EEG Signal Analysis for Epilepsy Patterns Using EDFBrowser
Keywords:
Electroencephalogram (EEG) Signal, neurological disorder, epilepsy, seizure, EDFBrowser, European Data Format (EDF) files.Abstract
Background- In the healthcare system, biomedical signals are significant. It aids in diagnosing medical conditions and offers valuable information about a patient's health. Neurologists can find meaningful information and patterns by carefully examining electroencephalogram (EEG) data, which enables sufferers to receive proper care at the right time.
Objective- Biomedical EEG signals can be used to study the neurological condition known as epilepsy. Signals from an EEG can be used to study brain activity and identify neurological disorders. Recurrent seizures are a sign of epilepsy that affects the brain.
Methods- A medical history, neurological examination, and diagnostic EEG tests are frequently used to identify seizures. EEG bio-signals can be viewed and analyzed using an open-source software program called an EDFBrowser.
Results- This research defines the various ways, including visual inspection, frequency analysis, time-frequency analysis, and spike detection, to read EDF (European Data Format) for epileptic patterns. Also, to define the amplitude-frequency relationship, analyze EEG signals, and examine brain activity in different frequency bands, like Delta, theta, alpha, beta, and gamma, employing Fast Fourier Transform (FFT).
Conclusion- In neuroscience, it must correctly interpret the EEG signal patterns to analyze the condition of the brain.
Downloads
References
A. Leal et al., “Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-022-23902-6.
A. Kawala-Janik, M. Pelc, and M. Podpora, “Method for EEG signals pattern recognition in embedded systems,” Elektronika ir Elektrotechnika, vol. 21, no. 3, pp. 3–9, 2015, doi: 10.5755/j01.eee.21.3.9918.
A. Sharmila, “Epilepsy detection from EEG signals: a review,” Journal of Medical Engineering and Technology, vol. 42, no. 5. Taylor and Francis Ltd, pp. 368–380, Jul. 04, 2018. doi: 10.1080/03091902.2018.1513576.
F. Bröhl and C. Kayser, “Delta/theta band EEG differentially tracks low and high frequency speech-derived envelopes,” Neuroimage, vol. 233, Jun. 2021, doi: 10.1016/j.neuroimage.2021.117958.
S. Kiranyaz, T. Ince, M. Zabihi, and D. Ince, “Automated patient-specific classification of long-term Electroencephalography,” J Biomed Inform, vol. 49, pp. 16–31, 2014, doi: 10.1016/j.jbi.2014.02.005.
S. N. Kalitzin, D. N. Velis, and F. H. Lopes da Silva, “Stimulation-based anticipation and control of state transitions in the epileptic brain,” Epilepsy and Behavior, vol. 17, no. 3, pp. 310–323, Mar. 2010, doi: 10.1016/j.yebeh.2009.12.023.
E. M. Goldberg and D. A. Coulter, “Mechanisms of epileptogenesis: A convergence on neural circuit dysfunction,” Nature Reviews Neuroscience, vol. 14, no. 5. pp. 337–349, May 2013. doi: 10.1038/nrn3482.
A. Bhowmick, T. Abdou, T. G. Raymond, C. School, and A. Bener, “Predictive Analytics in Healthcare: Epileptic Seizure Recognition Mass Spectrometry BARC View project Predicting Software Defects Across Project View project Predictive Analytics in Healthcare Epileptic Seizure Recognition *,” 2018. [Online]. Available: https://doi.org/
S. T. Herman, M. Takeoka, J. R. Hughes, and F. W. Drislane, “Electroencephalography in clinical epilepsy research,” Epilepsy and Behavior, vol. 22, no. 1. pp. 126–133, Sep. 2011. doi: 10.1016/j.yebeh.2011.06.009.
Y. Kakisaka et al., “Generalized 3-Hz spike-and-wave complexes emanating from focal epileptic activity in pediatric patients,” Epilepsy and Behavior, vol. 20, no. 1, pp. 103–106, Jan. 2011, doi: 10.1016/j.yebeh.2010.10.025.
R. K. Maganti and P. Rutecki, “EEG and Epilepsy Monitoring,” 2013. [Online]. Available: www.ContinuumJournal.com
C. E. Stafstrom and L. Carmant, “Seizures and epilepsy: An overview for neuroscientists,” Cold Spring Harb Perspect Biol, vol. 7, no. 5, pp. 1–19, 2015, doi: 10.1101/cshperspect.a022426.
