Machine Learning-based Approach for Stroke Classiﬁcation using

Electroencephalogram (EEG) Signals

Aktham Sawan

1,2 a

, Mohammed Awad

1 b

and Radwan Qasrawi

2,3 c

Department of Computer Systems Engineering, Arab American University, Ramallah, Palestine

Department of Computer Science, Al-Quds University, Jerusalem, Palestine

Department of Computer Engineering, Istinye University, 34010, Istanbul, Turkey

Keywords:

Stroke, Electroencephalogram (EEG), Machine Learning (ML), Deep Learning (DL), Muse 2, Wearable

Devices, Wavelet Transformation, Fourier Transformation.

Abstract:

In recent years, the health care ﬁeld has heavily relied on the ﬁeld of computation. The medical decision

support system DSS, for instance, helps health professionals obtain accurate and reliable readings and diag-

nosis of patients’ vital signs. Nowadays, several medical devices allow capturing brain signals, some of these

devices are wearable, which enhances signal quality and facilitates access to the signals than the traditional

EEG devices. EEG signals are critical for assessing mental health and analyzing brain characteristics as they

are able to detect a wide range of nerve-related diseases, such as stroke. This research seeks to study the

use of machine learning techniques for the medical diagnosis of stroke through EEG signals obtained from

the wearable device ‘MUSE 2.’ Eight ML techniques were used for analysis, the XGboost classiﬁers outper-

formed other classiﬁers in identifying strokes with an accuracy rate of 83.89%. The ﬁndings proved a 7.89%

improvement on accuracy from the previous study “Predicting stroke severity with a 3-minute recording from

the Muse portable EEG study.

1 INTRODUCTION

Every year, 15 million people worldwide suffer from

a stroke, 5 million die as a result, and another 5 mil-

lion are permanently disabled (Persky et al., 2010).

Signiﬁcantly improving stroke classiﬁcation at its

early stages could radically enhance the quality of life

of patients who are unable to be successfully treated

using conventional therapeutic methods. Patients who

are diagnosed with ischemic stroke before the stroke

causes real damage to their brain tissues have a higher

recovery rate, and a lower chance of death given early

treatment by medical professionals or ﬁrst respon-

dents. Thus, pre-hospital diagnosis of such a condi-

tion, whether at the patient’s home or in the ambu-

lance, could save their life or enhance their life qual-

ity.

For this reason, this study seeks to ﬁnd a more

efﬁcient and accessible solution directly found either

https://orcid.org/0000-0002-2840-6024

https://orcid.org/0000-0002-5053-0785

https://orcid.org/0000-0002-8758-1420

at the patient’s home or accessible to ﬁrst responders

on the ambulance. A promising solution to be ex-

plored is the use of electroencephalography (EEG)

wearable devices, and the subsequent employment of

machine learning (ML) approaches for stroke classiﬁ-

cation. We expect the employment of such solution to

achieve greater precision than the traditional methods

(Wilkinson et al., 2020a).

Currently, Computed Tomography (CT) and Mag-

netic resonance imaging (MRI) are used to diag-

nose hemorrhagic or ischemic stroke by providing a

comprehensive analysis of the brain’s anatomy and

pathology. However, ﬁndings indicate that strokes

can be accurately diagnosed between 6 to 8 hours

prior to the stroke by using CT. On the other hand,

while MRI imaging is more accurate and may diag-

nose stroke within 30 minutes, it is less available and

may require a longer period of time, even in a major

medical center (Cillessen et al., 1994; Jordan, 2004;

Murri et al., 1998).

Over the last century, the use of non-invasive

structural imaging methods, such as EEG, has grown

from mainly scholarly to more industrial use. Docu-

Sawan, A., Awad, M. and Qasrawi, R.

Machine Learning-based Approach for Stroke Classiﬁcation using Electroencephalogram (EEG) Signals.

DOI: 10.5220/0010774200003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 1: BIODEVICES, pages 111-117

ISBN: 978-989-758-552-4; ISSN: 2184-4305

111

mented brain impulses from EEG are translated and

related to the cognitive activity by scientists to ob-

tain a deeper insight into the functioning of the hu-

man brain. Broadly speaking, clinical Nero imag-

ing instruments are most widely used in clinical re-

search, involving high-ﬁdelity accuracy and sophisti-

cated features (Soltanian-Zadeh, 2019).One of these

devices is the MUSE 2 portable device, which mon-

itors EEG signals at a sampling frequency of 256

Hz(Ho et al., 2017).

