Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study

Abstract

Accurate diagnosis and classification of epileptic seizures can greatly support patient treatments. As many epileptic seizures are convulsive and have a motor component, the analysis of muscle activity can provide valuable information for seizure classification. Therefore, this paper present a feasibility study conducted on healthy volunteers, focusing on tracking epileptic seizures movements using surface electromyography signals (sEMG) measured on human limb muscles. For the experimental studies, first, compact wireless sensor nodes were developed for real-time measurement of sEMG on the gastrocnemius, flexor carpi ulnaris, biceps brachii, and quadriceps muscles on the right side and the left side. For the classification of the seizure, a machine learning model has been elaborated. The 16 common sEMG time-domain features were first extracted and examined with respect to discrimination and redundancy. This allowed the features to be classified into irrelevant features, important features, and redundant features. Redundant features were examined with the Big-O notation method and with the average execution time method to select the feature that leads to lower complexity and reduced processing time. The finally selected six features were explored using different machine learning classifiers to compare the resulting classification accuracy. The results show that the artificial neural network (ANN) model with the six features: IEMG, WAMP, MYOP, SE, SKEW, and WL, had the highest classification accuracy (99.95%). A further study confirms that all the chosen eight sensors are necessary to reach this high classification accuracy.

Full text

Citation: Djemal, A.; Bouchaala, D.;
Fakhfakh, A.; Kanoun, O. Wearable
Electromyography Classification of
Epileptic Seizures: A Feasibility
Study. Bioengineering 2023,10, 703.
https://doi.org/10.3390/
bioengineering10060703
Academic Editors: Andrea Cataldo,
Giuseppe Baselli and Kevin J. Otto
Received: 4 May 2023
Revised: 29 May 2023
Accepted: 7 June 2023
Published: 9 June 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
bioengineering
Article
Wearable Electromyography Classification of Epileptic Seizures:
A Feasibility Study
Achraf Djemal 1,2 , Dhouha Bouchaala 3, Ahmed Fakhfakh 2and Olfa Kanoun1,*
1Measurement and Sensor Technology, Chemnitz University of Technology, Reichenhainer Straße 70,
09126 Chemnitz, Germany
2Laboratory of Signals, Systems, Artificial Intelligence and Networks, Digital Research Centre of Sfax,
National School of Electronics and Telecommunications of Sfax, Technopole of Sfax, Ons City 3021, Tunisia
3National Engineering School of Sfax, University of Sfax, Route de la Soukra km 4, Sfax 3038, Tunisia
*Correspondence: olfa.kanoun@etit.tu-chemnitz.de; Tel.: +49-371-531-36931
Abstract:
Accurate diagnosis and classification of epileptic seizures can greatly support patient
treatments. As many epileptic seizures are convulsive and have a motor component, the analysis
of muscle activity can provide valuable information for seizure classification. Therefore, this paper
present a feasibility study conducted on healthy volunteers, focusing on tracking epileptic seizures
movements using surface electromyography signals (sEMG) measured on human limb muscles.
For the experimental studies, first, compact wireless sensor nodes were developed for real-time
measurement of sEMG on the gastrocnemius, flexor carpi ulnaris, biceps brachii, and quadriceps
muscles on the right side and the left side. For the classification of the seizure, a machine learning
model has been elaborated. The 16 common sEMG time-domain features were first extracted and
examined with respect to discrimination and redundancy. This allowed the features to be classified
into irrelevant features, important features, and redundant features. Redundant features were
examined with the Big-O notation method and with the average execution time method to select the
feature that leads to lower complexity and reduced processing time. The finally selected six features
were explored using different machine learning classifiers to compare the resulting classification
accuracy. The results show that the artificial neural network (ANN) model with the six features:
IEMG, WAMP, MYOP, SE, SKEW, and WL, had the highest classification accuracy (99.95%). A further
study confirms that all the chosen eight sensors are necessary to reach this high classification accuracy.
Keywords:
epilepsy diagnosis; seizures classification; machine learning; features extraction; features
selection; surface electromyography (sEMG); Big-O notation; wearable systems
1. Introduction
One of the most prevalent neurological disorders is epilepsy, which affects about
50 new persons per 100,000 annually [
1
]. Unexpected and unprovoked seizures are a symp-
tom of this complex neurological condition brought on by abnormally high or synchronized
neuronal activity in the brain. A seizure results from the brain’s nerve cells firing out of
control, and it might cause a convulsion, minor physical symptoms, mental confusion,
or a mix of symptoms. A psychogenic non-epileptic seizure (PNES), which resembles an
epileptic seizure but lacks the distinctive electrical discharges associated with epilepsy, is
one example of a non-epileptic event that can be distinguished from an epileptic event using
an epileptic seizure. Persons of all ages are impacted by the chronic, non-communicable
brain disorder known as epilepsy. According to the World Health Organization (WHO),
over 50 million persons worldwide (or about 1% of the population) suffer from epilepsy,
with the majority of them living in developing countries [
2
]. According to the latest WHO
data published in 2020, 0.43% of total deaths were caused by epilepsy in Lesotho. Mortality
among people with epilepsy is up to three times higher than for the general population [
1
].
In addition, the annual rates of epilepsy misdiagnosis are still stubbornly high, which range
Bioengineering 2023,10, 703. https://doi.org/10.3390/bioengineering10060703 https://www.mdpi.com/journal/bioengineering