M. K. Siddiqui, R. Morales-Menendez, X. Huang, and N. Hussain, “A review of epileptic seizure detection using machine learning classifiers,” Brain Informatics, vol. 7, no. 1. Springer, Dec. 01, 2020. doi: 10.1186/s40708-020-00105-1.
A. Shoeb and J. Guttag, “Application of Machine Learning To Epileptic Seizure Detection,” 2010.
T. D. Lagerlund, G. D. Cascino, K. M. Cicora, and F. W. Sharbrough, “Long-Term Electroencephalographic Monitoring for Diagnosis and Management of Seizures,” Mayo Clin Proc, vol. 71, no. 10, pp. 1000–1006, Oct. 1996, doi: 10.4065/71.10.1000.
L. V. Tran, H. M. Tran, T. M. Le, T. T. M. Huynh, H. T. Tran, and S. V. T. Dao, “Application of Machine Learning in Epileptic Seizure Detection,” Diagnostics, vol. 12, no. 11, p. 2879, Nov. 2022, doi: 10.3390/diagnostics12112879.
T. Haddad, N. Ben-Hamida, L. Talbi, A. Lakhssassi, and S. Aouini, “Temporal epilepsy seizures monitoring and prediction using cross-correlation and chaos theory,” Healthc Technol Lett, vol. 1, no. 1, pp. 45–50, Jan. 2014, doi: 10.1049/htl.2013.0010.
U. R. Acharya, Y. Hagiwara, and H. Adeli, “Automated seizure prediction,” Epilepsy and Behavior, vol. 88. Academic Press Inc., pp. 251–261, Nov. 01, 2018. doi: 10.1016/j.yebeh.2018.09.030.
S. R. Benbadis, E. O’neill, W. O. Tatum, and L. Heriaud, “Outcome of Prolonged Video-EEG Monitoring at a Typical Referral Epilepsy Center,” 2004. [Online]. Available: http://epilepsy.usf.edu
K. R. dos Santos, M. A. de Abreu de Sousa, S. D. dos Santos, R. Pires, and S. Thome-Souza, “Differentiation between Epileptic and Psychogenic Nonepileptic Seizures in Electroencephalogram Using Wavelets and Support-Vector Machines,” Applied Artificial Intelligence, vol. 36, no. 1, Dec. 2022, doi: 10.1080/08839514.2021.2008612.
P. I. Rodriguez, J. Mejia, B. Mederos, N. E. Moreno, and V. M. Mendoza, “Acquisition, analysis and classification of EEG signals for control design,” 2018. [Online]. Available: www.uacj.mx
Y. Paul, “Various epileptic seizure detection techniques using biomedical signals: a review,” Brain Informatics, vol. 5, no. 2. Springer Berlin Heidelberg, Dec. 01, 2018. doi: 10.1186/s40708-018-0084-z.
A. Nakra and M. Duhan, “Motor imagery EEG signal classification using long short-term memory deep network and neighbourhood component analysis,” International Journal of Information Technology, vol. 14, no. 4, pp. 1771–1779, Jun. 2022, doi: 10.1007/s41870-022-00866-4.
P. Nejedly et al., “Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-27978-6.
N. Moghim and D. W. Corne, “Predicting epileptic seizures in advance,” PLoS One, vol. 9, no. 6, Jun. 2014, doi: 10.1371/journal.pone.0099334.
B. Lakshmipriya and S. Jayalakshmy, “Wavelet scattering and scalogram visualization based human brain decoding using empirical wavelet transform,” International Journal of Information Technology, vol. 15, no. 3, pp. 1699–1708, Mar. 2023, doi: 10.1007/s41870-023-01213-x.
J. L. Pachuau et al., “Segmentation of composite signal into harmonic Fourier expansion using genetic algorithm,” International Journal of Information Technology, vol. 14, no. 7, pp. 3507–3515, Dec. 2022, doi: 10.1007/s41870-022-00944-7.
M. Zhou et al., “Epileptic seizure detection based on EEG signals and CNN,” Front Neuroinform, vol. 12, Dec. 2018, doi: 10.3389/fninf.2018.00095.
Y. Abdallah, M. K. Saeed, A. Suilman, M. Omer, and A. S. Ahmed, “Patient Radiation Doses Assessment at Diagnostic X-rays Department of King Khalid hospital (KKH)-Majmaah,” Current Medical Imaging Formerly Current Medical Imaging Reviews, vol. 19, Mar. 2023, doi: https://doi.org/10.2174/1573405619666230322102011.
