Dual tree complex wavelet transform based analysis of epileptiform discharges

Original Research

Abstract

Diagnosis of epileptic seizures entails visual inspection of complex seizure patterns which is a tedious task. Development of automated systems for analysing brain activity would significantly minimise the epilepsy treatment gap by providing assistance to neurophysiologists. Present research work is intended to provide insight to the epileptiform discharges during the seizures using dual tree complex wavelet transform. Algorithm is developed using publically available data from Bonn University. Statistical and nonlinear features, selected on the basis of Bhattacharyya distance, are extracted from EEG segments to demarcate the seizure and nonseizure EEG boundaries. Quadratic classification of EEG features followed by k-fold cross validation with varying train to test ratios is employed to develop a generalised robust model. Performance of classifier is accessed in terms of statistical parameters.

Keywords

Classification Dual tree complex wavelet transform EEG K-fold cross validation Seizure detection 

Notes

Acknowledgements

The authors would like to thank Prof. Nick Kingsbury (University of Cambridge, UK) for providing the DTCWT toolbox and Professor Ralph G. Andrzejak (University of Bonn, Germany) for making the database publically available.

References

  1. 1.
    WHO (2010) Epilepsy in the WHO Eastern Mediterranean Region: bridging the gap. Regional Office for the Eastern Mediterranean. http://apps.who.int/iris/handle/10665/119905. Accessed 12 Feb 2017
  2. 2.
    WHO (2017). Epilepsy fact sheet. http://www.who.int/mediacentre/factsheets/fs999/en/. Accessed 28 Feb 2017
  3. 3.
    Alickovic E, Kevric J, Subasi A (2018) Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomed Signal Process Control 39:94–102CrossRefGoogle Scholar
  4. 4.
    Zahra A, Kanwal N, UR Rehman N, Ehsan S, McDonald-Maier KD (2017) Seizure detection from EEG signals using multivariate empirical mode decomposition. Comput Biol Med 88:132–141CrossRefGoogle Scholar
  5. 5.
    Rafi N, Khan YU, Farooq O (2014) Epileptic seizure detection: reformation of the traditional method on scalp recorded electroencephalogram. In: International conference on emerging trends in electrical engineeringGoogle Scholar
  6. 6.
    Das AB, Bhuiyan MIH, Alam SS (2016) Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection. SIViP 10(2):259–266CrossRefGoogle Scholar
  7. 7.
    Das AB, Bhuiyan MIH, Alam SS (2014) A statistical method for automatic detection of seizure and epilepsy in the dual tree complex wavelet transform domain. In: 3rd International conference on informatics, electronics & visionGoogle Scholar
  8. 8.
    Sareen S, Sood SK, Gupta SK (2016) A cloud-based seizure alert system for epileptic patients that uses higher-order statistics. Comput Sci Eng 5:56–67CrossRefGoogle Scholar
  9. 9.
    Farooq O, Khan YU (2010) Automatic seizure detection using higher order moments. In: International conference on recent trends in information, telecommunication and computingGoogle Scholar
  10. 10.
    Liang SF, Wang HC, Chang WL (2010) Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP J Adv Signal Process 2010(1):853434CrossRefGoogle Scholar
  11. 11.
    Joo HS, Han SH, Lee J, Jang DP, Kang JK, Woo J (2017) Spectral analysis of acceleration data for detection of generalized tonic–clonic seizures. Sensors 17:481–492CrossRefGoogle Scholar
  12. 12.
    Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37(12):8659–8666CrossRefGoogle Scholar
  13. 13.
    Lee J, Park J, Yang S, Kim H, Choi YS, Kim HJ, Lee HW, Lee BU (2017) Early seizure detection by applying frequency-based algorithm derived from the principal component analysis. Front Neuroinform 11:1–2CrossRefGoogle Scholar
  14. 14.
    Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) 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 64:061907CrossRefGoogle Scholar
  15. 15.
    Bajaj V, Pachori RB (2012) Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed 16:1135–1141CrossRefGoogle Scholar
  16. 16.
    Selesnick IW, Baraniuk RG, Kingsbury NG (2005) The dual tree complex wavelet transform. IEEE Signal Process Mag 22:123–151CrossRefGoogle Scholar
  17. 17.
    Khan YU, Gotman J (2003) Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin Neurophysiol 114:898–908CrossRefGoogle Scholar
  18. 18.
    Khan AT, Husain I, Khan YU (2015) Seizure onset patterns in EEG and their detection using statistical measures. In: 12th IEEE India international conference (INDICON) on electronics, energy, environment, communication, computer, controlGoogle Scholar
  19. 19.
    Swami P et al (2016) A novel robust diagnostic model to detect seizures in electroencephalography. Expert Syst Appl 56:116–130CrossRefGoogle Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringAligarh Muslim UniversityAligarhIndia

Personalised recommendations