- Volume 42 Issue 2
DOI QR Code
Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals
- Lee, Miran (Graduate School of Information Science and Engineering, Ritsumeikan University) ;
- Ryu, Jaehwan (Technical Research Institute, Sammi Information System) ;
- Kim, Deok-Hwan (Department of Electronic Engineering, Inha University)
- Received : 2018.03.06
- Accepted : 2019.07.05
- Published : 2020.04.03
Long-term electroencephalography (EEG) monitoring is time-consuming, and requires experts to interpret EEG signals to detect seizures in patients. In this paper, we propose a novel automated method called adaptive slope of wavelet coefficient counts over various thresholds (ASCOT) to classify patient episodes as seizure waveforms. ASCOT involves extracting the feature matrix by calculating the mean slope of wavelet coefficient counts over various thresholds in each frequency subband. We validated our method using our own database and a public database to avoid overtuning. The experimental results show that the proposed method achieved a reliable and promising accuracy in both our own database (98.93%) and the public database (99.78%). Finally, we evaluated the performance of the method considering various window sizes. In conclusion, the proposed method achieved a reliable seizure detection performance with a short-term window size. Therefore, our method can be utilized to interpret long-term EEG results and detect momentary seizure waveforms in diagnostic systems.
Grant : Development of human-friendly human-robot interaction technologies using human internal emotional states, Intelligent Mobile Edge Cloud Solution for Connected Car
Supported by : National Research Foundation of Korea, Ministry of Trade, Industry & Energy (MI), Institute for Information & communications Technology Promotion(IITP)
- F. Mormann et al., Seizure prediction: the long and winding road, Brain 130 (2006), no. 2, 314-333. https://doi.org/10.1093/brain/awl241
- B. Litt and K. Lehnertz, Seizure prediction and the preseizure period, Curr. Opin. Neurol. 14 (2002), no. 2. 173-177.
- V. Joshi, R. B. Pachori, and A. Vijesh, Classification of ictal and seizure‐free EEG signals using fractional linear prediction, Biomed. Signal Process. Contr. 9 (2013), 1-5.
- L. Guo et al., Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks, J. Neurosci. Methods 191 (2010), no. 1, 101-109. https://doi.org/10.1016/j.jneumeth.2010.05.020
- A. Subasi and M. I. Gursoy, EEG signal classification using PCA, ICA, LDA and support vector machines, Expert Syst. Applicat. 37 (2010), no. 12, 8659-8666. https://doi.org/10.1016/j.eswa.2010.06.065
- S. Xie et al., Feature extraction via dynamic PCA for epilepsy diagnosis and epileptic seizure detection, in Proc. IEEE Int. Work. Machine Lear. Signal Process., Kittila, Finland, 2010, pp. 337-342.
- S. Ghosh‐Dastidar, H. Adeli, and N. Daddmehr, Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection, IEEE Trans. Biomed. Eng. 55 (2008), no. 2, 512-518. https://doi.org/10.1109/TBME.2007.905490
- J. Kevric and A. Subasi, The effect of multiscale PCA de-noising in epileptic seizure detection, J. Med. Syst. 38 (2014), no. 10, 131-143. https://doi.org/10.1007/s10916-014-0131-0
- Y. Kumar, M. L. Dewal, and R. S. Anand, Epileptic seizures detection in EEG using DWT‐based ApEn and artificial neural network, Signal Image Video Process 8 (2012), no. 7, 1323-1334. https://doi.org/10.1007/s11760-012-0362-9
- M. Sharmar, R. B. Pachori, and U. R. Acharya, A new approach to characterize epileptic seizures using analytic time‐frequency flexible wavelet transform and fractal dimension, Pattern Recogn. Lett. 94 (2017), 172-179. https://doi.org/10.1016/j.patrec.2017.03.023
- D. Bhati, R. B. Pachori, and V. M. Gadre, Time-frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification, Digit. Signal Process. 62 (2017), 259-273. https://doi.org/10.1016/j.dsp.2016.12.004
- H. Ocak, Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy, Expert Syst. Applicat. 36 (2009), no. 2, 2027-2036. https://doi.org/10.1016/j.eswa.2007.12.065
- S. Nasehi and H. Pourghassem, Automatic prediction of epileptic seizure using kernel fisher discriminant classifiers, in Proc. Int. Compt. Bio-Med. Instrum., Wuhan, China, 2011, pp. 200-203.