Bioengineering 2023,10, 703 2 of 18
from 2% to 71% [
3
]. For example, in the case of therapeutic epilepsy diagnosis in clinical
practice, as well as drug trials, the seizure diagnosis is based on a self-reporting approach.
This remains largely unreliable, where 47–63% of seizures are unrecognized by patients,
and this is even higher (86%) for nocturnal seizures [4].
The treatment gap may be one of the reasons contributing to the higher mortality
rate [
1
]. It is estimated by the WHO that up to 70% of persons living with epilepsy could
live seizure-free if diagnosed and treated appropriately. For epilepsy diagnosis, a few
marketed devices for epilepsy monitoring using surface EMG sensors are presented. The
most common ones are non-invasive, wearable, and used as seizure detection systems.
In [
5
], Conradsen et al. suggest a method for detecting generalized tonic-clonic (GTC)
seizures. This algorithm has been modified and implemented in a small sEMG wireless
device developed by DELTA, Denmark, on behalf of IctalCare A/S, Denmark. The wireless
device for sEMG measurements was attached to the tibia muscle. The final results show a
mean detection rate of 57%. According to reports, the device only sets hidden alarms; thus,
medical workers are not aware of the times of the alarms. In [
6
], Bruno et al. highlight the
SPEAC device used for adjunct seizure monitoring for adults. The device is positioned on
the belly of the biceps muscle to analyze sEMG signals that may be associated with GTC
seizures. The authors report that during the study, it became clear that the device had been
improperly placed and was not correctly attached to the belly of the biceps, resulting in
a detection rate of 76%. Whitmire et al. [
7
] employed the same system to identify GTCSs,
and it was noted that the false-alarm rate ranged from 0.3 to 0.5 each day. There was
no information on the seizure detection rate in this group compared to seizure diaries.
SeizureLink, another surface EMG-based seizure detector utilized by Beniczky et al. [
8
],
was formerly known as the Epileptic seizure Detector Developed by IctalCare (EDDI). The
system needs to be fastened to the patient’s biceps and wirelessly connects to other devices
to provide real-time alarms in the case of convulsive seizures and reach a detection rate of
93.8%. Most existing systems achieve acceptable performance in terms of sensitivity for
detecting GTC seizures based on sEMG signals [9].
Detecting seizures is important for patients and their caregivers because it provides
an opportunity for intervention. But, a gap for current devices is that they are tasked
with detecting only generalized tonic-clonic seizures (binary classification). Although, it is
essential to determine the type of seizure to guarantee an appropriate sufficient diagnosis
and therapy.
Identifying the type of seizure, although sometimes difficult, is done through clinical
observation with reference to patients’ medical history and demographic information. It is
supported by general brain imaging techniques, such as electroencephalograph (EEG), mag-
netoencephalography (MEG), and functional magnetic resonance imaging (fMRI) [
10
,
11
].
Apart from these clinical observations, there is currently no portable device to assist in
the classification of seizure types. Devices for seizure classification offer more precise
seizure quantification, enabling doctors to customize treatment more objectively. Aside
from this, however, electroencephalography (EEG) coupled with video surveillance is
considered the most reliable and recognized analytical technique for diagnosing and clas-
sifying epilepsy [
12
,
13
]. Accurate seizure classification is important for patients, families,
researchers, and medical professionals who care for persons with epilepsy and influences
medication selection [
14
,
15
]. The accurate identification of the seizure type is challenging
because of numerous factors. First, the clinical and EEG signs of different seizure types are
similar. Even for a highly experienced neurologist, it can often be difficult to distinguish
between focal and generalized seizures [
3
,
16
]. Second, in some cases, long-term monitoring
(sometimes referred to as video-EEG monitoring) is required, and it may last for days
to analyze these enormous records manually. Additionally, signal interpretation has a
notoriously low inter-rater agreement, which is entirely dependent on the expert’s level
of experience. Further, the inter-subject variability results in a variety of symptoms of
the same type of seizures [
17
]. Moreover, patients must wear scalp electrodes and remain
attached to EEG equipment during monitoring, which increases artifacts, is impractical,