Through enhancing stroke classiﬁcation with the

MUSE 2 device by using ML algorithms, this paper

provides the following contributions:

• A novel approach was used to classify the stroke,

which included using the frequency of brain

waves on each electrode and applying it to all fea-

tures through feature selection, which contributed

to improving the accuracy of our model.

• Extensive testing was done to put classic and ad-

vanced ML techniques to the test in order to ﬁnd

a good classiﬁer for the task at hand.

This paper is divided into ﬁve sections, one of

which is the current introduction: Section 2 includes

a synopsis of related works. Section 3 discusses

and clariﬁes the background. Section 4 contains the

methodology. Section 5 contains the results of stroke

classiﬁcation using the ML classiﬁers. Finally, Sec-

tion 6 summarizes the work done in this study as well

as the future work.

2 LITERATURE REVIEW

Several studies have been conducted in the ﬁeld of

EEG signal analysis for stroke detection using the

Muse 2 portable device. The authors in (Wilkinson

et al., 2020b) showed the use of portable EEG as a

pre-hospital stroke diagnostic process. They used a

portable EEG machine to record data from 25 sub-

jects, 16 had acute ischemic stroke cases and corre-

lated the outcomes with accuracy controls which in-

cluded stroke imitates. In(Gottlibe et al., 2020) ),

the authors examined whether a short recording us-

ing a portable EEG device would differentiate be-

tween control and stroke classes. Data was obtained

from patients with acute ischemic stroke. The mon-

itoring group consisted of balanced volunteers. EEG

recordings were obtained using a handheld brain wave

monitor. The Updated Brain Symmetry Index (pdBSI

) was used to measure the spectral energy similar-

ity between the cerebral sides. Authors in (Djamal

et al., 2020) used a MUSE 2 portable EEG system to

record information for 25 participants, 16 of whom

had sever ischemic stroke cases. The ﬁndings indi-

cated an improvement in ischemic stroke-related pa-

tients with serious (p¡0.01).This study will focus on

accurately classifying strokes using an enhanced ma-

chine learning algorithm and MUSE2 to read EEG

signals. Diagnosis will then be provided to the nurs-

ing or ER team.

3 BACKGROUND

3.1 ML Classiﬁer

A variety of machine learning classiﬁers were used to

conduct the analysis:

• A Random Forest: (RF)classiﬁer is a form of

ensemble classiﬁer that generates numerous de-

cision trees using a random selection of training

variables and data. This classiﬁer has gained pop-

ularity in the ﬁeld of remote sensing due to the

accuracy of its classiﬁcations (Belgiu and Dr

agut¸,

2016).

• eXtreme Gradient Boosting: is a scalable and

efﬁcient implementation of Friedman’s gradient

boosting paradigm. A linear model, a tree, and

a solver learning approach are included in the

software. It offers a variety of objective func-

tions, such as regression, classiﬁcation, and scor-

ing. The software is meant to be extensible, allow-

ing users to simply create their own goals (Chen

et al., 2015).

• A Decision Tree: is constructed by interactively

partitioning the feature space of the training set.

The objective is to create a set of decision rules

that naturally partition the feature space, resulting

in an effective and resilient hierarchy classiﬁca-

tion model (Myles et al., 2004; Tolles and Meurer,

2016).

• Support-vector Machines: (also known as

support- vector networks) are machine learning

algorithms that teach themselves to solve two-

group classiﬁcation tasks. The machine gener-

ally follows the schedule: input vectors are non-

linearly transformed to a feature vector of ex-

tremely high dimensions. A linear decision layer

is built in this spatial domain. The unique proper-

ties of the decision surface enable the ML excel-

lent classiﬁcation accuracy (Cortes and Vapnik,

1995).

• Stochastic Gradient Descent: is a numerical

approach for dealing with large-scale inverse is-

sues. When seen through the perspective of clas-

BIODEVICES 2022 - 15th International Conference on Biomedical Electronics and Devices

112

sical regularization theory, however, its theoretical

characteristics are primary (Jahn and Jin, 2020).

• A Naive Bayes Classiﬁer: is a basic probabilis-

tic model based on Bayes rule and a high degree

of independence. When unnecessary words are

eliminated from a document, this form of naive

Bayes technique is known as Bernoulli Naive

Bayes (Narayanan et al., 2013).

• The K-nearest Neighbors’: technique is a ma-

chine learning model-based on a non-parametric

classiﬁcation method. Furthermore, like with

other conventional data mining approaches, it has

computational problems when applied to large

amounts of data (Saadatfar et al., 2020). We di-

rect the reader to the following book for further

information on the mathematical formulations for

some of the classiﬁers discussed ((Han et al.,

2012)).

