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Time domain based seizure onset analysis of brain signatures in pediatric EEG

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Abstract

A comprehensive insight into the epileptiform discharges at the time of seizure onset can aid neurophysiologists in the diagnosis and treatment of epileptic seizures. Visual analysis of seizure onset patterns is often a complex and tedious task. These problems suggest the development of automated seizure onset detection systems. The present research work is oriented for automatic detection of epileptic seizures at the onset using statistical measures. A quadratic classifier with fourfold cross-validation is used to demarcate the seizure and non-seizure activity. The algorithm is evaluated for 24 patients from the CHB MIT scalp EEG database. Classifier performance is assessed in terms of sensitivity, specificity, accuracy, and latency.

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Abbreviations

ANN:

Artificial Neural Network

CHB:

Children’s Hospital Boston

DWN:

Dynamic Wavelet Network

DWT:

Discrete Wavelet Transform

EEG:

Electroencephalogram

FEBANN:

Feedforward Error Backpropagation Artificial Neural Network

FN:

False Negative

FP:

False Positive

MIT:

Massachusetts Institute of Technology

TN:

True Negative

TP:

True Positive

References

  1. Corsini J, Shoker L, Sanei S, Alarcón G (2006) Epileptic seizure predictability from scalp EEG incorporating constrained blind source separation. IEEE Trans Biomed Eng 53:790–799

    Article  Google Scholar 

  2. Shoeb A, Guttag J (2010) Application of machine learning to epileptic seizure detection. In: 27th international conference on machine learning, Haifa, Israel, June 2010, pp 975–982

  3. Prior PF, Virden RSM, Maynard DE (1973) An EEG device for monitoring seizure discharges. Epilepsia 14:367–372

    Article  Google Scholar 

  4. Ives JR (1974) The on-line computer detection and recording of spontaneous temporal lobe epileptic seizures from patients with implanted depth electrodes via a radio telemetry link. Electoencephalogr Clin Neurophysiol 37:205

    Google Scholar 

  5. Gotman J (1982) Automatic recognition of epileptic seizures in the EEG. Electoen Clin Neurophysiol 54(5):530–540

    Article  Google Scholar 

  6. Khan YU, Gotman J (2003) Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin Neurophysiol 114(5):898–908

    Article  Google Scholar 

  7. Shoeb AH (2003) Patient-specific seizure onset detection. M.S. thesis, Dept. Elect. & Comp Sc., MIT Univ., Cambridge, MA

  8. Subasi A (2005) Epileptic seizure detection using dynamic wavelet network. Expert Syst Appl 29:343–355

    Article  Google Scholar 

  9. Kharbouch AA (2012) Automatic detection of epileptic seizure onset and termination using intracranial EEG. M.S. thesis, Dept. Elect. & Comp Sc., MIT Univ., Cambridge, MA

  10. Khan AT, Khan YU (2018) Dual tree complex wavelet transform based analysis of epileptiform discharges. Int J Inf Technol 10:543. https://doi.org/10.1007/s41870-018-0149-5

    Article  Google Scholar 

  11. Achilles F, Tombari F, Belagiannis V, Loesch AM, Noachtar S, Navab N (2018) Convolutional neural networks for real-time epileptic seizure detection. Comput Methods Biomech Biomed Eng Imaging Vis 6(3):264–269. https://doi.org/10.1080/21681163.2016.1141062

    Article  Google Scholar 

  12. Subasi A, Kevric J, Abdullah Canbaz M (2019) Epileptic seizure detection using hybrid machine learning methods. Neural Comput Appl 31:317. https://doi.org/10.1007/s00521-017-3003-y

    Article  Google Scholar 

  13. Bhattacharyya A, Singh L, Pachori RB (2019) Identification of epileptic seizures from scalp EEG signals based on TQWT. In: Tanveer M, Pachori R (eds) Machine intelligence and signal analysis, vol 748. Advances in intelligent systems and computing. Springer, Singapore

    Chapter  Google Scholar 

  14. Rafiuddin N, Khan YU, Farooq O (2011) Feature extraction and classification of EEG for automatic seizure detection. In: International conference on multimedia, signal processing and communication technologies, Aligarh, pp 184–187

  15. Mihajlovic V, Patki S, Grundlehner B (2014) The impact of head movements on EEG and contact impedance: an adaptive filtering solution for motion artifact reduction. In: 36th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Chicago, pp 5064–5067

  16. 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 engineering, Kollam, pp 1–6

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Authors and Affiliations

  1. Department of Electrical Engineering, Aligarh Muslim University, Aligarh, India

    Ayesha Tooba Khan & Yusuf Uzzaman Khan

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  1. Ayesha Tooba Khan
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  2. Yusuf Uzzaman Khan
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Correspondence to Ayesha Tooba Khan.

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Khan, A.T., Khan, Y.U. Time domain based seizure onset analysis of brain signatures in pediatric EEG. Int. j. inf. tecnol. 13, 453–458 (2021). https://doi.org/10.1007/s41870-020-00596-5

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  • DOI: https://doi.org/10.1007/s41870-020-00596-5

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