Bioengineering 2023,10, 703 3 of 18
and potentially leads to stigmatization and discomfort [
1
,
18
]. In recent clinical studies, re-
searchers have explored different methods for monitoring epileptic seizures. In level 3
[19]
,
and level 4
[20]
clinical studies recordings of EEG, 3D-accelerations and angular velocity
have been used for monitoring epileptic seizures. Combining these different types of data
can provide a more comprehensive view of the seizure and potentially improve diagnostic
accuracy and treatment options. In this paper we pursue the same research path towards
monitoring epileptic seizures and propose a new method based on wearable wireless EMG
sensors monitoring the muscle activity and seizure classification method based on machine
learning techniques. The suggested method involves multiple measurements of surface
EMG data to categorize epileptic seizures according to typical seizure movements. This
study highlights the technical nature of the research and emphasizes its preliminary nature
as a feasibility study on healthy volunteers before progressing to clinical trials.
The paper is organized mainly into six sections. Section 2presents an overview of
epileptic seizure types and sensors used for an epilepsy diagnosis. Section 3is devoted to
the implementation of the wireless sensor node, data collection, and description. Section 4
highlights the processing steps to classify seizures, including feature extraction, selection
step, and evaluation. Section 5introduces different machine learning algorithms for classi-
fying seizures and provides an evaluation of their performance. Finally, the conclusions of
the work are presented in Section 6.
2. Epileptic Seizure Types and Sensors Used for the Diagnosis
Two main groups of epileptic seizures, which are focal and generalized, are presented
in Figure 1according to the International League Against Epilepsy (ILAE) categorization
strategy for epileptic seizure classifications [
21
]. When abnormal electrical activity begins
in one area of the brain, it is called a focal or partial seizure; however, generalized seizures
begin on both sides of the brain [
21
]. The terms “motor ” and “non-motor” are also used
when describing seizure types. According to the Epilepsy Foundation, motor relates to
physical movement or motion, and seizures involving motor activity may either increase
or decrease muscle tone, leading to muscle twitches, jerks, or contractions. Non-motor
onset seizures don’t involve muscle action but may include behavioral, emotional, and/or
sensory activity or actions.
Figure 1. Epileptic seizure types.

Bioengineering 2023,10, 703 4 of 18
2.1. Epileptic Myoclonic Seizure
Myoclonic seizures are characterized by sudden, brief muscle contractions or twitches.
The term “myoclonic” comes from the word “myo,” meaning muscle, and “clonus,” mean-
ing rapidly alternating contraction and relaxation [
22
]. These seizures typically last for
less than a couple of seconds [9]. They may occur singularly or in clusters. Some forms of
epilepsy are referred to as syndromes due to their distinct signs and symptoms [
23
]. The
type of seizures, age of onset, gender, behavior, and results from medical investigations
and genetic testing may all be considered by doctors, as noted by the epilepsy foundation.
Myoclonic seizures are more observed in the case of children, but can also occur in the
case of adults as well. In fact, some individuals may continue to experience these types
of seizures into adulthood, especially if they have an underlying neurological condition
that predisposes them to this type of seizures
[24]
. Understanding whether a person’s
epilepsy is linked to a syndrome can help in determining if their seizures can be con-
trolled and in selecting the most appropriate diagnostic approach, either physiological or
non-physiological [21].
Epilepsy patients can experience myoclonic seizures that result in coordinated, un-
usual movements across both sides of their body. These seizures can appear in a variety of
epilepsy syndromes, each with its own unique characteristics, such as juvenile myoclonic,
Lennox-Gastaut, and progressive myoclonic. The seizures associated with juvenile my-
oclonic syndrome typically affect the neck, shoulders, and upper arms and often occur
shortly after waking up. Lennox-Gastaut syndrome is characterized by seizures that can
be severe and difficult to control and affect the neck, shoulders, upper arms, and some-
times the face. Unfortunately, treatment for progressive myoclonic syndrome is typically
ineffective as the condition tends to worsen over time and is not often seen.
2.2. Epileptic Tonic Seizure
Generalized tonic seizures are defined by the simultaneous tonic extension of both
upper and lower limbs, giving the appearance of “decerebrate” posturing, as well as the
simultaneous tonic flexion of the upper limbs and extension of the lower limbs, resembling
“decorticate” posturing. They may also be accompanied by tremors in the extremities,
according to [
1
]. The classification assumes that tonic activity is not followed by clonic
movements. Tonic seizures are brief episodes, typically lasting less than 60 s, during which
there is a sudden increase in muscle tone in the extensor muscles. They are generally
of longer duration than myoclonic seizures and may also occur in the case of adults,
particularly if they have a neurological condition that makes them more susceptible to
these seizures [24].
Tonic seizures are commonly seen in patients with Lennox Gastaut syndrome and have
been classified into four types: axial, axorhizomelic, global, and asymmetric. Axial tonic
seizures are marked by a tightening of the neck muscles that causes the head to be held
upright, the eyes to be wide open, and the jaw to clench or the mouth to open. This type
of seizure is also accompanied by contraction of the respiratory and abdominal muscles,
which may result in a high-pitched cry and brief pauses in breathing. Axorhizomelic
seizures resemble axial tonic seizures, but the tonic contractions extend to the proximal
muscles of the upper limbs, causing the shoulders to raise and the arms to be abducted.
Global seizures are characterized by tonic contractions that affect the peripheral muscles
of the limbs, causing the arms to be raised and clenched in front of the head, creating a
defensive posture. Asymmetric tonic seizures can range from a slight head rotation to a
tonic contraction of all the muscles on one side of the body.
2.3. Surface Electromyography (sEMG) and Quantity Analysis
Muscle movement is made under the control of our brain [
25
]. Thus, the electrical
activity of muscles is very closely related to the nervous system. The brain produces an
action potential, which passes through the nerve fibers. This action potential that passes
through the nerve fibers will stimulate the muscle fibers. Motor neurons transmit electrical