3.2 MUSE EEG

The brain waves are recorded using a standard MUSE

EEG with a sampling frequency of 256 Hz. The

MUSE’s EEG data provides a real-time look into the

human mind. It is easily ﬂexible, and the electrodes

are placed on the brain at the locations TP10, TP9,

AF7, and AF8, according to the 10-20 electrodes

pressure sensor, with the Fpz functioning electrode.

The plastic used in AF8 and AF7 is made of platinum,

whereas the silicone rubber used in the conductor is

utilized in TP9 and TP10. Figure: 1 shows the MUSE

EEG as well as the electrode orientation according to

the 10-20 pressure sensor. The EEG data gathered by

the MUSE 2 offers a real-time glimpse into the hu-

man mind. The device is conveniently ﬂexible while

the electrodes are located on the brain as per the 10-

20 electrodes pressure sensor at the positions TP10,

TP9, AF7, and AF8, with the Fpz working electrode.

Providing a wide range of advantages, such as ver-

satility, complete usability, and low weight, it is mo-

bile and can be paired with any smartphone, tablet, or

computer. The EEG data are collected on a mobile

Figure 1: (a) Device (b) Electrode positions (Liu et al.,

2020).

device using the MUSE 2 display program, transmit-

ted using wired headphones for online processing (Ho

et al., 2017) .

4 METHODOLOGY

4.1 Experimental Setup

Extensive experiments were carried out using eight

different ML algorithms to analyze the frequency of

waves’ techniques and their inﬂuence on the ﬁnal re-

sults. All trials were conducted in the same environ-

ment, on the same computer system (Intel Core(TM)

i7 CPU, 16 GB RAM 1.8GHz (4cores)). The Python

programming language and Oracle SQL were em-

ployed.

4.2 Data Collection Procedure

Each experiment began with a method description to

the subject or their guardian, followed by explicit con-

sent. To ensure a strong connection, the participant’s

head and earlobes were washed with NuPrep, an exfo-

liating gel, and then cleaned with alcohol swabs. Be-

fore and after each session, the Muse-2 was cleaned

with alcohol wipes. EEG recordings were made in

two sessions of three minutes each (eyes open, eyes

closed), with a resting state in between. The patient’s

eyes were open and he was focused on a ﬁxation cross

in the center of his vision while resting.

4.3 Data Set

In this study, we have used a data set that contains 25

participants, 16 had an acute ischemic stroke, and 9

acted as a control group.

4.4 Data Cleaning and Features

Selection

Preparing the data for the use of these algorithms is

not a trivial task and special care must be taken not to

make labeling errors of the signals. The process starts

removing artifacts from signals by using Fast Fourier

Transform (FFT) and Wavelet transformation. The

features were extracted (shown in equation 1 and 2),

pair-derived Brain Symmetry Index (PDBSI) (shown

in equation 3), and applied based on the Fourier trans-

formation and Wavelet transformation.

DAR =

Deltawave

al phawaves

(1)

Machine Learning-based Approach for Stroke Classiﬁcation using Electroencephalogram (EEG) Signals

113

Figure 2: Methodology of this study.

Table 1: Participants information.

Type Severity lateralization Since(Days) Gender Age

Control Control - - M 91

Stroke Small Lef 6 M 87

Stroke Moderate Lef 7 F 61

Stroke Moderate Lef 1 M 65

Stroke Moderate Lef 3 F 83

Stroke Small Right 3 F 19

Stroke Moderate Right 2 F 71

Stroke Moderate Lef 4 M 71

Stroke Small Lef 1 F 86

Stroke Moderate Lef 0 M 85

Stroke Moderate Lef 8 M 37

Stroke Large Right 16 M 87

Control Control - - M 66

Stroke Large Lef 2 F 53

Control Control - - M 53

Stroke Small Lef 3 M 66

Control Control - - F 64

Control Control - - F 81

Stroke Moderate Lef 2 M 75

Control Control - - M 56

Stroke Moderate Lef 0 M 87

Control Control - - M 59

Control Control - - F 48

Stroke Large Right 6 F 72

Control Control - - M 29

DAR was computed as the sum of delta (1–3 Hz) fre-

quency power divided by alpha (8–13 Hz) frequency

power.

DBAT R =

(delta +theta)

(al pha + beta)

(2)

DTABR was determined as the summation of the volt-

age of the delta (1–3 Hz) and theta (4–7 Hz) frequency

divided by the total number of the voltage of the alpha

(8–13 Hz) and beta (14–20 Hz) frequency.

