Epileptic seizure detection combining power spectral density and high-frequency oscillations
Keywords:Epilepsy, Maximum power, Ripples, Subband
AbstractDetection of pre-seizure signs in epileptic signals may help patients to survive the seizure with minimal damage. This study aims to detect epileptic seizure patterns using EEG datasets of five patients. The signals' maximum power spectral density (PSD) and high-frequency oscillations (HFOs) signals are investigated. The PSDs of all patients' signals are calculated, and the subbands of the maximum PSD are examined. It is observed that 95%, 85%, 85%, 75%, and 80% of the channels of the five patients are in the sum of delta and theta subbands of the maximum PSD, respectively. All patients' maximum power frequency subbands of F4 and T3 channels included only delta and theta subbands. Furthermore, frequency increase rates of pre-ictal and ictal signals are investigated, and increasing PSDs of ripples and fast ripples are then calculated. A much higher-frequency ripple follows the low-frequency ripple in 75%, 75%, 65%, 85%, and 75% of the channels of the first, second, third, fourth, and fifth patients, respectively. For the pre-ictal data, a much higher frequency ripple is not seen, followed by a low-frequency ripple in 90%, 85%, 75%, 90%, and 90% of all channels of five patients, respectively. In addition, in this study, the frequency of signals is observed to be 80 Hz and above in the Fp2, C4, P4, O2, and Pz channels, which are common to all patients. Consequently, examining PSD and HFO signals ensures the detection of the differences between the data sets and detects the epileptic seizure patterns in all five patients.
World Health Organization, Epilepsy, pp. 1-2, 2022.
Sirven JI, “Epilepsy: A Spectrum Disorder,” Cold Spring Harb Perspect Med., 2015 Sep; 5(9): a022848. doi: 10.1101/cshperspect.a022848.
Akdağ et al., "Epilepsy,” Osmangazi Medical Journal, 38 (Special Issue 1): 35-41, 2016. https://doi.org/10.20515/otd.88853.
Fisch BJ, Spehlmann's EEG Primer. Elsevier, pp. 634, 1991.
Meldrum BS, Chapman AG, “Excitatory amino acids receptors and antiepileptic drug development,” Jasper's Basic Mechanisms of the Epilepsies, Delgado-Escueta, A. V. Wilson, W. Olsen, R. W. Porter, R. J. eds. Third Edition: Advances in Neurology, Vol. 79, 1999, Chapter 65:965–978 Lippincott Williams & Wilkins Philadelphia.
Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” The American Physical Society, volume 64, 061907, 2001. DOI: 10.1103/PhysRevE.64.061907.
Zhao M, Suh M, Ma H, Perry C, Geneslaw A ve Schwartz TH, “Focal Increases in Perfusion and Decreases in Hemoglobin Oxygenation Precede Seizure Onset in Spontaneous Human Epilepsy,” Epilepsia, 48(11): 2059–2067, 2007. doi: 10.1111/j.1528-1167.2007.01229.x.
Schwartz TH, “Neurovascular Coupling and Epilepsy: Hemodynamic Markers for Localizing and Predicting Seizure Onset,” Epilepsy Curr. 7(4): 91–94, Jul. 2007. doi: 10.1111/j.1535-7511.2007.00183.x.
Varsavsky A, Mareels I, Cook M, Epileptic Seizures and the EEG/Measurement, Models, Detection and Prediction. CRC Press, International Standard Book Number-13: 978-1-4398-1204-4 (eBook - PDF), 2011. https://doi.org/10.1201/b10459.
Bek S, Erdoğan E, Gökçil Z, “Vagal Nerve Stimulation and Patient Selection,” Epilepsy, 18(Appendix 1): 63-67, 2012. DOI: 10.5505/epilepsi.2012.96168.
Meenakshi et al., “Frequency Analysis of Healthy Epileptic Seizure in EEG using Fast Fourier Transform,” International Journal of Engineering Research and General Science Volume 2, Issue 4, June-July, 2014.
Moghim N, Corne DW, “Predicting Epileptic Seizures in Advance,” PLoS One, 9(6): e99334, 2014. doi: 10.1371/journal.pone.0099334.
Kılıç TY, Yesilaras M, Atilla ÖD, Sever M, Aksay E, “Can venous blood gas analysis be used for predicting seizure recurrence in emergency department?” World J Emerg Med, Vol 5, No 3, 2014, doi: 10.5847/wjem.j.issn.1920-8642.2014.03.005.
Aguiar K, França FMG, Barbosa VC, Teixeria CS, “Early detection of epilepsy seizures based on a weightless neural network,” IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 2016. DOI:10.1109/EMBC.2015.7319387.