Bioengineering 2023,10, 703 5 of 18
signals that cause muscles to contract. This causes the movement of the muscles. The
electric potential from the muscles, which is represented in the form of a time-varying signal,
is known to be the electromyography (EMG) signal [
26
]. Surface EMG (sEMG) is among
the most promising physiological signals in the health monitoring field due to its flexibility,
non-invasive method, large recording region, and high-quality measurement, which are
essential properties for numerous clinical applications such as epilepsy diagnosis [
27
]. It is
well demonstrated that the amplitude of the EMG signal is random and can be reasonably
represented by a Gaussian distribution function. EMG’s amplitude is quite small. When
the muscle does not contract, the amplitude of the EMG signal is generally in the range of
[80 mV–90 mV]. However, the muscle contraction amplitude is only a few hundred mV at
most [
28
]. So, in order to acquire an observable signal, the EMG signal is often amplified
by 50–100 times to reach above 1–2 volt [26].
Muscles are the endpoints of the common final neural pathways involved in motor
seizures. Thus, surface EMG signals provide valuable information on the Central Nervous
System (CNS) activity during epileptic seizures [
29
]. Up to now, no data on quantitative
EMG features during tonic or myoclonic seizures has been published. We hypothesized that
quantitative EMG features would distinguish between the tonic and myoclonic phases. In
addition, we also wanted to compare these phases to the normal state (no seizure) when no
movement is simulated (EMG recording of the normal muscle activation in the rest position
of the subject). Assessment of the EMG signals showed that the quantitative analysis of
muscle activation differs from epileptic seizures and convulsive Psychogenic Non-Epileptic
Seizures (PNES), even when both types of episodes occur in the same subject [
29
]. For that,
the subject’s movements can be distinguished during both episodes. The tonic phase was
characterized by a marked increase in amplitude-derived parameters; tonic seizure had
a marked increase in frequency compared to myoclonic seizure for all muscles and was
more straightforward for the lower limb muscles. Moreover, the coherence between the
homologous muscles on the left and right sides was higher than during voluntary muscle
activation [
30
–
34
]. Based on the quantitative analysis of the EMG signal, surface EMG
proved to be an efficient tool for the classification of the specific dynamic evolution of tonic,
myoclonic, and no-seizure movement activity [8,26,35].
For this purpose, a surface EMG dataset is recorded as a first step using a wearable
sensor node for tonic, myoclonic, and no-seizure classification. This is followed by a
processing step which includes feature extraction and selection methods. Finally, the
development of several machine learning algorithms will be described, followed by an
evaluation. Figure 2. highlights different blocs used for epilepsy diagnosis, and each bloc
will be detailed in the next sections.
Figure 2. A framework for epileptic movement classification.

Bioengineering 2023,10, 703 6 of 18
3. Materials and Methods
3.1. System Design
A full control system was developed with high resolution, real-time response, wireless,
compact, and high sensitivity insured by WiFi communication with the ESP32 board and a
local host (Figure 3a). The components of the proposed prototype are shown in Figure 3b.
The system consists of a myoware sensor that converts the surface EMG signal into an easily
readable format by measuring, filtering, and rectifying the recorded EMG data. Ag/AgCl
electrodes with a 10 mm diameter on self-adhesive supports are used. The recorded sEMG
data is transmitted to the ESP32-WROOM-32D microcontroller and then converted to a
12-bit analog-to-digital converter (ADC). A rechargeable Li-ion battery with a capacity of
2400 mAh, 3.7 V, and 8.9 Wh is used as a power supply for all components. The wireless
node can continuously transmit raw data for up to 12 h. All components can perfectly
fit into the textile hand band with a system length, width, and height equal to 50.5 mm,
38.6 mm, and 33.6 mm, respectively.
(a) (b)
Figure 3.
Proposed measurement system. (
a
) Proposed Prototype; (
b
) Prototype specification circuit.
3.2. sEMG Electrodes Placement
Electrode placement has a noticeable influence on the quality of the measurement,
which imposes the necessity to investigate this factor. Commercial Ag/Agcl gel-based
electrodes were used to facilitate electrochemical reactions and reduce the skin-electrode
interface impedance (less than 10 K
Ω
) [
36
]. The considered electrodes permit the charges
to pass through the skin-electrode interface without hindrance, which helps the reduction
of the signal-to-noise ratio for the recorded biological signals. Furthermore, their low
resistivity will help to determine local changes in the impedance of a specific muscle group
and prevent overflow of electrical stimulation to other muscle groups [
37
]. The electrodes
were placed in a longitudinal position regarding the muscle fibers to decrease the effect of
the subcutaneous fat layer traversed by the current [26].
The placement of the proposed wireless sensor node is presented in Figure 4. sEMG
electrodes are placed at the recommendation of the Department of child neurology at Hos-
pital Hedi Chaker of Sfax in Tunisia. For that, Ag/Agcl electrodes are placed at a specific
position regarding the epileptic seizure movement chosen to be detected. The gastrocne-
mius flexor carpi ulnaris, biceps brachii, and quadriceps muscles are the selected position
for No-seizure, Myoclonic, and Tonic seizure movements distinguish and classification.