- A. K. Tiwari et al., Automated diagnosis of epilepsy using keypoints based local binary pattern of EEG signals, IEEE J. Biomed. Health. 21 (2017), no. 4, 888-896. https://doi.org/10.1109/JBHI.2016.2589971
- A. Bhattacharyya et al., Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals, Appl. Sci. 7 (2017), no. 4, 385-402. https://doi.org/10.3390/app7040385
- R. B. Pachori and S. Patidar, Epileptic seizure classification in EEG signals using second‐order difference plot of intrinsic mode functions, Comput. Methods Programs Biomed. 113 (2014), no. 2, 494-502. https://doi.org/10.1016/j.cmpb.2013.11.014
- V. Bajaj and R. B. Pachori, Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals, Biomed. Eng. Lett. 3 (2013), no. 1, 17-21. https://doi.org/10.1007/s13534-013-0084-0
- V. Bajaj and R. B. Pachori, Classification of seizure and nonseizure EEG signals using empirical mode decomposition, IEEE Trans. Inf. Technol. Biomed. 16 (2012), no. 6, 1135-1142. https://doi.org/10.1109/TITB.2011.2181403
- R. B. Pachori and V. Bajaj, Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition, Comput. Methods Programs Biomed. 104 (2011), no. 3, 373-381. https://doi.org/10.1016/j.cmpb.2011.03.009
- R. B. Pachori, Discrimination between ictal and seizure‐free EEG signals using empirical mode decomposition, Res. Lett. Signal Process. 2008 (2008), no. 14, 1-5.
- I. Conradsen et al., Automated algorithm for generalised tonicclonic epileptic seizure onset detection based on sEMG zero‐crossing rate, IEEE Trans. Biomed. Eng. 59 (2012), no. 2, 579-585. https://doi.org/10.1109/TBME.2011.2178094
- S. Elgohary, S. Eldawlathly, and M. I. Khalil, Epileptic seizure prediction using zero‐crossings analysis of EEG wavelet detail coefficients, in Proc. IEEE Conf. Comput. Intell. Bioinformatics. Comput. Biol., Chiang, Thailand, Oct. 2016, pp. 1-6.
- A. S. Zandi et al., Predicting epileptic seizures in scalp EEG based on a variational bayesian faussian mixture model of zero‐crossing intervals, IEEE Trans. Biomed. Eng. 60 (2013), no. 5, 1401-1413. https://doi.org/10.1109/TBME.2012.2237399
- A. S. Zandi et al., Predicting temporal lobe epileptic seizures based on zero-crossing interval analysis in scalp EEG, in Proc. Ann. Int. Conf. Eng. Medi. Biol. Soc., Buenos Aires, Argentina, 2010, pp. 5537-5540.
- A. Subasi, Application of adaptive neuro‐fuzzy inference system for epileptic seizure detection using wavelet feature extraction, Comput. Biol. Med. 37 (2007), no. 2, 227-244. https://doi.org/10.1016/j.compbiomed.2005.12.003
- M. R. Lee et al., Classification of both seizure and non‐seizure based on EEG signals using hidden markov model, in Proc. Int. Conf. BIGCOMP., Shanghai, China, 2018, pp. 469-474.