Fujiwara K, Miyajima M, Yamakawa T, Abe E, Suzuki Y, Sawada Y, Kano M, Maehara T, Ohta K, Sakuma TS, Sasano T, Matsuura M, Matsushima E, “Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features,” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 63, NO. 6, JUNE 2016. doi: 10.1109/TBME.2015.2512276.
Aggarwal G, Gandhi TP, “Prediction of Epileptic Seizures based on Mean Phase Coherence,” bioRxiv preprint, November 1, 2017. doi: https://doi.org/10.1101/212563.
Lai YF, Chiang GS, “Automatic Detection of Epileptic Seizures in EEG Using Machine Learning Methods,” E-ISSN: 2224-2902, Volume 14, 2017.
Billeci L, Marino D, Insana L, Vatti G, Varanini M, “Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis,” Sep 25; 13(9): e0204339, eCollection 2018, doi: 10.1371/journal.pone.0204339.
Wang et al., “Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer,” Epilepsi, 23(3): 109-117, 2017.
Hussein R, Palangi H, Ward RK, Wang ZJ, “Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals,” Clinical Neurophysiology 130, 2019; 25–37, DOI: 10.1016/j.clinph.2018.10.010.
Abedin MZ, Akther S, Hossain MS, “An Artificial Neural Network Model for Epilepsy Seizure Detection,” 5th International Conference on Advances in Electrical Engineering (ICAEE), 2019.
Slimen IB, Boubchir L, Seddik H, “Epileptic seizure prediction based on EEG spikes detection,” J Biomed Res., 2020 May; 34(3): 162–169, doi: 10.7555/JBR.34.20190097.
Stirling RE, Cook MJ, Grayden DB, Karoly PJ, “Seizure forecasting and cyclic control of seizures,” 2020. https://doi.org/10.1111/epi.16541.
Zhang Z, Parhi KK, “Seizure detection using regression tree based feature selection and polynomial SVM classification,” 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015. DOI:10.1109/EMBC.2015.7319900.
McGrogan N, “Neural network detection of epileptic seizures in the electroencephalogram,” Oxford University, 1999.
Srinivasan V, Eswaran C, Sriraam N, “Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features,” Journal of Medical Systems, Vol. 29, No. 6, December 2005. DOI: 10.1007/s10916-005-6133-1.
Tzallas AT, Tsipouras MG, Fotiadis DI, “Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis,” IEEE Transactions on Information Technology in Biomedicine, Volume: 13, Issue: 5, Sept. 2009. DOI: 10.1109/TITB.2009.2017939.
Ubeyli ED, “Statistics over features: EEG signals analysis,” Computers in Biology and Medicine, pp. 733 - 741, 39 (2009). https://doi.org/10.1016/j.compbiomed.2009.06.001.
Bandarabadi M, Teixeira CA, Rasekhi J, Dourado A, “Epileptic seizure prediction using relative spectral power features,” Clinical Neurophysiology, Volume 126, Issue 2, pp. 237-248, February 2015. https://doi.org/10.1016/j.clinph.2014.05.022.
Balasaraswathy N, Rajavel R, “Automatic seizure detection system with low complex PSD using TMS320C6713 DSP,” Int. J. Biomedical Engineering and Technology, Vol. 18, No. 2, 2015. https://doi.org/10.1504/IJBET.2015.070034.
Ashokkumar SR, Anupallavi S, Premkumar M, Jeevanantham V, “Implementation of deep neural networks for classifying electroencephalogram signal using fractional S-transform for epileptic seizure detection”, Int J Imaging Syst Technol., 31: 895–908, 2021. https://doi.org/10.1002/ima.22565.
Nanthini K, Tamilarasi A, Pyingkodi M, Kaviya SM, Mohideen PA, “Epileptic Seizure Detection and Prediction Using Deep Learning Technique”, 2022 International Conference on Computer Communication and Informatics (ICCCI ), Jan. 25 – 27, 2022. Coimbatore, INDIA.
Singh K, Malhotra J, “Smart neurocare approach for detection of epileptic seizures using deep learning based temporal analysis of EEG patterns,” Multimedia Tools and Applications, 2022. https://doi.org/10.1007/s11042-022-12512-z.
Ma D, Zheng J, Peng L, “Performance Evaluation of Epileptic Seizure Prediction Using Time, Frequency, and Time–Frequency Domain Measures,” Processes, 9(4), 682, 2021. https://doi.org/10.3390/pr9040682.
Park Y, Luo L, Parhi KK, Netoff T, “Seizure prediction with spectral power of EEG using cost-sensitive support vector machines,” Epilepsia, Volume52, Issue10, pp. 1761-1770, October 2011. DOI: 10.1111/j.1528-1167.2011.03138.x.