Bioengineering 2023,10, 703 7 of 18
Figure 4.
sEMG electrodes placement to classify no-seizure, myoclonic, and tonic seizure movements.
3.3. sEMG Dataset Description
In order to obtain a sufficient EMG dataset for the classification of the selected epileptic
activity motion, 20 healthy subjects simulated tonic, myoclonic, and no-seizure movements.
Tonic seizures are characterized by extension of both upper and lower extremities, flexion of
upper extremities, and extension of lower extremities. These movements are simulated by
having subjects guided by a trainer after watching video recordings of real examples of tonic
movements. The videos have been provided by the hospital Hedi Chaker, Sfax, Tunisia.
The trainers were asked to correct how they activated the muscles if necessary. Selected
subjects belong to the same generation and are aged between 24 and 31, as illustrated in
the Table 1.
Table 1. Selected subjects specification.
Subject Gender Age Weight
(kg)
High
(m) Subject Gender Age Weight
(kg)
High
(m)
1 male 23 63 1.83 11 female 26 66 1.68
2 male 24 70 1.85 12 female 24 92 1.86
3 male 27 63 1.75 13 female 24 62 1.64
4 male 25 84 1.78 14 female 25 65 1.70
5 male 25 81 1.77 15 female 25 61 1.64
6 male 25 74 1.83 16 female 24 58 1.72
7 male 25 82 1.78 17 female 27 75 1.71
8 male 24 97 1.80 18 female 27 62 1.68
9 male 27 63 1.83 19 female 24 53 1.62
10 male 26 89 1.82 20 female 26 58 1.76
Figure 5shows an example of the recorded row sEMG signal for no-seizure, myoclonic,
and tonic phase motions. The difference in muscle contraction strength results in a differ-
ence in frequency and amplitude range for the three movements. Selected subjects were
asked to avoid the direct effect of alcohol and caffeine on muscle contraction by the increase
in calcium permeability. They were prohibited from consuming any source of caffeine and
alcohol for at least 6 h before the test [
37
]. They were also asked to fast and stop drinking
water for at least 2 h from the beginning of the test and until the end to eliminate the
significant change in the bio-impedance quantity caused by food or fluid ingestion [
37
]. In
the same direction, the measurements were performed for each volunteer under the same
conditions, e.g., position and measurement duration. After 10 s of maximal contraction
in all muscles for the tonic phase, the subjects simulated the myoclonic movement for 2 s
with successive epochs of maximal contraction and relaxation in the upper limb muscles.
Each subject was asked to simulate ten episodes, with two minutes of rest between trials to
avoid muscle charging and ten minutes between motion measurements to avoid muscle

Bioengineering 2023,10, 703 8 of 18
fatigue. The four episodes closest to resembling a tonic, myoclonic, or no-seizure motion
were chosen for further analysis.
(a)
(b)
(c)
(d)
Figure 5.
Recorded sEMG signal representation of No-seizure, Myoclonic, and Tonic phases motions.
(
a
) Recorded sEMG signal from gastrocnemius muscle; (
b
) Recorded sEMG signal from quadriceps
muscle; (
c
) sEMG signal from flexor carpi ulnaris muscle; (
d
) Recorded sEMG signal from biceps
brachii muscle.

Bioengineering 2023,10, 703 9 of 18
4. Data Processing
4.1. Feature Extraction
Surface EMG signals have the properties of non-stationary and non-linear signals,
making them unusable as raw signals [
38
,
39
]. As a result, when these raw signals are used
as inputs in sEMG classification, the classifier’s efficiency decreases. In order to improve
the performance of the classifier, researchers are using different types of EMG features [
40
].
Feature extraction transforms short time windows of the raw EMG signal to generate
additional information and improve information density [26,27].
During the past decades, numerous different EMG feature extraction methods based
on the time domain, frequency domain, and time–frequency domain information have
been proposed and explored [
8
,
39
]. In general, features in this group are used to detect
muscle contraction, muscle action, and onset detection. Sixteen Time Domain Features
(TDF) are selected for myoclonic and tonic seizures classification, including Integrated
Electromyogram (IEMG), Mean Absolute Value (MAV), Mean Absolute Value 1 (MAV 1),
Mean Absolute Value 2 (MAV 2), Sample Square Integral (SSI), Variance (VAR), Temporal
Moment (TM), Root Mean Square (RMS), LOG detector (LOG), Waveform Length (WL),
Zero Crossing (ZC) [
32
], Myopulse Percentage Rate (MYOP), Willison Amplitude (WAMP),
Kurtosis (KURT), Skewness (SKEW), and Shannon Entropy (SE) [
31
–
34
]. The described
features in Table 2, are the most commonly used ones for EMG data processing [27,31,41].
Prior to feature extraction, min-max normalization is performed to compare features
initially with variant scales.The radar or spider plot (Figure 6) provide an interesting way
to visualize multiple variables in a single graph. This permits to investigate the degree
of similarity between multiple classes and their distinguishability. The charts in Figure 6
show the EMG features that provide non-redundant information and build the basis for
a principled and interpretable choice of EMG features [
30
]. To use the output of this
topological feature map selection and engineering, we can evaluate measures (such as class
separability and robustness) to select from the fundamental and most interesting feature
groups the best representative features [30].
The first plot in Figure 6a presents the Integrated EMG feature and the impact of this
feature to classify epileptic movements. Each axis presents a sensor (IEMG1, IEMG2,
. . .
,
IEMG8). From this figure, we can identify which sensor can better contribute to the classifi-
cation of the movements. For example, for the IEMG plot IEMG_S5 and IEMG_S8 have
almost the same value for myoclonic and tonic seizure and therefore, they cannot con-
tribute to differentiate between tonic and myoclonic seizure movements. On the other hand,
IEMG_S6 show big differences between myoclonic seizure, tonic seizure, and no-seizure
and can contribute well for seizure classification. By interpreting the different extracted
features, Figure 6a shows that the radar plots of the IEMG, MAV, MAV1, MAV2, RMS,
VAR, TM, LOG, and SSI present redundant information that could be a time-consuming
process when training a machine learning or deep learning model because the input to
the model depends on the number of extracted features. Also, this mutual information
can increase the complexity of a developed classifier. Figure 6b presents the radar plots of
the normalized irrelevant features: Kurtosis (KURT) and Zero Crossing (ZC) features. The
radar chart of the KURT and the ZC highlights irrelevant information, which means that
these features can not distinguish between myoclonic, tonic, and no-seizure movements.
As a result, the Kurtosis and Zero Crossing features should be removed. Figure 6c presents
the radar plots of the normalized relevant features: WAMP, MYOP, SE, SKEW, and WL.
These features can distinguish tonic, myoclonic, and no-seizure activities based on the
difference in action potential between the three epileptic movements.