J. Xie et al., “Potential Value of the Stretched Exponential and Fractional Order Calculus Model in Discriminating Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: an Animal Experiment of Orthotopic Xenograft Nude Mice,” Current Medical Imaging Reviews, vol. 19, Mar. 2023, doi: https://doi.org/10.2174/1573405619666230322123117.
J. Wang et al., “Clinical Application of Individualized 3D-Printed Chest Wall Conformal Device in IMRT for Post-Mastectomy Breast Cancer.,” Current Medical Imaging Reviews, vol. 19, Feb. 2023, doi: https://doi.org/10.2174/1573405619666230222093137.
K. Mohit, R. Gupta, and B. Kumar, “Computer-Aided Diagnosis of Various Diseases Using Ultrasonography Images,” Current Medical Imaging, Mar. 2023, doi: https://doi.org/10.2174/1573405619666230306101012.
Narayan, V., Faiz, M., Mall, P. K., & Srivastava, S. (2023). A Comprehensive Review of Various Approach for Medical Image Segmentation and Disease Prediction. Wireless Personal Communications, 132(3), 1819-1848.
Saxena, A., Chauhan, R., Chauhan, D., Sharma, S., Sharma, D., & Narayan, V. (2022). Comparative Analysis Of AI Regression And Classification Models For Predicting House Damages İn Nepal: Proposed Architectures And Techniques. Journal of Pharmaceutical Negative Results, 6203-6215.
Kumar, V., Singh, B., Sharma, S., Sharma, D., & Narayan, V. (2022). A Machine Learning Approach For Predicting Onset And Progression"“Towards Early Detection Of Chronic Diseases “. Journal of Pharmaceutical Negative Results, 6195-6202.
Chaturvedi, P., Daniel, A. K., & Narayan, V. A Novel Heuristic for Maximizing Lifetime of Target Coverage in Wireless Sensor Networks. In Advanced Wireless Communication and Sensor Networks (pp. 227-242). Chapman and Hall/CRC.
Mall, P. K., Narayan, V., Srivastava, S., Sabarwal, M., Kumar, V., Awasthi, S., & Tyagi, L. (2023). Rank Based Two Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification: AN APPROACH TOWARD TAGGING UNLABELED MEDICAL DATASET. Journal of Scientific & Industrial Research (JSIR), 82(08), 818-830.
Faiz, M., & Daniel, A. K. (2023). A hybrid WSN based two-stage model for data collection and forecasting water consumption in metropolitan areas. International Journal of Nanotechnology, 20(5-10), 851-879.
Channi, H. K., Sandhu, R., Faiz, M., & Islam, S. M. (2023, August). Multi-Criteria Decision-Making Approach for Laptop Selection: A Case Study. In 2023 3rd Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-5). IEEE.
Faiz, M., Fatima, N., & Sandhu, R. (2023). A Vaccine Slot Tracker Model Using Fuzzy Logic for Providing Quality of Service. Multimodal Biometric and Machine Learning Technologies: Applications for Computer Vision, 31-52.
Sandhu, R., Singh, A., Faiz, M., Kaur, H., & Thukral, S. (2023). Enhanced Text Mining Approach for Better Ranking System of Customer Reviews. Multimodal Biometric and Machine Learning Technologies: Applications for Computer Vision, 53-69.
Sandhu, R., Bhasin, C., Faiz, M., & Islam, S. M. (2023, August). Managing E-Reviews: A Performance Enhancement Technique Using Deep Learning. In 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon) (pp. 662-666). IEEE.
Mall, P. K., Narayan, V., Srivastava, S., Sabarwal, M., Kumar, V., Awasthi, S., & Tyagi, L. (2023). Rank Based Two Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification.
Narayan, V., Daniel, A. K., & Chaturvedi, P. (2023). E-FEERP: Enhanced Fuzzy based Energy Efficient Routing Protocol for Wireless Sensor Network. Wireless Personal Communications, 1-28.
Paricherla, M., Babu, S., Phasinam, K., Pallathadka, H., Zamani, A. S., Narayan, V., ... & Mohammed, H. S. (2022). Towards Development of Machine Learning Framework for Enhancing Security in Internet of Things. Security and Communication Networks, 2022.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
