Integration of DWT, FFT, and Spatial domain for the identification of epileptic seizure utilizing electroencephalogram signal
References
- Bronzino, J.D., Peterson, D.R.: Principles of electroencephalography. In: Biomedical Engineering Fundamentals, pp. 445–456. CRC press, (2006).
- Adeli H, Zhou Z, Dadmehr N, “Analysis of EEG records in an epileptic patient using wavelet transform.” Journal of neuroscience methods 123(1), 69–87 (2003).
- Tsiouris et al. (2019) – “A Long Short-Term Memory Deep Learning Network for the Prediction of Epileptic Seizures Using EEG Signals.” DOI: .1016/j.compbiomed.2019.01.024.
- Zhou et al. (2021) – “Epileptic Seizure Detection Based on EEG Signals and CNN.” DOI: 10.1109/ACCESS.2021.3051339
- Roy et al. (2019) – “Deep learning based interictal spike detection in EEG using convolutional neural networks.” 10.1016/j.neucom.2018.09.100,
- Abdel hameed et al. (2020) – “Graph theory and machine learning: A new perspective for EEG-based computer-aided diagnosis of epilepsy.” 10.1016/j.cmpb.2020.105996
- Yang et al. (2020) – “Epileptic seizure detection using deep graph convolutional networks.” 10.1109/TNSRE.2020.2991362
- Al-Salman W, Li Y, Wen P, Miften FS, Oudah AY, Ghayab HRA. Extracting epileptic features in EEGs using a dual-tree complex wavelet transform coupled with a classification algorithm. Brain Res. 2022 Mar 15;1779:147777. doi: 10.1016/j.brainres.2022.147777. Epub 2022 Jan 6. PMID: 34999060.
- Kiymik MK, Güler I, Dizibüyük A, Akin M. Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Comput Biol Med. 2005 Oct;35(7):603–16. doi: 10.1016/j.compbiomed.2004.05.001. PMID: 15809098.
- Mingkan Shen, Fuwen Yang, Peng Wen, Bo Song, Yan Li, “A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network, Heliyon, Volume 10, Issue 11, 2024, e31827, ISSN 2405-8440,
https://doi.org/10.1016/j.heliyon.2024.e31827 . - Sharmila A, Geethanjali P. DWT based detection of epileptic seizure from EEG signals using naive Bayes and K-NN classifiers, IEEE Access. 2016;4: 7716–27.
- Gandhi TK, Chakraborty P, Roy GG, Panigrahi BK. Discrete harmony search based expert model for epileptic seizure detection in electroencephalography. Expert Syst Appl. 2012;39(4):4055–62.
- Acharya UR, Fujita H, Sudarshan VK, Bhat S, Koh JE. Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowl Based Syst. 2015; 88:85–96.
- Xiang J, Li C, Li H, Cao R, Chen J. The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Methods. 2015; 243:18–25.
- Tzallas AT, Tsipouras MG, Fotiadis DI. Epileptic seizure detection in EEGs using time–frequency analysis. IEEE Trans InfTechnol Biomed. 2009;13(5):703–10.
- Polat K, Gunes S. Artificial immune recognition system with fuzzy resource allocation mechanism classifier, principal component analysis and FFT method based new hybrid automated identification system for classification of EEG signals. Expert Syst Appl. 2008;34(3):2039–48.
- Khan Y, Gotman J., “Wavelet based automatic seizure detection in intracerebral electroencephalogram”. ClinNeurophysiol. 2003;114(5):898–908.
- Adeli H, Ghosh-Dastidar S, Dadmehr N. A wavelet-chaos methodology for analysis of EEGs and EEG sub bands to detect seizure and epilepsy. IEEE Trans Biomed Eng. 2007;54(2):205–11.
- Avdakovic S, Omerhodzic I, Badnjevic A, Boskovic D. Diagnosis of epilepsy from EEG signals using global wavelet power spectrum. In: 6th European Conference of the International Federation for Medical and Biological Engineering. New York, Springer, 2015. P. 481–484.
- Al Ghayab HR, Li Y, Siuly S, Abdulla S. A feature extraction technique based on tunable q-factor wavelet transform for brain signal classification. J Neurosci Methods. 2019; 312:43–52.
- Taran S, Bajaj V, Siuly S. An optimum allocation sampling-based feature extraction scheme for distinguishing seizure and seizure-free EEG signals. Health InfSci Syst. 2017;5(1):7.
- Gajic D, Djurovic Z, Di Gennaro S et al (2014) Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition. Biomed EngAppl Basis Commun 26:1450021.
- Amin HU, Malik AS, Ahmad RF et al (2015) Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australis PhysEngSci Med 38:139–149.
- Samiee K, Kovacs P, Gabbouj M (2015) Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform. IEEE Trans Biomed Eng 62:541–552.