Bioengineering 2023,10, 703 10 of 18
Table 2. Extracted Time Domain Features (TDF) from EMG signal.
Abbreviation Feature Equation
IEMG Integrated EMG
IE MG =∑N
k=1|Sk|,
Here N denotes the length of the signal and
Skrepresents the sEMG signal in a segment.
MAV Mean Absolute Value MAV =1
N∑N
k=1|Sk|
MAV 1 Mean Absolute Value 1
MAV1=1
N∑N
k=1ωn|Sk|,
ωk=1, 0.25N≤k≤0.75N
0.5, otherwise
MAV 2 Mean Absolute Value 2
MAV2=1
N∑N
k=1ωk|Sk|,
ωk=




1, 0.25N≤k≤0.75N
4k
N, 0.25N>k
4(k−N)
N, 0.75N<k
SSI Simple Square Integral SSI =∑N
k=1Sk2
VAR Variance VAR =1
N−1∑N
k=1Sk2
TM Temporal Moment TM =|1
N∑N
k=1Sk3|
RMS Root Mean Square R MS =q1
N∑N
k=1Sk2
LOG LOG detector LOG =e1/N∑N
k=1log|Sk|
WL Waveform Length W L =∑N−1
k=1|Sk+1−Sk|
ZC Zero Crossing
ZC =∑N−1
k=1[sgn(Sk·Sk+1)T|Sk−Sk+1| ≥ 0],
sgn(S) = 1, S≥threshold
0, otherwise
MYOP Myopulse Percentage Rate
MYOP =1
N∑N
k=1f(|Sk|)≥threshold,
f(S) = 1, S≥threshold
0, otherwise
WAMP
Willison Amplitude
WAMP =∑N−1
k=1f(|Sk+1−Sk|)>threshold
,
f(S) = 1, S≥threshold
0, otherwise
KURT Kurtosis KURT =1
N
∑N
k=1(Sk−µ)4
σ4
SKEW Skewness SKEW =1
N
∑N
k=1(Sk−µ)3
σ3
SE Shannon Entropy SE =−∑N
k=1Sklog(Sk)

Bioengineering 2023,10, 703 11 of 18
(a)
(b) (c)
Figure 6.
Radar chart of the normalized extracted features. (
a
) Redundant features; (
b
) Irrelevant
features; (c) Relevant features.
4.2. Feature Selection
Feature selection is an essential task in data analysis and information retrieval process-
ing [26]. It reduces the number of features by removing noise and extraneous data [33,42].
Highlighted features in Figure 6a present a similarity in information. Another feature
selection method should be performed to choose one feature with low computational time
and complexity. This study presents a new method called Big-O Notation, which compares
extracted features in terms of time complexity. Time complexity measures how long an
algorithm takes to run as a function of the input length. In the same way, space complexity
measures how much memory or space an algorithm requires to execute based on input
length. Several factors affect space and time complexity, such as the underlying hardware,
operating system, CPU, and processor. However, none of these factors are considered when
analyzing the algorithm’s performance. Figure 7a presents the Big-O notation chart. It
identifies functions according to their growth rates.

