Journal of Medical and Biological Engineering

, Volume 37, Issue 6, pp 843–857 | Cite as

Classification of Normal, Ictal and Inter-ictal EEG via Direct Quadrature and Random Forest Tree

  • Enas Abdulhay
  • Maha Alafeef
  • Arwa Abdelhay
  • Areen Al-Bashir
Original Article


This paper presents an accurate nonlinear classification method that can help physicians diagnose seizure in electroencephalographic (EEG) signal characterized by a disturbance in temporal and spectral content. This is accomplished by applying four steps. First, different EEG signals containing healthy, ictal and seizure-free (inter-ictal) activities are decomposed by empirical mode decomposition method. The instantaneous amplitudes and frequencies of resulted bands (intrinsic mode functions, IMF) are then tracked by the direct quadrature method (DQ). In contrast to other approaches, DQ cancels the effect of amplitude modulation on frequency calculation. The dissociation between instantaneous amplitude and frequency information is therefore fully achieved to avoid features confusion. Afterwards, the Shannon entropy values of both sets of instantaneous values (amplitudes and frequencies)—related to every IMF—are calculated. Finally, the obtained entropy values are classified by random forest tree. The proposed procedure yields 100% accuracy for (healthy)/(ictal) and 98.3–99.7% for (healthy)/(ictal)/(interictal) classification problems. The suggested method is hence robust, accurate, fast, user-friendly, data driven with open access interpretability.