Parhi KK, Zhang Z, “Discriminative Ratio of Spectral Power and Relative Power Features Derived via Frequency-Domain Model Ratio With Application to Seizure Prediction,” IEEE Transactions on Biomedical Circuits and Systems, Volume: 13, Issue: 4, pp. 645 - 657, Aug. 2019. DOI: 10.1109/TBCAS.2019.2917184.
Tsipouras MG, “Spectral information of EEG signals with respect to epilepsy classification,” EURASIP Journal on Advances in Signal Processing volume 2019, Article number: 10. 2019. https://doi.org/10.1186/s13634-019-0606-8.
Ge L, Parhi KK, “Applicability of Hyperdimensional Computing to Seizure Detection,” IEEE Open Journal of Circuits and Systems, Volume: 3, pp. 59-71, 2022. DOI:10.1109/ojcas.2022.3163075.
Zweiphenning WJEM, Ellenrieder NV, Dubeau F, Martineau L, Minotti L, Hall JA, Chabardes S, Dudley R, Kahane P, Gotman J, Frauscher B, “Correcting for physiological ripples improves epileptic focus identification and outcome prediction,” Epilepsia, 63: 483–496, 2022. https://doi.org/10.1111/epi.17145.
Kramer MA, Ostrowski LM, Song DY, Thorn EL, Stoyell SM, Parnes M, Chinappen D, Xiao G, Eden UT , Staley KJ , Stufflebeam SM, Chu CJ, “Scalp recorded spike ripples predict seizure risk in childhood epilepsy better than spikes,” Brain, 142(5): 1296-1309, 2019 May 1. doi: 10.1093/brain/awz059.
Lachner-Piza D, Jacobs J, Bruder JC, Schulze-Bonhage A, Stieglitz T, Dümpelmann M, “Automatic detection of high-frequency-oscillations and their sub-groups co-occurring with inter-ictal-epileptic-spikes,” J Neural Eng, 17(1): 016030, 2020 Jan 14;. doi: 10.1088/1741-2552/ab4560.
Nadalin JK, Eden UT, Han X, Richardson RM, Chu CJ, Kramer MA, “Application of a convolutional neural network for fully-automated detection of spike ripples in the scalp electroencephalogram,” J Neurosci Methods, 360: 109239, 2021 Aug 1. doi: 10.1016/j.jneumeth.2021.109239.
Schönberger J, Knopf A, Dümpelmann M, Schulze-Bonhage A, Jacobs J, “Distinction of Physiologic and Epileptic Ripples: An Electrical Stimulation Study,” Brain Sci. 11, 538, 2021. doi: 10.3390/brainsci11050538.
Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” The American Physical Society, PHYSICAL REVIEW E, VOLUME 64, 061907, 2001. DOI: 10.1103/PhysRevE.64.061907.
Bairagi RN, Maniruzzaman Md, Pervin S, Sarker A, “Epileptic seizure identification in EEG signals using DWT, ANN and sequential window algorithm,” Soft Computing Letters 3, (2021) 100026.
Libenson M, “Practical approach to electroencephalography,” First ed., Saunders, United States, 2009.
LeVan P,Urrestarazu E, Gotman J, “A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification,” Clinical Neurophysiology 117, 912–927, 2006. https://doi.org/10.1016/j.clinph.2005.12.013.
Al-Fahoum AS, Al-Fraihat AA, “Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains”, Volume 2014 |Article ID 730218, http://dx.doi.org/10.1155/2014/730218.
Nayak CS, Anilkumar AC, “EEG Normal Waveforms”, Last Update: July 31, 2021.
Park CJ, Hong SB, “High Frequency Oscillations in Epilepsy: Detection Methods and Considerations in Clinical Application,” J Epilepsy Res, 9(1): pp. 1–13, 2019 Jun. doi: 10.14581/jer.19001.
Moctezuma LA, Molinas M, “EEG Channel-Selection Method for Epileptic-Seizure Classification Based on Multi-Objective Optimization,” Frontiers in Neuroscience, Volume 14, Article 593, June 2020. https://doi.org/10.3389/fnins.2020.00593.
MathWorks Inc., “MATLAB [online],” Website https://www.mathworks.com/products/matlab.html [accessed 13 March 2022].
Bhavsar R, Helian N, Sun Y, Davey N, Steffert T, Mayor D, “Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis,” Procedia Computer Science 145, 97–104, 2018 . DOI:10.1016/j.procs.2018.11.016.
Zhong L, Wan J, Wu J, He S, Zhong X, Huang Z, Li Z, “Temporal and spatial dynamic propagation of electroencephalogram by combining power spectral and synchronization in childhood absence epilepsy”, Front. Neuroinform., 16 August 2022. https://doi.org/10.3389/fninf.2022.962466.
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