• ISSN 1674-8301
  • CN 32-1810/R
Volume 34 Issue 3
May  2020
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Quintero-Rincón Antonio, D'Giano Carlos, Batatia Hadj. A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures[J]. The Journal of Biomedical Research, 2020, 34(3): 205-212. doi: 10.7555/JBR.33.20190012
Citation: Quintero-Rincón Antonio, D'Giano Carlos, Batatia Hadj. A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures[J]. The Journal of Biomedical Research, 2020, 34(3): 205-212. doi: 10.7555/JBR.33.20190012

A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures

doi: 10.7555/JBR.33.20190012
More Information
  • Corresponding author: Antonio Quintero-Rincón, Epilepsy and Telemetry Integral Center, Fight Against Child Neurological Diseases Foundation, Montañeses 2325, Buenos Aires C1428AQK, Argentina. E-mail: tonioquintero@ieee.org
  • Received: 2019-01-16
  • Revised: 2019-04-12
  • Accepted: 2019-06-03
  • Published: 2019-08-28
  • Issue Date: 2020-05-28
  • The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals. In this paper, we develop a model-based classification method to detect epileptic seizures that relies on this algorithm to filter electroencephalogram (EEG) signals. The underlying idea was to design an EEG filter that enhances the waveform of epileptic signals. The filtered signal was fitted to a quadratic linear-parabolic model using the curve fitting technique. The model fitting was assessed using four statistical parameters, which were used as classification features with a random forest algorithm to discriminate seizure and non-seizure events. The proposed method was applied to 66 epochs from the Children Hospital Boston database. Results showed that the method achieved fast and accurate detection of epileptic seizures, with a 92% sensitivity, 96% specificity, and 94.1% accuracy.