PDBSI =

∑

j=1

∑

i=1



(Ri j − Li j)

(Ri j + Li j)



(3)

pdBSI is deﬁned as: where Rij and Lij are the spectral

power density of the signals for every electrode pair-

ing (i=1, 2,..., M) for each frequency (j=1, 2,..., N).

A standard MUSE EEG with a sampling frequency of

256 Hz will be used to record the brain waves. The

EEG data from the MUSE gives a real-time view into

the human mind. The electrodes are inserted on the

brain at the sites TP10, TP9, AF7, and AF8, accord-

ing to the 10-20 electrodes pressure sensor, with the

Fpz functional electrode. Platinum is used in the ma-

terial used in AF8 and AF7, whereas silicone rubber

is used in the conductor in TP9 and TP10. We mea-

sured the standard deviations and root mean square

(RMS) of the head movement over time using the on-

board gyroscope and accelerometer to ﬁnd changes in

movement variability across the X, Y, and Z move-

ment planes. ﬁnally In machine learning classiﬁers,

chosen characteristics from Table 2 are used.

In comparison to (Wilkinson et al., 2020b) , we

utilized the same features and parameters as in this

study, which are listed in table 2, but we added fre-

quency measurements and employed eight machine

learning classiﬁers instead of simply random forest in

article(Wilkinson et al., 2020b) .

BIODEVICES 2022 - 15th International Conference on Biomedical Electronics and Devices

114

Table 2: Features from signals.

Age Gyroscope RMS-X plane

Gender Gyroscope RMS-Y plane

DAR-contralateral hemisphere Gyroscope RMS-Z plane

DTABR-contralateral Gyroscope standard deviation-X plane

pdBSI Gyroscope standard deviation-Y plane

Relative beta power Gyroscope standard deviation-Z plane

Relative alpha power Accelerometer RMS-X plane

Relative theta power Accelerometer RMS-Y plane

Relative delta power Accelerometer RMS-Z plane

High frequency pdBSI Accelerometer standard deviation-X plane

Low frequency pdBSI Accelerometer standard deviation-Y plane

PdBSI-frontal electrodes Accelerometer standard deviation-Z plane

Frequency of delta Frequency of alpha

Frequency of beta Frequency of theta

4.5 Data Preparation and Machine

Learning Steps

• Replace the null value with a zero value.

• Using the dummies approach, convert category

variables to binary values.

• Normalize and scale data Normalization refers to

the calculation of measured statistical characteris-

tics in the range of 0 to 1.

• Recursive Feature Elimination RFE is used to

choose features. RFE is a technique for selecting

features.

• For data partitioning, K=10 cross validation was

utilized.

• Eight machine learning classiﬁers were tested,

with each classiﬁer taking between 3-5 minutes

to compute.

4.6 Classiﬁcations Methodology

Following data cleaning, the data preparation phase

was performed. EEG signals have been classiﬁed as

0 or 1 depending on whether they are up to normal

or normal respectively. This is done in order to train

the ML algorithm to indicate what class each input

refers to. Machine learning methods are relatively

easy to implement and can be applied to several types

of problems. Figure 2 shows the architecture of the

model following the steps previously outlined.

5 RESULTS

Several common and complex ML classiﬁer tech-

niques were investigated in order to determine which

one performed best for the given dataset. We uti-

lized the Decision Tree (DT), Logistic Regression

(LR), eXtreme Gradient Boosting (XGB), Random

Forest (RF), K Nearest Neighbors (KNN), Stochas-

tic Gradient Descent (SGD), Linear Support Vector

Machine Classiﬁer (SVM), and the Bernoulli Naive

Bayes (BNB).

The accuracy, recall, precision, and F-score out-

comes of the dataset are evidenced in Table 4, based

on the confusion matrix, the meaning of these mea-

sures in the following

• Confusion Matrix

Table 3: Confusion Matrix.

Confusion Matrix Prediction No Prediction Yes

Actual No TN FP

Actual Yes FN TP

• FP (False Positives ):- In case that a stroke patient

is predicted but he is actually a normal person.

• FN (False Negatives) :-In case that a normal per-

son is predicted but he is actually a stroke patient.

• TP (True Positive ) :-In case that a stroke patient

is predicted as a stroke patient

• TN (True Negative) :-In case that a normal person

is predicted as a normal person

The classiﬁer classiﬁcation criteria used for the

rating of classiﬁers are as follows:

• Accuracy :- Accuracy determines the accuracy of

the classiﬁers and describes them as the following.

Accuracy =

(T P + T N)

(T P + T N + FP + FN)

(4)

• precision :- The proportion of relevant examples

among the recovered instances.