Bioengineering 2023,10, 703 12 of 18
(a) (b)
Figure 7.
Big-O time complexity chart based feature selection. (
a
) Big-O complexity chart; (
b
) Time
complexity.
Different levels of complexity are presented starting from the horrible state (functions
can be presented as
O(n!)
,
O(
2
n)
, or
O(n2)
), which is the highest time complexity level
until the excellent state (functions can be presented as
O(log n)
or
O(
1
)
) which represents
the lowest time complexity level. Based on Figure 7b, LOG, MAV1, and MAV2 present
high-level time complexity (bad state for LOG:
O(nlog n)
, Horrible state for MAV1 and
MAV2:
O(n2)
) compared to other features including the IEMG, MAV, VAR, SSI, RMS, and
TM, which presents the same time complexity level
O(n)
. Based on the Big-O feature
selection technique, LOG, MAV1, and MAV2 features should be removed. For the six
features with the same time complexity level, an average execution time for each feature
is used to keep only one feature with a low running time. After ten time trials, Figure 8
shows that the Integrated EMG feature presents the lowest running time compared to other
features, with an average execution time of 10.88 s. As a result, the IEMG feature will be
used for further processing.
Once the feature set has been evaluated in terms of similarity, insignificant information,
execution time, and complexity, six features, including the IEMG, WAMP, MYOP, SE, SKEW,
and WL, will be concatenated in the format of vectors and transmitted as inputs to different
machine learning classifiers to know the impact of selected features to differentiate between
epileptic seizure movements.
Figure 8. Average execution time per redundant features: IEMG, MAV, VAR, SSI, RMS, and TM.
5. Epileptic Movement Classification Based on Machine Learning Algorithms
Machine learning has proven to be effective in interpreting sEMG signals for different
purposes [
43
], such as to classify gestures [
44
], to detect muscle fatigue [
45
], to investigate
human–machine interaction [
46
], and in epilepsy diagnosis or monitoring [
47
,
48
]. The
possibility of adopting a machine learning approach that learns to interpret the shape of
the sEMG signals for assessing muscle-activation onset and offset seems to be a feasible

Bioengineering 2023,10, 703 13 of 18
solution [
49
]. Machine learning is often used as a suitable approach for signal processing.
Decision Tree (DT) [
50
], Random Forest (RF) [
51
], K-Nearest Neighbors (KNN) [
52
], and
Artificial Neural Network (ANN) [
53
,
54
], are the most commonly used classifiers [
33
,
55
].
These classifiers are developed and evaluated to classify no-seizure, tonic, and myoclonic
epileptic movements based on selected features.
5.1. Models Hyperparameter Setup
A hyperparameter is a parameter that measures the learning process using its value.
Hyperparameter optimization or tuning is the issue in machine learning to determine
a set of ideal hyperparameters for learning models that generalize the model for better
accuracy [
56
]. The performance of the developed machine learning model is dependent
on the various hyperparameters such as the criterion, depth of trees for the DT and RF
models, the distance and K-neighbor value for the KNN, number of hidden layers, units
per layer, epochs, activation function, regularizer, learning rate, batch size, and loss rate for
the ANN model. A machine learning engineer can adjust the value of the hyperparameter
manually before explicitly training the model. In this study, the used hyperparameters
for the DT, RF, KNN, and ANN models are detailed in Table 3. These four algorithms
have been implemented to evaluate the classification results according to the investigated
dataset and to assess their performance with the change of the implemented dataset. All
the results are shown and discussed in the next part.
Table 3. Selected hyper-parameters for classification models.
Predictive Model Hyperparameter Tuned to
DT Criterion Gini, Entropy
Depth of trees 4
RF
Criterion Gini, Entropy
Decision trees 2
Maximum features Auto
KNN K-neighbour K = 3
Distance Euclidean
ANN
Batch size 20
Epochs 50
Hidden layers 1
Neurons 64
Activation function Softmax
Learning rate 10−5
Optimizer Adam
Loss rate Categorical Crossentropy
Regularizer L2 regularizer
5.2. Machine Learning Models Evaluation
Evaluation of a model is the process of calculating the effectiveness of the data set
results. Data manipulation is carried out by the python tool. Table 4presents various
statistics of measurement metrics such as accuracy, precision, recall (sensitivity), and f1-
score that are considered to evaluate the performance of all classification algorithms.
Table 4. Performance metrics for classifiers evaluation.
Metric Description
Accuracy Measure of the model’s correct predictions.
Precision Determine the classifier’s ability to deliver accurate positive predictions.
Recall Probability of a positive test, conditioned on truly being positive.
F1-score Weighted average of precision and recall.

Bioengineering 2023,10, 703 14 of 18
In this study, the dataset was divided into three parts for training, testing, and valida-
tion purposes. The dataset is divided into 70% training data, 15% testing data, and 15%
validation data. After training the developed models based on the optimal hyperparameters
presented in Table 4, the Decision Tree model achieved an average classification accuracy of
91.67% with a precision of 91.90%, recall of 91.67%, and an f1-score of 91.72%. The achieved
results by the DT model are approximately the same as for the Random Forest model.
The K-Nearest Neighbor model reached an average accuracy of 93.75% with a precision
of 94.36%, recall of 93.75%, and an f1-score of 93.66%. However, among the four models
assessed, the Artificial Neural Network model performed best. The ANN had an average
classification accuracy of 99.95% with a precision of 99.43%, recall of 99.56%, and f1-score
of 99.63%. Moreover, Figure 9shows the training and the validation progress according to
the number of epochs in terms of accuracy and loss. The training and validation accuracy
reached about 99.95% for the first 10 epochs (Figure 9a). For the loss curve, the training
and validation loss went down to about 0.05% with the first 20 epochs (Figure 9b).
(a) (b)
Figure 9.
Artificial Neural Network model performance. (
a
) ANN model accuracy over epochs;
(b) ANN model loss over epochs.
The ANN model shows better performance than the KNN model, and the KNN
classifier reported better than the RF and the DT models. All achieved results from the
developed models are mentioned in Table 5.
Table 5. Machine learning models evaluation.
Predictive Model Accuracy (%) Precision (%) Recall (%) F1-Score (%)
DT 91.67 91.90 91.67 91.72
RF 91.67 92.13 91.67 91.65
KNN 93.75 94.36 93.75 93.66
ANN 99.95 99.43 99.56 99.63
To validate the experimental results, sensor importance is added to know the influence
of each sensor to classify the selected epileptic movement. The performance of the ANN
algorithm in terms of accuracy is evaluated according to the input data. Fifteen different
data combinations have been used for the classification, as described in the Table 6. First, a
combination of two sensors has been used as inputs for the ANN model. Compared to the
accuracy of each sensor combination, the ANN model with 2 sensors placed on both biceps
brachii muscles can reach an accuracy of 91.83%. The addition of the number of sensors
leads to an increase in the accuracy of the classifier from 94.6% to 96.05% when using a
combination of six sensors placed on both gastrocnemius muscles (S1, and S3), quadriceps
muscles (S2, and S4), and biceps brachii muscles (S6, and S8). A maximum classification
accuracy of 99.95% is achieved while using the combination of the eight proposed sensors.
The results in Table 6show, that the ANN model with the eight sensors is necessary for
accurate epileptic movements classification. Reducing the number of sensors leads also to
a reduction in classification accuracy.

