Impact of Artifact Removal from EEG Signals on Epileptic Seizure Detection

1. Partho Artifact Removal for EEG based Epileptic Seizure Detection Page 01/20
Presented by:
Partho Prosad, Zarif Ahmed, Sojib Ahmed Refath
Department of Electrical & Electronic Engineering
INDEPENDENT UNIVERSITY, BANGLADESH
EEE400 PROGRESS REPORT PRESENTATION (1ST
TERM)
Impact of Artifact Removal from EEG Signals on
Epileptic Seizure Detection
Supervisor: Dr. Md. Kafiul Islam

2. Partho Artifact Removal for EEG based Epileptic Seizure Detection Page 02/20
 Background, Motivation and Objectives
 Existing systems and related works (literature review)
 Problems and challenges in the existing systems
 Possible solutions and objectives
 Research Methodology
 Proposed System
 Work Plan
 Expected outcome of the project
 Impact of project outcome on the
 societal, health, safety, legal and cultural issues
 environment and sustainability
 Proposed budget for implementing the project
 Addressing Complex Engineering Problems and Knowledge Profile
 Summary
Outline

3. Partho Artifact Removal for EEG based Epileptic Seizure Detection Page 03/20
Background and Motivation
Epilepsy is a very common problem,
affects millions of people worldwide.
An estimation of around 50 Million people
in worldwide affected.
Epilepsy is a neurological illness of the
brain.
The sign of an excessive electrical
discharge in the brain's neurons is an
epileptic seizure.
Fig: Epilepsy Patient [1]

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Background and Motivation (contd..)
One of the primary tools in diagnosing and
managing epilepsy is
Electroencephalography (EEG), which
records the electrical activity of the brain.
The test is done to make the diagnosis of
epilepsy, which is called as EEG.
EEG data holds critical information about
epileptic seizures.
Working with EEG signals is very difficult.
Fig. Recording an EEG Signals [2]

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Background and Motivation (contd..)
 Signal Properties:
 Amplitude range: 50 uV - 3.5mV [4]
 Frequency range: 0.05 Hz to 100 Hz
EEG signals affected by various kinds of
noises or artifacts (for biosignals).
Artifacts are unwanted signals
originated from non-neural source.
Like muscle activity and external
interference, making accurate seizure
detection a challenging task. Fig: EEG signals for Epilepsy Detection [3]

6. Partho Artifact Removal for EEG based Epileptic Seizure Detection Page 06/20
Literature Review (most related journals / conferences)
Sl
Reference
(Journal / Conference papers)
Outcome
Limitations / Problems /
Challenging issues
Proposed Solution
1. A. K. Tiwari, R. B. Pachori, V. Kanhangad
and B. K. Panigrahi, "Automated
Diagnosis of Epilepsy Using Key-Point-
Based Local Binary Pattern of EEG
Signals," in IEEE Journal of Biomedical
and Health Informatics, vol. 21, no. 4,
pp. 888-896, July 2017, doi:
10.1109/JBHI.2016.2589971. [5]
EEG dataset demonstrates
the effectiveness of their
method for classifying
epileptic and seizure-free
EEG signals. The suggested
methodology seizure
identification is
straightforward and simple
to execute.
It is not investigated
whether signal distortion
from artifact removal. There
will be just one artifact
removed.
It is necessary to focus on
various artifact
categories. It is necessary
to look at the degree of
signal distortion.
2. H. -S. Chiang, M. -Y. Chen and Y. -J.
Huang, "Wavelet-Based EEG Processing
for Epilepsy Detection Using Fuzzy
Entropy and Associative Petri Net," in
IEEE Access, vol. 7, pp. 103255-103262,
2019, doi:
10.1109/ACCESS.2019.2929266. [6]
Associative Petri net
methodology outperforms
comparable methods
utilizing decision tree,
support vector machine,
neural network, and Bayes
net, providing diagnosis
accuracy rates of 93.8%
using APN model.
The technique of
classification is not
disclosed. There is no
quantification of the artifact
removal performance. The
effect of removing artifacts
on epilepsy classification at a
later stage.
Quantifying the
effectiveness of artifact
removal is necessary. On
the basis of criteria, the
effect of artifact removal
on epilepsy
categorization later stage
performance is to be
assessed.

7. Partho Artifact Removal for EEG based Epileptic Seizure Detection Page 07/20
Literature Review (most related journals / conferences)
Sl
Reference
(Journal / Conference papers)
Outcome
Limitations / Problems /
Challenging issues
Proposed Solution
3. M. U. Abbasi, A. Rashad, A. Basalamah
and M. Tariq, "Detection of Epilepsy
Seizures in Neo-Natal EEG Using LSTM
Architecture," in IEEE Access, vol. 7, pp.
179074-179085, 2019, doi:
10.1109/ACCESS.2019.2959234. [7]
The proposed Long Short-
Term Memory (LSTM)
classifier classifies these
three kinds of signals with
up to 95% accuracy. For
binary classification such
as detection of inter-ictal
or ictal only, its accuracy
increases to 98%.
Signal distortion from
artifact removal is not being
studied. There will only be
one artifact taken out.
It is essential to
concentrate on different
artifact categories.
Analyzing the level of
signal distortion is
essential.