- Ghayab HR, Li Y, Siuly S et al (2017) Developing a tunable Q-factor wavelet transform based algorithm for epileptic EEG feature extraction. In: International conference on health information science. Springer, Cham, pp 45–55.
- Ullah, I., Hussain, M., & Aboalsamh, H. (2018). An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Systems with Applications, 107, 61–71.
- Sriraam, N., Raghu, S., Tamanna, K., Narayan, L., Khanum, M., Hegde, A. S., & Kumar, A. B. (2018). Automated epileptic seizures detection using multi-features and multilayer perceptron neural network. Brain informatics, 5(2), 1–10.
- Aldana, Y. R., Hunyadi, B., Reyes, E. J. M., Rodriguez, V. R., & Van Huffel, S. (2018). Nonconvulsive epileptic seizure detection in scalp EEG using multiway data analysis. IEEE journal of biomedical and health informatics, 23(2), 660–671.
- Wei, X., Zhou, L., Chen, Z., Zhang, L., & Zhou, Y. (2018). Automatic seizure detection using three-dimensional CNN based on multi-channel EEG. BMC medical informatics and decision making, 18(5), 71–80.
- Wang, X., Gong, G., Li, N., &Qiu, S. (2019). Detection analysis of epileptic EEG using a novel random forest model combined with grid search optimization. Frontiers in human neuroscience, 13, 52.
- Peachap, A. B., & Tchiotsop, D. (2019). Epileptic seizures detection based on some new Laguerre polynomial wavelets, artificial neural networks and support vector machines. Informatics in Medicine Unlocked, 16, 100209.
- Wu, J., Zhou, T., & Li, T. (2020). Detecting epileptic seizures in EEG signals with complementary ensemble empirical mode decomposition and extreme gradient boosting. Entropy, 22(2), 140.
- Acharya, U. R., et al. (2013). “Automated diagnosis of epileptic EEG using entropies.” Biomedical Signal Processing and Control, 7(4), 401–408.
- Subasi, A. (2007). “EEG signal classification using wavelet feature extraction and a mixture of expert model.” Expert Systems with Applications, 32(4), 1084–1093.
- Sharma, R., et al. (2018). “A wavelet-based automated epileptic seizure detection method using EEG signals.” Neural Computing and Applications, 29, 47–57.
- Ocak, H. (2009). “Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm.” Signal Processing, 89(12), 2453–2462.
- L. Sujbert, G. Orosz, “FFT-based spectrum analysis in the case of data loss”, 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, Pisa, Italy, Published in IEEE Xplore, pp. 1–6, 11–14 May 2015, doi: 10.1109/I2MTC.2015.7151371.
- P. Ramesh Babu, “Digital Signal Processing” Scitech Publications (India) Pvt. Limited, 2011.
- A Chattopadhyaya, S. Chattopadhyay, J. N. Bera, S. Sengupta, “Assessment of saturated current transformer primary current by multi resolution analysis of its secondary current”, 2nd International Conference on Control, Instrumentation, Energy and Communication (CIEC16), University of Calcutta, Kolkata, pp. 255–259, 2016, Published in IEEE Xplore Digital Library, DOI: 10.1109/CIEC.2016.7513763.
- N. Mukherjee, A. Chattopadhyaya, S. Chattopadhyay, S. Sengupta, “Discrete Wavelet Transform and Stockwell Transform based Statistical Parameters Estimation for Fault Analysis in Grid Connected Wind Power System”, IEEE Systems Journal, Volume: 14, Issue: 3, pp. 4320–4328. DOI: 10.1109/JSYST.2020.2984132 (2020).
- Hassan AR, Siuly S, Zhang Y. Epileptic seizure detection in EEG signals using tunable-q factor wavelet transform and bootstrap aggregating. Comput Methods Prog Biomed. 2016; 137: 247–59.
- Samiee K, Kovacs P, Gabbouj M., “Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform”, IEEE Trans Biomed Eng. 2015;62(2):541–52.
- Jaiswal AK, Banka H., “Local pattern transformation-based feature extraction techniques for classification of epileptic EEG signals”, Biomed Signal Process Control. 2017; 34:81–92.
- Wang L, Xue W, Li Y, Luo M, Huang J, Cui W, Huang C., “Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis”, Entropy. 2017;19(6):222.
- R Shiva Shankar, CH Raminaidu, VV Sivarama Raju, and J Rajanikanth, “Detection of Epilepsy based on EEG Signals using PCA with ANN Model”, Journal of Physics: Conference Series, 2070 (2021) 012145 IOP Publishing doi:10.1088/1742-6596/2070/1/012145
DOI: https://doi.org/10.2478/ijssis-2025-0041 | Journal eISSN: 1178-5608
Language: English
Submitted on: Mar 6, 2025
Published on: Aug 8, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
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© 2025 Rabel Guharoy, Nanda Dulal Jana, Suparna Biswas, Aveek Chattopadhyaya, published by Professor Subhas Chandra Mukhopadhyay
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