Bioengineering 2023,10, 703 15 of 18
Table 6. Classification results over different sensor combination.
Sensors Combination S1 S2 S3 S4 S5 S6 S7 S8 Accuracy (%)
2
x x 85.51
x x 87.75
x x 88.50
x x 91.83
4
x x x x 90.00
x x x x 92.40
x x x x 93.64
x x x x 93.82
x x x x 94.27
x x x x 94.60
6
x x x x x x 93.84
x x x x x x 94.21
x x x x x x 95.59
x x x x x x 96.05
8 x x x x x x x x 99.95
Even though multiple sensors may pose challenges, they are very important to realize
the necessary accuracy enabling the effective detection and classification of motor seizures.
At the same time, it is also important to consider the practicability and usability of the
system in real-life scenarios and explore ways to make the sensors less stigmatizing and
more comfortable for patients by developing smaller and more discreet sensors or finding
ways to integrate the sensors into existing clothes or accessories.
6. Conclusions
The study in this paper shows, that the analysis of muscle activity can provide valuable
information for seizure classification. In a novel approach, we propose to track epileptic
seizures with eight surface electromyography signals (sEMG) measured at dedicated place-
ments on human limbs. We propose to use a machine learning model to analyze and classify
two motor seizures for epileptic subjects. Measurements on 20 subjects imitating tonic,
myoclonic, and no-seizure movements support this study. Features of the EMG signals,
such as maximum class separability, robustness, and computational complexity, lead to very
good classification performance. The conclusion was that the IEMG, MYOP, WAMP, SE,
SKEW, and WL feature highly separable epileptic movements. The ANN model achieved
the greatest classification accuracy rate of 99.95% in comparison to classification algorithms
based on decision tree, random forest, k-nearest neighbors, and artificial neural networks.
This work proves that surface electromyography is promising for the classification of
myoclonic and tonic epileptic seizures. The investigation is mainly based on measurements
during movements imitating the movements observed during seizures. Medical doctors
report, that the muscle contractions during seizure attacks are expected to be much stronger,
so that the classification for real non-healthy subject becomes even easier. This study
serves as a technical feasibility investigation, paving the way for clinical trials. In future
further studies need to be conducted to expand the dataset with further epileptic seizure
movements (absence seizure). Clinical studies need to be conducted to record data from
the pediatric patients and to explore longer monitoring periods to capture also infrequent
epileptic seizure movements.

Bioengineering 2023,10, 703 16 of 18
Author Contributions:
A.D. contributed by the experiment, measurement, manuscript concept,
methodology, original draft writing, visualization, and editing. O.K. contributed to the conceptual-
ization of the study and to the manuscript concept, D.B., A.F. and O.K. contributed by conceiving
and writing sections, reviewing, visualization, and editing. All authors have read and agreed to the
published version of the manuscript.
Funding:
This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Re-
search Foundation)—Project-ID 416228727—SFB 1410. Moreover, this work was also supported by the
German Academic Exchange Service ‘DAAD’ within the BISMON-57477606 project, and Chemnitz
University of Technology.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement:
The study was conducted according to the guidelines of the Decla-
ration of Helsinki, and approved by the Institutional Ethics Committee of Chemnitz University of
Technology, Germany (reference: V-331-15-GJSensor-13052019). Informed consent was obtained from
all subjects, and they were informed of the purpose of the study, the procedures involved, and their
rights as research participants.
Data Availability Statement: Not applicable.
Acknowledgments:
The authors appreciate the assistance of Chahnez Triki and Fatma Kammoun
from the Department of Child Neurology at the Hedi Chaker Hospital, Sfax, Tunisia for the fruitful
discussions on epileptic seizures and for help with the optimisation of the sensor placements. We
appreciate Alexandra Bendixen from the Department of Natural Sciences at TU Chemnitz for giving
insights in experimental aspects related to EEG measurements.
Conflicts of Interest: The authors declare no conflict of interest.
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