EEG Forest tree Ictal Direct quadrature Decomposition Entropy Instantaneous 


  1. 1.
    Sharma, R., Pachori, R. B., & Acharya, U. R. (2015). Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy, 17, 669–691.CrossRefGoogle Scholar
  2. 2.
    Srinivasan, V., Eswaran, C., & Sriraam, N. (2005). Artificial neural network based epileptic detection using time-domain and frequency domain features. Journal of Medical Systems, 29(6), 647–660.CrossRefGoogle Scholar
  3. 3.
    Tzallas, A. T., Karvelis, P. S., Katsis, C. D., Fotiadis, D. I., Giannopoulos, S., & Konitsiotis, S. (2006). A method for classification of transient events in EEG recordings: Application to epilepsy diagnosis. Methods of Information inMedicine, 45(6), 610–621.Google Scholar
  4. 4.
    Mormann, F., Lehnertz, K., David, P., & Elger, C. E. (2000). Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D: Nonlinear Phenomena, 144, 358–369.CrossRefMATHGoogle Scholar
  5. 5.
    Lehnertz, K., & Elger, C. E. (1995). Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. Electroencephalography and Clinical Neurophysiology, 95, 108–117.CrossRefGoogle Scholar
  6. 6.
    Prior, P. F., Virden, R. S. M., & Maynard, D. E. (1973). An EEG device for monitoring seizure discharges. Epilepsia, 14(4), 367–372.CrossRefGoogle Scholar
  7. 7.
    Gotman, J. (1982). Automatic recognition of epileptic seizures in the EEG. Electroencephalography and Clinical Neurophysiology, 54(5), 530–540.CrossRefGoogle Scholar
  8. 8.
    Webber, W. R. S., Lesser, R. P., Richardson, R. T., & Wilson, K. (1996). An approach to seizure detection using an artificial neural network (ANN). Electroencephalography and Clinical Neurophysiology, 98(4), 250–272.CrossRefGoogle Scholar
  9. 9.
    Harding, G. W. (1993). An automated seizure monitoring system for patients with indwelling recording electrodes. Electroencephalography and Clinical Neurophysiology, 86(6), 428–437.CrossRefGoogle Scholar
  10. 10.
    Pachori, R. B. (2008). Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. The IEEE Signal Processing Letters. doi: 10.1155/2008/293056.Google Scholar
  11. 11.
    Huang, N., Shen, Z., Long, S., Wu, M., Shih, H. H., Zheng, Q., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454, 903–995.MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Oweis, R., & Abdulhay, E. (2011). Seizure identification in EEG signals utilizing Huang and Hilbert transforms. BioMedical Engineering OnLine, 10, 38.CrossRefGoogle Scholar
  13. 13.
    Pachori, R. B., & Bajaj, V. (2011). Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Computer Methods and Programs in Biomedicine, 104, 373–381.CrossRefGoogle Scholar
  14. 14.
    Pachori, R. B., Sharma, R., & Patidar, S. (2015). Classification of normal and epileptic seizure EEG signals based on empirical mode decomposition. Complex System Modelling and Control through Intelligent Soft Computations, 319, 367–388.Google Scholar
  15. 15.
    Pachori, R. B., & Patidar, S. (2014). Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Computer Methods and Programs in Biomedicine, 113, 494–502.CrossRefGoogle Scholar
  16. 16.
    Sharma, R., & Pachori, R. B. (2015). Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Systems with Applications, 42, 1106–1117.CrossRefGoogle Scholar
  17. 17.
    Kumar, T. S., Kanhangad, V., & Pachori, R. B. (2014). Classification of seizure and seizure-free EEG signals using multi-level local patterns. In Proceedings of the IEEE 19th international conference on digital signal processing, Hong Kong (pp. 646–650).Google Scholar
  18. 18.
    Li, S., Zhou, W., Yuan, Q., Geng, S., & Cai, D. (2013). Feature extraction and recognition of ictal EEG using EMD and SVM. Computers in Biology and Medicine, 43, 807–816.CrossRefGoogle Scholar
  19. 19.
    Zhu, G., Li, Y., Wen, P. P., Wang, S., & Xi, M. (2013). Epileptogenic focus detection in intracranial EEG based on delay permutation entropy. AIP Conference Proceedings, 1559, 31–36.CrossRefGoogle Scholar
  20. 20.
    Sharma R, Pachori, R. B., & Gautam, S. (2014). Empirical mode decomposition based classification of focal and non-focal EEG signals, In Proceedings of the international conference on medical biometrics, Shenzhen (pp. 135–140).Google Scholar
  21. 21.
    Orosco, L., Correa, A. G., & Laciar, E. (2010). Multiparametric detection of epileptic seizures using empirical mode decomposition of eeg records. In Proceedings of 32nd annual international conference of the IEEE EMBS Buenos Aires (pp. 951–954).Google Scholar