Precision =

T P

(FP + T P)

(5)

• Recall :-recall is the percentage of relevant in-

stances that were found.

Recall =

T P

(FN + T P)

(6)

• F1-score :- It is deﬁned as the harmonic mean of

recall and precision .

F1 − score =

2(precision ∗ Recall)

(precision + Recall)

(7)

Among all comparable algorithms for the (Wilkin-

son et al., 2020b) dataset, the XGB Classiﬁer proved

the best accuracy (0.8389), while Random Forest ob-

tained the highest Precision score (0.868). The SGD

Classiﬁer, on the other hand, obtained the lowest per-

formance accuracy (0.6184) and precision (0.6863);

moreover, XG- B Classiﬁer outperforms (Wilkinson

et al., 2020b) (0.76) by 0.7389, as shown in Figure 3.

Machine Learning-based Approach for Stroke Classiﬁcation using Electroencephalogram (EEG) Signals

115

Table 4: Results of ML algorithms centered.

Classiﬁer Accurecy Precision Recall F1

BernoulliNB 0.6826 0.7014 0.7259 0.7131

DecisionTreeClassiﬁer 0.7597 0.7836 0.7682 0.7726

KNeighborsClassiﬁer 0.8107 0.8262 0.8196 0.8226

LinearSVC 0.6742 0.6902 0.6267 0.6489

LogisticRegression 0.7069 0.7407 0.6964 0.7172

RandomForestClassiﬁer 0.8374 0.868 0.8261 0.8465

SGDClassiﬁer 0.6184 0.6863 0.5641 0.7211

(Wilkinson et al., 2020b) 0.76 – – –

XGBClassiﬁer 0.8389 0.8518 0.8473 0.8493

Figure 3: Accuracy of all ML algorithms based in Table 4.

As demonstrated in Figure 4, the Random Forest

Classiﬁer in the (Wilkinson et al., 2020b) dataset has

the best Precision for all datasets (0.868), while the

SGD Classiﬁer has the lowest Precision (0.6863).

Figure 4: Precision based on reported results in Table 4.

Based on the above ﬁndings and discussion, it

is reasonable to infer that the proposed approach

will provide adequate performance for categorizing

strokes when compared to the results of the xx

(Wilkinson et al., 2020b) study.

6 CONCLUSION AND FUTURE

WORK

Given the precision accuracy found in the XGB and

Random Forest classiﬁers, we expect that the current

ﬁndings will enable health professionals and health

respondents to timely classify strokes using EEG

signals in the early stages. Furthermore, this arti-

cle describes the main guidelines for running exper-

iments on the (Wilkinson et al., 2020b) dataset for

stroke classiﬁcation. This research studied the efﬁ-

cacy of several ML classiﬁers (XGBoost, KNN, NB,

DT, SVM, LR,RF, and SGC) for classifying strokes

and thus allowing for the provision of timely and

early treatment. The experimental results showed

that the XGboost classiﬁer had a maximum accuracy

of around 83.89 %, compared to (Wilkinson et al.,

2020b) 76%. The given study will be applied to hy-

brid ML algorithms in the future to improve accuracy.

The suggested model will allow the computer to iden-

tify patterns and anomalies in the EEG data, as well as

possible future possibilities for decision support sys-

tems (DSS).

7 DISCUSSION

These ﬁndings suggest that the Muse EEG device can

identify strokey. At some frequencies, brain symme-

try changes between stroke patients and healthy con-

trols. Furthermore, DAR and DTABR are elevated in

moderate and severe strokes, indicating a slowdown

of brain activity. Furthermore, the Muse installation

took around 5 minutes and was accepted even by pa-

tients with severe impairments, making this system

suitable to be used in an ambulance in the future. In

an emergency medicine context, the qEEG measure-

ments employed, including pdBSI and slowing mea-

sures, may be promptly determined. Its interpreta-

tion might be simpliﬁed further, for as in a program

that processes and analyzes EEG data from a proba-

ble stroke patient. The quick set-up time, along with

simple qEEG measurements, makes this approach a

potential tool for discriminating strokes from stroke

mimics and detecting those strokes linked with LVO

that require priority triage to complete stroke cen-

ters with percutaneous thrombectomy capabilities.In

comparison to (Wilkinson et al., 2020b), we utilized

the same features and parameters as in this study,

which are listed in table 2, but we added frequency

measurements and employed eight machine learning

classiﬁers instead of simply random forest in article

(Wilkinson et al., 2020b).

BIODEVICES 2022 - 15th International Conference on Biomedical Electronics and Devices

116

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