8. Refath Artifact Removal for EEG based Epileptic Seizure Detection Page 08/20
After literature review, the following major problems are discovered and
needed to be addressed:
 The impact of artifact removal on signal distortion is not assessed.
 They tried to removed only one kind of artifact.
 The impact of the artifact removal on particular Epileptic Seizure detection is
not investigated.
 There is no quantification of the artifact's performance.
 It is recommended to utilize the artifact removal technique in all
applications.
The objectives of the proposed system are:
 Find an effective artifact removal technique, then use it on the dataset.
 Analyze the impact of artifact removal on the classification of Epileptic
Seizure detection.
Problem Statement & Objectives

9. Refath Artifact Removal for EEG based Epileptic Seizure Detection Page 09/20
Research Methodology
Artifact
Detection &
Removal
Pre-
processing
Feature
Extraction
& Selection.
EEG
Dataset
Selection
Classification
&
Evaluation

10. Refath Artifact Removal for EEG based Epileptic Seizure Detection Page 10/20
Proposed System
EEG signal acquired from subject Computing Device
Designed algorithm to
preprocess data
Designed artifact
removal method applied
Epileptiform EEG feature
extracted and selected
Selected classifier applied
for BCI classification
Accurate output can be
used as command signal for
electrical device

11. Refath Artifact Removal for EEG based Epileptic Seizure Detection Page 11/20
Work Plan – RACI Matrix
Period Assignments
Responsible (R), Accountable (A), Consulted (C) &
Informed (I) - RACI Matrix
Start End Status
Dr. Md. Kafiul
Islam
Partho Prosad Zarif Ahmed Sojib Ahmed
Refath
1st
Term
 Prepare Plan C R R R 01.06.2023 30.06.2023
 Review Literature I R R A 01.07.2023 30.08.2023
 Problem Identification C R R R 16.06.2023 30.08.2023
 Prepare Draft Budget I A R A 01.08.2023 30.08.2023
 Prepare, Submit & Present
(Proposal, Progress Presentation
& Progress Report)
I R R A 18.08.2023 17.10.2023
2nd
Term
 Project Design
(specify the work) C R R R 01.10.2023 30.11.2023
 Simulation / Hardware
(specify the work) C R R R 01.11.2023 30.12.2023
 Prepare, Submit & Present
(Presentation & Progress Report) I A A A 01.12.2023 15.01.2024
Final Term
 Testing prototype C R R R 01.01.2024 25.02.2024
 Result & Analysis C R R R 01.02.2024 30.03.2024
 Prepare, Submit & Present
(Final Report & Presentation) I A A A 01.03.2024 30.04.2024
 Prepare Poster & Present Group
Demonstration
I R R R 01.04.2024 30.05.2024

12. Refath Artifact Removal for EEG based Epileptic Seizure Detection Page 12/20
Project Plan – Gantt Chart
Task Name
1st
Term 2nd
Term 3rd
Term
Jun-23 Jul-23 Aug-23 Sep-23 Oct-23 Nov-23 Dec-23 Jan-23 Feb-24 Mar-24 Apr-24 May-24
Prepare Plan
Understanding concepts
of Epileptic Seizure
Research Literature
Problem Finding
Prepare & Submit Project
Proposal
Prepare & Submit 1st
Term Progress Report
Prepare & Present 1st
Term Progress
Selecting Suitable Artifact
removal method , dataset
Study the Result
Prepare & Submit 2nd
Term Progress Report
Prepare & Present 2nd
Term Progress
Classification
Results on various criteria
Final Report
Final Presentation
Project Demonstration

13. Refath Artifact Removal for EEG based Epileptic Seizure Detection Page 13/20
 The following outcome are expected from the proposed project:
 To study the effects of artifact removal from EEG signal based on
Epileptic seizures
 Identifying an ideal artifact removal method which will aid in pre-ictal
Epileptic detection to assist in alerting patients pre-emptively to take
preventive measures
Expected outcome of the project

14. Zarif Artifact Removal for EEG based Epileptic Seizure Detection Page 14/20
 Social impact:
• Improve online detection, assisting healthcare professionals to take measures
based on early diagnosis of pre-ictal epileptic detection.
 Health & safety issues:
• Non-invasive EEG used so no risky surgical procedures involved.
• EEG device used is FDA approved.
 Legal & cultural issues:
• Patient consent is necessary to avoid legal repercussions when recording their
EEG data.
Impact of project on societal, health, safety, legal & cultural issues

15. Zarif Artifact Removal for EEG based Epileptic Seizure Detection Page 15/20
Impact of project on the environment & sustainability
 Technical:
• Additional analysis of Epilepsy-based research
 Economical:
• N/A
 Environmental:
• Green Technology
 SDG 2030:
• SDG 3: Target 3.4: Reduce mortality rate of non-communicable disease
by 1/3 via prevention and treatment.

