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Detection and Cortical Localization of Ictal Signatures using Electroencephalogram Signals

PIYUSH SWAMI1, 2*, Bijaya K. Panigrahi3, Sneh Anand1, 2, Manvir Bhatia4, Manjari Tripathi5 and Tapan K. Gandhi3
  • 1 Indian Institute of Technology Delhi, Centre for Biomedical Engineering, India
  • 2 All India Institute of Medical Sciences, Biomedical Engineering Unit, India
  • 3 Indian Institute of Technology Delhi, Department of Electrical Engineering, India
  • 4 Saket City Hospital, Department of Neurology, India
  • 5 All India Institute of Medical Sciences, Department of Neurology, India

Epilepsy is a common brain disorder which affects nearly 65 million people in the world. Electroencephalography is the most important technique for the diagnosis of epilepsy till date. The ictal signatures present in electroencephalogram (EEG) signals are usually detected by visual examination by the neuro-clinician or neuro-physiologist. This is an extremely tedious and error prone work since, the EEG signals are contaminated with various artifacts. Further, locating the epicenter of seizures is even more challenging due to the non-stationary nature of EEG. Hence, application of expert models for detection and source localization of ictal signatures is extremely important. In this work, we have proposed a novel methodology for detecting ictal signatures in EEG using complex wavelet transform and fuzzy-Sugeno classifier. The minimum reduction and maximum relevance technique was applied for feature selection. The results showed ceiling level of accuracy and coherent statistical performances. The classified ictal periods were used for the performing independent component analysis and subsequently cortical source localization over the Montreal Neurological Institute head model. The outcomes stamp successful seizure detection and visualization of the ictal events over the human cortex. This development assists in fast and accurate diagnosis of epilepsy and can be of utmost use in developing and under-developed countries where the patients to neurologists’ ratio is phenomenally higher. Additionally, the cortical location of the epileptic seizures helps in predicting the progression of pharmacological treatment and possible surgical outcomes.

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References

1. Swami, P., Gandhi, T., Panigrahi, B. K., Tripathi, M., Anand, S. (2016). A novel robust diagnostic model to detect seizures in electroencephalography. Expert Systems with Applications, 56:116—130. doi: 10.1016/j.eswa.2016.02.040.
2. Swami, P., Gandhi, T., Panigrahi, B. K., Bhatia, M., Santhosh, J., Anand, S. (2016). A comparative account of modelling seizure detection system using wavelet techniques. Systems Science: Operations & Logistics special edition on Modelling and Simulation in Healthcare Systems, 1—12. doi: 10.1080/23302674.2015.1116637.
3. Ferlazzo, E., Mammone, N., Cianci, V., Gasparini, S., Gambardella, A., Labate, A., Latella, M. A., Sofia, V., Elia, M., Morabito, F. C., Aguglia, U. (2014). Permutation entropy of scalp EEG: A tool to investigate epilepsies. Suggestions from absence epilepsies. Clinical Neurophysiology, 125:1 13–20. doi: 10.1016/j.clinph.2013.06.023.

Keywords: electroencephalogram (EEG), Ictal signatures, cortical source localization, Complex wavelet transform, fuzzy-Sugeno classifier.

Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.

Presentation Type: Investigator presentations

Topic: Brain disorders

Citation: SWAMI P, Panigrahi BK, Anand S, Bhatia M, Tripathi M and Gandhi TK (2016). Detection and Cortical Localization of Ictal Signatures using Electroencephalogram Signals. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00013

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Received: 30 Apr 2016; Published Online: 18 Jul 2016.

* Correspondence: Mr. PIYUSH SWAMI, Indian Institute of Technology Delhi, Centre for Biomedical Engineering, New Delhi, Delhi, 110016, India, piyushswami@yahoo.com