  22. 22.
    Kiranmayi, G. R., & Udayashankara, V. (2014). EEG subband analysis using approximate entropy for the detection of epilepsy. IOSR Journal of Computer Engineering, 16(5), 21–27.CrossRefGoogle Scholar
  23. 23.
    Adeli, H., Dastidar, S. G., & Dadmehr, N. (2007). A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Transactions on Biomedical Engineering, 54(2), 205–211.CrossRefGoogle Scholar
  24. 24.
    Dastidar, S. G., Adeli, H., & Dadmehr, N. (2007). Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Transactions on Biomedical Engineering, 54(9), 1545–1551.CrossRefGoogle Scholar
  25. 25.
    Dastidar, S. G., Adeli, H., & Dadmehr, N. (2008). Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Transactions on Biomedical Engineering, 55(2), 512–518.CrossRefGoogle Scholar
  26. 26.
    Subasi, A. (2007). EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications, 32(4), 1084–1093.CrossRefGoogle Scholar
  27. 27.
    Wang, C. M., Zou, J.-Z., Zhang, J., Zhang, Z.-S., & Zhang, C.-M. (2009). Classifying detection of epileptic EEG based on approximate entropy in wavelet domain. In Proceedings of the IEEE conference on bio medical engineering and informatics (pp. 1–5).Google Scholar
  28. 28.
    Guo, L., Rivero, D., & Pazos, A. (2010). Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. Journal of Neuroscience Methods, 193, 156–163.CrossRefGoogle Scholar
  29. 29.
    Vavadi, H., Ayatollahi, A., & Mirzaei, A. (2010). A wavelet-approximate entropy method for epileptic activity detection from EEG and its sub-bands. Journal Biomedical Science and Engineering, 3, 1182–1189.CrossRefGoogle Scholar
  30. 30.
    Tzallas, A. T., Tsipouras, M. G., & Fotiadis, D. I. (2009). Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Transactions on Information Technology in Biomedicine, 13(5), 703–710.CrossRefGoogle Scholar
  31. 31.
    Srinivasan, V., Eswaran, C., & Sriraam, N. (2007). Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Transactions on Information Technology in Bio Medicine, 11(3), 288–295.CrossRefGoogle Scholar
  32. 32.
    Polat, K., & Günes, S. (2007). Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Applied Mathematics and Computation, 32(2), 625–631.MathSciNetMATHGoogle Scholar
  33. 33.
    Tzallas, T., Tsipouras, M. G., & Fotiadis, D. I. (2007). Automatic seizure detection based on time-frequency analysis and artificial neural networks. Computational Intelligence and Neuroscience, 7(3), 1–13.CrossRefGoogle Scholar
  34. 34.
    Güler, N. F., Ubeyli, E. D., & Güler, I. (2005). Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Systems with Applications, 29(3), 506–514.CrossRefGoogle Scholar
  35. 35.
    Abdulhay E, Guméry PY, Fontecave-Jallon J, Baconnier P. (2009). Cardiogenic oscillations extraction in inductive plethysmography: Ensemble empirical mode decomposition. In IEEE EMBS proceedings, Minnesota (pp. 2240–2243).Google Scholar
  36. 36.
    Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 64, 061907-1–061907-8.CrossRefGoogle Scholar
  37. 37.
    Huang, N. E., & Wu, Z. (2008). A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Reviews of Geophysics, 46, 228–251.CrossRefGoogle Scholar
  38. 38.
    Kschischang, F. R. (2006). The Hilbert Transform. Toronto: University of Toronto.Google Scholar
  39. 39.
  40. 40.
    Random, F. T., & Leo, B. (2001). Random forests. Machine Learning., 45(1), 5–32.CrossRefMATHGoogle Scholar
  41. 41.
  42. 42.
    Seni, G., & Elder, J. F. (2010). Ensemble methods in data mining: Improving accuracy through combining predictions. Synthesis Lectures on Data Mining and Knowledge Discovery, 2(1), 1–126.CrossRefGoogle Scholar
  43. 43.
    Das, A. B., Bhuiyan, M. I. H., & Alam, S. M. S. (2016). Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection. Signal Image and Video Processing, 10(2), 259–266.CrossRefGoogle Scholar
  44. 44.
    Kannathal, N., Choo, M. L., Acharya, U. R., & Sadasivan, P. K. (2005). Entropies for detection of epilepsy in EEG. Computer Methods and Programs in Biomedicine, 80(3), 187–194.CrossRefGoogle Scholar
  45. 45.
    Palani Thanaraj, K., & Chitra, K. (2014). Multichannel feature extraction and classification of epileptic states using higher order statistics and complexity measures. International Journal of Engineering and Technology, 6(1), 102–109.Google Scholar