- R. Oostenveld and P. Praamstra, The five percent electrode system for high‐resolution EEG and ERP measurements, Clin. Neurophysiol. 112 (2001), no. 4, 713-719. https://doi.org/10.1016/S1388-2457(00)00527-7
- M. R. Lee et al., A novel R peak detection method for mobile environments, IEEE Access 6 (2018), 51227-51237. https://doi.org/10.1109/ACCESS.2018.2867329
- R. G. Andrzejak et al., Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: dependence on recording region and brain state, Phys. Rev. E. 64 (2001), no. 6, 061907:1-8. https://doi.org/10.1103/PhysRevE.64.061907
- K. P. Indiradevi et al., A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram, Comput. Biol. Med. 38 (2008), no. 7, 805-816. https://doi.org/10.1016/j.compbiomed.2008.04.010
- R. W. Wall, Simple methods for detecting zero crossing, in Proc. Ann. Conf. Industrial Elect. Soc., Roanoke, VA, USA, 2004, pp. 2477-2481.
- M. Z. Uddin, D. H. Kim, and T. S. Kim, A human activity recognition system using HMMs with GDA on enhanced independent component features, Int. Arab J. Inf. Technol. 12 (2015), no. 3, 304-310.
- M. H. Abdullah, J. M. Abullah, and M. Z. Abdullah, Seizure detection by means of hidden Markov model and stationary wavelet transform of electroencephalograph signals, in Proc. Biomed. Health Informatics, Hong Kong, China, 2012, pp. 62-65.
- R. Sharma and R. B. Pachori, Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions, Expert Syst. Applicat. 42 (2015), no. 3, 1106-1117. https://doi.org/10.1016/j.eswa.2014.08.030
- R. R. Sharma and R. B. Pachori, Time-frequency representation using IEVDHM-HT with application to classification of epileptic EEG signals, IET Sci. Meas. Technol. 12 (2017), no. 1, 72-82.
- M. Peker, B. Sen, and D. Delen, A novel method for automated diagnosis of epilepsy using complex‐valued classifiers, IEEE J. Biomed. Health Inform. 20 (2015), no. 1, 108-118. https://doi.org/10.1109/JBHI.2014.2387795
- K. Samiee, P. Kovacs, and M. Gabboouj, Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform, IEEE Trans. Biomed. Eng. 62 (2014), no. 2, 541-552. https://doi.org/10.1109/TBME.2014.2360101
- T. S. Kumar, Classification of seizure and seizure‐free EEG signals using local binary patterns, Biomed. Signal Process. Contr. 15 (2015), 33-40. https://doi.org/10.1016/j.bspc.2014.08.014
- Y. Kaya et al., 1D-local binary pattern based feature extraction for classification of epileptic EEG signals, Appl. Math. Comput. 243 (2014), 209-219.
- T. S. Kumar et al., Classification of seizure and seizure-free EEG signals using multi-level local patterns, in Proc. Int. Conf. Digit. Signal Process., Hong Kong, China, Aug. 2014, pp. 646-650.
- A. K. Jaiswal and H. Banka, Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals, Biomed. Signal Process. Contr. 34 (2017), 81-92. https://doi.org/10.1016/j.bspc.2017.01.005
- A. K. Jaiswal and H. Banka, Epileptic seizure detection in EEG signal with GModPCA and support vector machine, Biomed. Mater. Eng. 28 (2017), no. 2, 141-157.
- A. K. Jaiswal and H. Banka, Epileptic seizure detection in EEG signal using machine learning techniques, Australas. Phys. Eng. Sci. Med. 41 (2018), no. 1, 81-94. https://doi.org/10.1007/s13246-017-0610-y
- T. Gandhi et al., Expert model for detection of epileptic activity in EEG signature, Expert Syst. Appl. 37 (2010), no. 4, 3513-3520. https://doi.org/10.1016/j.eswa.2009.10.036
- N. J. Sairamya et al., Detection of epileptic EEG signal using improved local pattern transformation methods, Circ. Syst. Signal Process. 37 (2018), no. 12, 1-22. https://doi.org/10.1007/s00034-017-0737-9
- J. L. Song and R. Zhang, Automatic seizure detection using a novel EEG feature based on nonlinear complexity, in Proc. Int. Joint Conf. Neural Netw., Vancouver, Canada, 2016, pp. 1686-1695.
- Y. Kumar, M. L. Dewal, and R. S. Anand, Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine, Neurocomput. 133 (2014), 271-279. https://doi.org/10.1016/j.neucom.2013.11.009