the protocol. These were

detected with the help of the Matlab findpeaks

function, using 20 points as the minimum peak

distance and 5 times the average of the prominence

of all peaks as minimum peak height options.

Following the creation of this graph and due to

misidentification of R waves, signal smoothing is

performed, thus eliminating false R waves. (see

Figure 7). This smoothing was performed with the

medfilt1 function, which applies a 10

order median

filter to the signal.

Following the creation of the latter graph, and

similar to what was done with the electrodermal

signal, the maximum value of the stimulus response

and the baseline values are extracted so that the

amplitude can be calculated. Since the start and end

times of all movements are the same on the HR graph

and EDA graph, it is easy to identify the heart

response to movement and do that to pain.

Regarding HR study, one is interested in the

difference between the peak value of HR response to

movement / pain and the basal value found

immediately before the response to movement (DIF

(HR)). Similarly to the EDA approach, also this

difference is normalized by the highest difference HR

obtain for each subject.

Figure 6: Electrocardiogram of patient 02 recorded during

data collection. R Waves are highlighted.

Figure 7: Heart rate as functions of time, a) original and b)

smoothed. Graphs relative to patient 02.

3 RESULTS AND DISCUSSION

The results obtained in this study are divided into two

parts. The first one is about the healthy individuals

group and the second one about the patients group.

3.1 Healthy Group

The first important idea to mention is that even

without causing pain, movements performed during

the experimental protocol cause an increase in the

EDA signal, as expected. Figure 8 shows a column

chart for one healthy participant, showing the

amplitude of the EDA along the sequence of

movements.

Figure 8: Electrodermic signal amplitude of healthy

individual 01 along the sequence of movements.

It is apparent that the amplitude of the

electrodermic signal is higher in the first movements

and then gradually decreases. This response behavior

is thought to be related to some subjective factors.

Namely, the stress / surprise caused by starting a new

task This behavior is observed in about one third of

healthy participants, with the rest showing similar

Analysis of the Relationship between Electrodermal Activity and Heart Rate with Pain in Individuals with a Shoulder Pathology

113

amplitude throughout the protocol or, in rare cases,

sporadic increases. The latter may be due to factors

outside the experimental protocol (room temperature

and involuntary auditory stimuli, for example).

Due to the gradual decrease mentioned above and

considering that the protocol starts with shoulder

flexion and elbow extension, the average response

amplitude is expected to be greater in this movement

(see Figure 9). Taking that into consideration, it is

also relevant to note that the amplitude of the

stimulus response is not movement dependent, in the

heathy group.

Figure 9: Mean amplitude of the electrodermic signal of

healthy patient 01 for both movements.

Figure 10: Normalized mean amplitude of the

electrodermic signal of all healthy participants for the two

movements performed.

Figure 11: Heart rate amplitude of healthy individual 17 for

both movements.

This idea is also corroborated by the graph shown

in Figure 10, where the mean and standard deviation

of the normalized signals for each movement were

shown.

Similar to the EDA signal, heart rate also

increases after a movement. Figure 11 depicts the

heart rate column graph along the sequence of

movements in one subject with typical behavior.

In Figure 11 it is evident that there is a clear

difference in HR amplitude relative to both

movements. It is an effect manifested in more than

half of participants without pathology. The others

have an average difference in HR amplitude similar

between the two movements. Thus, when all these

differences for all individuals without pathology are

normalized, the shoulder rotation movement with

elbow flexion presents an arithmetic average superior

to the shoulder flexion movement with elbow

extension (see Figure 12). A possible explanation for

this effect could be that the shoulder rotation

movement with elbow flexion requires more physical

effort.

Figure 12: Normalized HR mean amplitude of all healthy

participants for both movements performed.

3.2 Patients Group

For the patient group, an analysis similar to the

previously presented one, the non-pathological

group, was performed. In addition, the patient group

included information regarding the pain score.

Figure 13 shows the amplitude of the EDA along

the sequence of movements, for patient 20.

Figure 13: Amplitude of the electrodermic signal of patient

20 along the sequence of movements.

In Figure 13 it is clear that the amplitude of the

EDA does not remain constant and does not gradually

increase or decrease along the sequence of

movements, as it was observed in the healthy group.

This effect is present in 90% of participants with

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pathology. This difference from what happens to

individuals without pathology could be explained by

the pain felt when performing the movements.