16. Zarif Artifact Removal for EEG based Epileptic Seizure Detection Page 16/20
Overall Budget to Implement the Project
Sl Item Justification Price (BDT)
1.
Matlab Software
License
For pre-processing raw EEG data and to apply
signal processing techniques for artifact
removal
30,150
2.
EMOTIV Pro software
License (8 months)
For recording, storing and exporting raw EEG
data and post-processed data analysis
86,800
3.
EEG data collection
incentive
Incentive for research subjects 25,000
Total (BDT) 1,41,950
In word: One Lakh Forty-One Thousand Nine Hundred Fifty Taka Only

17. Zarif Artifact Removal for EEG based Epileptic Seizure Detection Page 17/20
Addressing complex engineering problems
Attributes Addressing complex engineering problems in the project
WP1 Depth of knowledge required
(WK3-WK5, WK8)
WK3-Knowledge of Signal and Systems, Numerical technique Lab, WK4- Digital Signal
Processing (DSP), DSP Lab, Biomedical Signal Processing, WK5- Engineering Design, Control
Systems, WK8-literature review including Journal and Conference papers.
WP2 Range of conflicting
requirements
N/A
WP3 Depth of analysis required In depth knowledge of signal processing for EEG signal.
WP4 Familiarity of issues EEG signal processing, Machine learning algorithms.
WP5 Extent of applicable codes N/A
WP6 Extent of stakeholder
involvement
Patients prone to epileptic seizures, healthcare professionals, medical engineering researchers
focusing on epilepsy detection and prevention research.
WP7 Interdependence Subsystems include EEG signal collection, the signal preprocessing system, artifact removal
method application, feature extraction, Epilepsy classification method.

18. Zarif Artifact Removal for EEG based Epileptic Seizure Detection Page 18/20
 Familiarized ourselves with various advanced signal processing
techniques to work with EEG signals.
 Reviewed Literature involving artifact removal on EEG-based Epileptiform
signals.
 Articulated a potential work plan, including a primary and secondary
source for datasets to be used for the scope of the research.
Summary

19. Zarif Artifact Removal for EEG based Epileptic Seizure Detection Page 19/20
1. https://www.hindustantimes.com/lifestyle/health/how-to-assist-someone-who-is-having-a-brain-seizure-experts-s
hare-first-aid-tips-101674558834931.html
2. https://www.researchgate.net/publication/356981895_Affective_Analysis_and_Interpretation_of_Brain_Response
s_to_Music_Stimuli/figures?lo=1
3. https://en.wikipedia.org/wiki/Epilepsy
4. https://www.researchgate.net/publication/367283773_Invited_Speech_by_Dr_Md_Kafiul_Islam_titled_Recent_Tr
ends_and_Challenges_in_Artifact_Detection_and_Removal_from_Biomedical_Signals_-An_Overview_at_ICREST_2
022
5. A. K. Tiwari, R. B. Pachori, V. Kanhangad and B. K. Panigrahi, "Automated Diagnosis of Epilepsy Using Key-Point-
Based Local Binary Pattern of EEG Signals," in IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 4, pp.
888-896, July 2017, doi: 10.1109/JBHI.2016.2589971.
6. H. -S. Chiang, M. -Y. Chen and Y. -J. Huang, "Wavelet-Based EEG Processing for Epilepsy Detection Using Fuzzy
Entropy and Associative Petri Net," in IEEE Access, vol. 7, pp. 103255-103262, 2019, doi:
10.1109/ACCESS.2019.2929266.
7. M. U. Abbasi, A. Rashad, A. Basalamah and M. Tariq, "Detection of Epilepsy Seizures in Neo-Natal EEG Using LSTM
Architecture," in IEEE Access, vol. 7, pp. 179074-179085, 2019, doi: 10.1109/ACCESS.2019.2959234.
8. M. K. Islam, P. Ghorbanzadeh, and A. Rastegarnia, “Probability mapping based artifact detection and removal from
single-channel EEG signals for brain–computer interface applications,” J. Neurosci. Methods, vol. 360, no. June, p.
109249, 2021, doi: 10.1016/j.jneumeth.2021.109249.
Selected References

20. Zarif Artifact Removal for EEG based Epileptic Seizure Detection Page 20/20
Any questions, comments or suggestions?