One of the most common abnormal electrical activities of the brain is epileptic seizures, which reflect the abnormal behavior of brain signals, which may occur in patients of all ages. in medical management system the Electroencephalograph (EEG) signals are essential way to monitor this abnormal behavior to detect the epileptic seizures in aim of acting proper reaction. Analysis EEG signal in accurate way is an important step for brain diseases detection that help in extract relevant features used to classify and detect these diseases. This research produced analysis study for EEG signal in both time and frequency domain where the frequency domain contains the most relevant discriminate features. Filtering technique is used to isolates four bands (beta, theta, alpha, and delta) from EEG channel. Database of healthy and epileptic patients has been processed in time and frequency domain. Then power spectrum density (PSD) is calculated as feature to distinguish between healthy and epileptic patients. The results show that PSD for the EEG of epileptic patients is higher than PSD of EEG of healthy person.

1.
Ahmad
,
I.
,
Wang
,
X.
,
Zhu
,
M.
,
Wang
,
C.
,
Pi
,
Y.
,
Khan
,
J. A.
,
Khan
,
S.
,
Samuel
,
O. W.
,
Chen
,
S.
, &
Li
,
G.
(
2022
).
EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review
.
In Computational Intelligence and Neuroscience
(Vol.
2022
).
Hindawi Limited
.
2.
Andrzejak
,
R. G.
,
Lehnertz
,
K.
,
Mormann
,
F.
,
Rieke
,
C.
,
David
,
P.
, &
Elger
,
C. E.
(
2001
).
Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state
.
Physical Review E
,
64
(
6
),
61907
.
3.
Boashash
,
B.
(
2015
).
Time-frequency signal analysis and processing: a comprehensive reference.
Academic press
.
4.
Byrnes
,
J. S.
(
1999
).
Time-frequency and time-scale analysis
.
Signal Processing for Multimedia
,
174
,
55
.
5.
Fisher
,
R. S.
,
Scharfman
,
H. E.
, &
DeCurtis
,
M.
(
2014
).
How can we identify ictal and interictal abnormal activity?
Issues in Clinical Epileptology: A View from the Bench
,
3
23
.
6.
Franaszczuk
,
P. J.
,
Bergey
,
G. K.
, &
Kaminski
,
M. J.
(
1994
).
Analysis of mesial temporal seizure onset and propagation using the directed transfer function method
.
Electroencephalography and Clinical Neurophysiology
,
91
(
6
),
413
427
.
7.
Griffiths
,
H. J.
(
1989
).
Encyclopedia of Medical Devices and Instrumentation
.
Radiology
,
170
(
3
),
1016
.
8.
Guger
,
C.
,
Edlinger
,
G.
,
Harkam
,
W.
,
Niedermayer
,
I.
, &
Pfurtscheller
,
G.
(
2003
).
How many people are able to operate an EEG-based brain-computer interface (BCI)?
IEEE Transactions on Neural Systems and Rehabilitation Engineering
,
11
(
2
),
145
147
.
9.
Hosseinifard
,
B.
,
Moradi
,
M. H.
, &
Rostami
,
R.
(
2013
).
Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal
.
Computer Methods and Programs in Biomedicine
,
109
(
3
),
339
345
.
10.
Hussain
,
Z. M.
,
Sadik
,
A. Z.
, &
O’Shea
,
P.
(
2011
).
Digital signal processing: an introduction with MATLAB and applications
.
Springer Science & Business Media
.
11.
Lawhern
,
V.
,
Hairston
,
W. D.
,
McDowell
,
K.
,
Westerfield
,
M.
, &
Robbins
,
K.
(
2012
).
Detection and classification of subject-generated artifacts in EEG signals using autoregressive models
.
Journal of Neuroscience Methods
,
208
(
2
),
181
189
.
12.
Lopes da Silva
,
F. H.
(
1998
).
EEG analysis: theory and practice.
13.
Zhao
,
X.
,
Zhang
,
R.
,
Mei
,
Z.
,
Chen
,
C.
, &
Chen
,
W.
(
2019
).
Identification of epileptic seizures by characterizing instantaneous energy behavior of EEG
.
IEEE Access
,
7
,
70059
70076
.
14.
Ahmad
,
Ijaz
and
Wang
,
Xin
and
Zhu
,
Mingxing
and
Wang
,
Cheng
and
Pi
,
Yao
and
Khan
,
Javed Ali
and
Khan
,
Siyab
and Samuel,
Oluwarotimi
Williams
and
Chen
,
Shixiong
and
Li
,
Guanglin
.
2022
. “
EEG-based epileptic seizure detection via machine/deep learning approaches: A Systematic Review
.”
Computational Intelligence and Neuroscience
.
15.
Safdar
,
Saima
and
Zwick
,
Benjamin
and
Bourantas
,
George
and
Joldes
,
Grand R
and
Warfield
,
Simon K
and
Hyde
,
Damon E
and
Wittek
,
Adam
and
Miller
,
Karol
.
2022
. “Automatic Framework for Patient-Specific Biomechanical Computations of Organ Deformation: An Epilepsy (EEG) Case Study.”
In International Conference on Medical Image Computing and Computer-Assisted Intervention
, 75--89.
Springer
.
16.
Shahini
,
Nahal
and
Bahrami
,
Zeinab
and
Sheykhivand
,
Sobhan
and
Marandi
,
Saba
and
Danishvar
,
Morad
and
Danishvar
,
Sebelan
and
Roosta
,
Yousef
.
2022
. “
Automatically Identified EEG Signals of Movement Intention Based on CNN Network (End-To-End)
.”
Electronics
3297
.
This content is only available via PDF.
You do not currently have access to this content.