  46. 46.
    Li, P., Karmakar, C., Yan, C., Palaniswami, M., & Liu, C. (2016). Classification of 5-S epileptic EEG recordings using distribution entropy and sample entropy. Frontiers in Physiology, 7, 136.Google Scholar
  47. 47.
    Noertjahjani, S., Susanto, A., Hidayat, R., & Wibowo, S. (2016). Ictal epilepsy and normal eeg feature extraction based on PCA, KNN and SVM classification. Journal of Theoretical and Applied Information Technology, 83(1), 100–106.Google Scholar
  48. 48.
    Nigam, V. P., & Graupe, D. (2004). A neural-network-based detection of epilepsy. Neurological Research, 26(1), 55–60.CrossRefGoogle Scholar
  49. 49.
    Karimoi, R. Y., & Karimoi, A. Y. (2014). Classification of EEG signals using hyperbolic tangent-tangent plot. International Journal of Intelligent Systems and Applications, 08, 39–45.CrossRefGoogle Scholar
  50. 50.
    Sadati, N., Mohseni, H. R., & Maghsoudi, A. (2006). Epileptic seizure detection using neural fuzzy networks. In Proceedings of IEEE international conference on fuzzy systems, Vancouver (pp. 596–600).Google Scholar
  51. 51.
    Guo, L., Rivero, D., Dorado, J., Munteanu, C. R., & Pazos, A. (1042). Automatic feature extraction using genetic programming: An application to epileptic EEG classification. Expert Systems with Applications, 2011, 38.Google Scholar
  52. 52.
    Ubeyli, E. D. (2006). Analysis of EEG signals using Lyapunov exponents. Neural Network World, 16(3), 257.Google Scholar
  53. 53.
    Orhan, U., Hekim, M., & Ozer, M. (2011). EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Systems with Applications, 38, 13475.CrossRefGoogle Scholar
  54. 54.
    Wang, Y., Zhou, W., Yuan, Q., Li, X., Meng, Q., Zhao, X., et al. (2013). Comparison of ictal and interictal EEG signals using fractal features. International Journal of Neural Systems, 23(6), 1350028.CrossRefGoogle Scholar
  55. 55.
    Parvez, M. Z., Paul, M., & Antolovich, M. (2015). Detection of pre-stage of epileptic seizure by exploiting temporal correlation of EMD decomposed EEG signals. Journal of Medical and Bioengineering, 4(2), 110–116.CrossRefGoogle Scholar
  56. 56.
    Yayik, A., Yildirim, E., Kutlu, Y., & Yildirim, S. (2014). Epileptic state detection: Pre-ictal, inter-ictal, ictal. International Journal of Intelligent Systems and Applications in Engineering, 3(1), 14–18.CrossRefGoogle Scholar
  57. 57.
    Gajic, D., Djurovic, Z., Di Gennaro, S., & Gustafsson, F. (2014). Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition. Biomedical Engineering: Applications, Basis and Communications, 26(2), 1450021.Google Scholar
  58. 58.
    Parvez, M. Z., & Paul, M. (2014). Epileptic seizure detection by analyzing EEG signals using different transformation techniques. Neurocomputing, 145, 190–200.CrossRefGoogle Scholar
  59. 59.
    Thasneem, F., Bedeeuzzaman, M., & Paul, J. (2013). Wavelet based features for classification of normal, ictal and interictal EEG signals. Journal of Medical Imaging and Health Informatics, 3(2), 301–305.CrossRefGoogle Scholar
  60. 60.
    Duque-Muñoz, L., Espinosa-Oviedo, J. J., & Castellanos-Dominguez, C. G. (2014). Identification and monitoring of brain activity based on stochastic relevance analysis of short-time EEG rhythms. BioMedical Engineering OnLine, 13, 123.CrossRefGoogle Scholar
  61. 61.
    Ramgopal, S., Thome-Souza, S., Jackson, M., Kadish, N. E., Fernández, I. S., Klehm, J., et al. (2014). Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy & Behavior, 37, 291–307.CrossRefGoogle Scholar
  62. 62.
    Argoud, F. I. M., de Azevedo, F. M., Neto, J. M., & Grillo, E. (2006). SADE3: An effective system for automated detection of epileptiform events in long-term EEG based on context information. Medical & Biological Engineering & Computing, 44(6), 459–470.CrossRefGoogle Scholar

Copyright information

© Taiwanese Society of Biomedical Engineering 2017

Authors and Affiliations

  • Enas Abdulhay
    • 1
  • Maha Alafeef
    • 1
  • Arwa Abdelhay
    • 2
  • Areen Al-Bashir
    • 1
  1. 1.Department of Biomedical Engineering, Faculty of EngineeringJordan University of Science and TechnologyIrbidJordan
  2. 2.Department of Water and Environmental Engineering, Faculty of Natural Resources EngineeringGerman Jordanian UniversityAmmanJordan

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