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  • [1]
    Frei MG, Davidchack RL, Osorio I. Least squares acceleration filtering for the estimation of signal derivatives and sharpness at extrema [and application to biological signals][J]. IEEE Trans Biomed Eng, 1999, 46(8): 971–977. doi: 10.1109/10.775407
    Chernov N, Lesort C. Statistical efficiency of curve fitting algorithms[J]. Comput Stat Data Anal, 2004, 47(4): 713–728. doi: 10.1016/j.csda.2003.11.008
    López-Rubio E, Thurnhofer-Hemsi K, Blázquez-Parra EB, et al. A fast robust geometric fitting method for parabolic curves[J]. Pattern Recognit, 2018, 84: 301–316. doi: 10.1016/j.patcog.2018.07.019
    Zhang HH, Su JZ, Wang QY, et al. Predicting seizure by modeling synaptic plasticity based on EEG signals - a case study of inherited epilepsy[J]. Commun Nonlinear Sci Numer Simul, 2018, 56: 330–343. doi: 10.1016/j.cnsns.2017.08.020
    Ramon C, Holmes MD, Wise MV, et al. Oscillatory patterns of phase cone formations near to epileptic spikes derived from 256-channel scalp EEG data[J]. Comput Mathem Methods Med, 2018, 2018: 9034543.
    Liu HY, Yang Z, Meng FG, et al. Chronic vagus nerve stimulation reverses heart rhythm complexity in patients with drug-resistant epilepsy: an assessment with multiscale entropy analysis[J]. Epilepsy Behav, 2018, 83: 168–174. doi: 10.1016/j.yebeh.2018.03.035
    Begemann A, Acuña MA, Zweier M, et al. Further corroboration of distinct functional features in SCN2A variants causing intellectual disability or epileptic phenotypes[J]. Mol Med, 2019, 25: 6.
    Liang SF, Wang HC, Chang WL. Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection[J]. EURASIP J Adv Sign Processing, 2010, 2010: 853434. doi: 10.1155/2010/853434
    Sorensen TL, Olsen UL, Conradsen I, et al. Automatic epileptic seizure onset detection using matching pursuit: a case study[C]//Proceedings of 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. Buenos Aires, Argentina: IEEE, 2010: 3277-3280.
    Nasehi S, Pourghassem H. A novel fast epileptic seizure onset detection algorithm using general tensor discriminant analysis[J]. J Clin Neurophysiol, 2013, 30(4): 362–370. doi: 10.1097/WNP.0b013e31829dda4b
    Zhang ZN, Wen TX, Huang W, et al. Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing[J]. J X-Ray Sci Technol, 2017, 25(2): 261–272. doi: 10.3233/XST-17258
    Mahmoodian N, Boese A, Friebe M, et al. Epileptic seizure detection using cross-bispectrum of electroencephalogram signal[J]. Seizure, 2019, 66: 4–11. doi: 10.1016/j.seizure.2019.02.001
    Polychronaki G, Ktonas P, Gatzonis S, et al. Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection[J]. J Neural Eng, 2014, 7(4): 046007.
    Birjandtalab J, Pouyan MB, Cogan D, et al. Automated seizure detection using limited-channel EEG and non-linear dimension reduction[J]. Comput Biol Med, 2017, 82: 49–58. doi: 10.1016/j.compbiomed.2017.01.011
    Acharya UR, Oh SL, Hagiwara Y, et al. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals[J]. Comp Biol Med, 2018, 100: 270–278. doi: 10.1016/j.compbiomed.2017.09.017
    Quintero-Rincón A, Pereyra M, D'Giano C, et al. Fast statistical model-based classification of epileptic EEG signals[J]. Biocybern Biomed Eng, 2018, 38(4): 877–889. doi: 10.1016/j.bbe.2018.08.002
    Breiman L. Random forests[J]. Mach Learn, 2001, 45(1): 5–32. doi: 10.1023/A:1010933404324
    Flach P. Machine learning[M]. Cambridge: Cambridge University Press, 2012.
    Manzouri F, Heller S, Dümpelmann M, et al. A comparison of machine learning classifiers for energy-efficient implementation of seizure detection[J]. Front Syst Neurosci, 2018, 12: 43. doi: 10.3389/fnsys.2018.00043
    Le Douget JE, Fouad A, Maskani Filali M, et al. Surface and intracranial EEG spike detection based on discrete wavelet decomposition and random forest classification[C]//Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Seogwipo, South Korea: IEEE, 2017: 475-478.
    Donos C, Dümpelmann M, Schulze-Bonhage A. Early seizure detection algorithm based on intracranial EEG and random forest classification[J]. Int J Neural Syst, 2015, 25(5): 1550023. doi: 10.1142/S0129065715500239
    López S, Suarez G, Jungreis D, et al. Automated identification of abnormal adult EEGs[C]//Proceedings of 2015 IEEE Signal Processing in Medicine and Biology Symposium. Philadelphia, PA, USA: IEEE, 2015.
    Shoeb A, Edwards H, Connolly J, et al. Patient-specific seizure onset detection[J]. Epilepsy Behav, 2004, 5(4): 483–498. doi: 10.1016/j.yebeh.2004.05.005
    Goldberger AL, Amaral LA, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals[J]. Circulation, 2000, 101(23): E215–E220.
    Sanei S. Adaptive processing of brain signals[M]. United Kingdom: Wiley, 2013.
    Bahill AT, McDonald JD. Frequency limitations and optimal step size for the two-point central difference derivative algorithm with applications to human eye movement data[J]. IEEE Trans Biomed Eng, 1983, 30(3): 191–194.
    Tseng CC, Lee SL. Design of digital differentiator using difference formula and Richardson extrapolation[J]. IET Sign Process, 2008, 2(2): 177–188. doi: 10.1049/iet-spr:20070145
    Semmlow JL, Griffel B. Biosignal and medical image processing[M]. 3rd ed. Boca Raton: CRC Press, 2014.
    Hansen PC, Pereyra V, Scherer G. Least squares data fitting with applications[M]. Baltimore: Johns Hopkins University Press, 2013.
    Neuhauser C. Calculus for biology and medicine[M]. 2nd ed. Upper Saddle River, NJ: Pearson, 2004.
    Samarasinghe G, Sowmya A, Moses DA. A semi-quantitative analysis model with parabolic modelling for DCE-MRI sequences of prostate[C]//Proceedings of 2014 International Conference on Digital Image Computing: Techniques and Applications. Wollongong, NSW, Australia: IEEE, 2014.
    Zhang C, Ma YQ. Ensemble machine learning: methods and applications[M]. Boston, MA: Springer, 2012.
    Quintero-Rincón A, Carenzo C, Ems J, et al. Spike-and-wave epileptiform discharge pattern detection based on Kendall's Tau-b coefficient[J]. Appl Med Inform, 2019, 41(1): 1–8.
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