In order to understand how pain influences the

electrodermal signal, graphs of the amplitude of the

EDA as a function of pain score were created for all

individuals with pathology (see Figure 14 for) patient

20.

Figure 14: Electrodermic signal amplitude of patient 20 as

a function of pain score.

In Figure 14 it is evidenced that higher pain levels

correspond to higher EDA amplitudes. However, we

observed that in the case of patient 12, among others,

this effect is not so easily visible (see Figure 15).

Since the difference between consecutive pain levels

is very difficult to distinguish, it was decided to

divide the pain scale into three classes: minimal pain

(pain levels 0, 1, 2 and 3), average pain (pain levels

4, 5 and 6) and maximum pain (pain levels 7, 8, 9 and

10). Thus it becomes even more evident that when

the pain experienced is greater, the amplitude of the

EDA signal is also greater. Figure 16 show the graphs

of the amplitude of the EDA as a function of pain

score for patient 12, after the pain scale division.

Figure 15: Electrodermic signal amplitude of patient 12 as

a function of pain score.

Figure 16: Electrodermal signal amplitude of patient 12 as

a function of grouped pain score.

Taking into account all individuals with

pathology, a graph of the mean normalized

amplitudes was created as a function of the grouped

pain scores (see Figure 17).

Figure 17: Normalized mean amplitude of the

electrodermic signal of all participants with pathology as a

function of the grouped pain score.

Concerning all participants with pathology, in

only two the amplitude of the EDA as a function of

the grouped pain score shown a different behavior

than the one observed in Figure 17. It should be notes

that although the graph in figure 17 contains

information from these two patients and also from

patients who did not experience pain levels in the full

spectrum of the scale, the "greater pain - greater

amplitude" ratio of EDA is still clearly visible.

Regarding heart rate, an analysis very similar to

that of the electrodermal signal was performed.

Figure 18 shows the HR data along the sequence of

movements for patient 19. It is apparent that the

differentiation of HR data from the two movements

is no longer evident as it was for individuals without

Analysis of the Relationship between Electrodermal Activity and Heart Rate with Pain in Individuals with a Shoulder Pathology

115

pathology. This is true for all patients and can also be

explained by the existence of pain when performing

the movements. This means that the effect of pain

overlaps the effect of physical effort observed in the

control group.

Figure 18: Heart rate amplitude of patient 19 for both

movements.

Similarly to what was performed for the EDA, the

pain scale was divided into three parts and the HR

amplitude graphs were created as a function of the

grouped pain scores (for example, see Figure 19

related to patient 16.

As with the electrodermal signal, heart rate also

increases as pain experienced by patients increases,

however, this relationship is not as linear as that

which appears to be present in the EDA signal. This

is shown in Figure 20, which has the pain-related HR

information for all individuals with pathology.

Again, it was necessary to normalize all amplitudes

for all subjects individually, as before, and to do the

arithmetic mean and standard deviation for each

group of pain levels. As could be observed, the

maximum pain also corresponds to maximum heart

rate, but the amplitude corresponding to the

minimum pain is slightly greater than that

corresponding to the average pain.

Figure 19: Heart rate amplitude of patient 16 as a function

of grouped pain score.

Figure 20: Normalized mean amplitude of the heart rate of

all participants with pathology as a function of the grouped

pain score.

4 CONCLUSIONS

Through the analysis of the obtained data it can be

concluded that there is a pain-EDA as well as a pain-

HR relationship. For both EDA and HR, the greater

the pain experienced by the individual, the greater the

amplitude of the respective signal. This is a clearly

observable relationship when comparing only low

and high pain scores.

In the future, it will be important to have a larger

and more homogeneous sample, in terms of age as

well as in pain scores. It would be, also, interesting to

conduct a similar study in individuals with

pathologies located elsewhere, for example in the leg,

or even of a different pathologies, for example,

neurological disorders.

Thus, it is important to continue the study of the

relationship between pain and physiological signs in

order to achieve stronger conclusions. Once

stablished a clear relationship between pain scores

and physiological signals, the clinicians will be able

to access an objective tool of diagnosis and

intervention.

ACKNOWLEDGEMENTS

The authors would like to thank all the patients who

participated in this study and all the staff of Hospital

Curry Cabral, specifically those who work in the

Occupational Therapy department in the area of

Physical Medicine and Rehabilitation, since without

them this study would not have been conducted.

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