Reconfigurable Hardware Design for Automatic Epilepsy Seizure Detection using EEG Signals

  • S. S. Rafiammal Department of Electronics and Communication Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, India http://orcid.org/0000-0002-5916-6924
  • D. N. Jamal Department of Electronics and Communication Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, India
  • S. K. Mohideen Department of Electronics and Communication Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, India
Keywords: seizure detection, FPGA, high level synthesis, Mahalanobis distance, automatic detection

Abstract

Reconfigurable circuit designs for automatic seizure detection devices are essential to prevent epilepsy affected people from severe injuries and other health-related problems. In this proposed design, an automatic seizure detection algorithm based on the Linear binary Support Vector Machine learning algorithm (LSVM) is developed and implemented in a Field-Programmable Gate Array (FPGA). The experimental results showed that the mean detection accuracy is 86% and sensitivity is 97%. The resource utilization of the implemented design is less when compared to existing hardware implementations. The power consumption of the proposed design is 76mW at 100MHz. The experimental results assure that a physician can make use of this proposed design in detecting seizure events.

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References

M. A. B. Altaf, J. Yoo, “A 1.83 μJ/classification, 8-channel, patient-specific epileptic seizure classification SoC using a non-linear Support Vector Machine”, IEEE Transactions on Biomedical Circuits and Systems, Vol. 10, No. 1, pp. 49–60, 2015

Y. Wang, Z. Li, L. Feng, H. Bai, C. Wang, “Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection”, IET Circuits, Devices & Systems, Vol. 12, No. 1, pp. 108–115, 2018

J. B. M. Hugle, S. Heller, M. Watter, M. Blum, F. Manzouri, M. Duempelmann, A. Schulze-Bonhage, P. Woias, J. Boedecker, “Early seizure detection with an energy-efficient convolutional neural network on an implantable microcontroller”, International Joint Conference on Neural Networks, Rio de Janeiro, Brazil, July 8-13, 2018

L. Feng, Z. Li, Y. Wang, “VLSI design of SVM-based seizure detection system with on-chip learning capability”, IEEE Transactions on Biomedical Circuits and Systems, Vol. 12, No. 1, pp. 171–181, 2018

J. Yoo, L. Yan, D. El-Damak, M. Bin Altaf, A. Shoeb, H. J. Yoo, A. Chandrakasan, “An 8-channel scalable EEG acquisition SoC with fully integrated patient-specific seizure classification and recording processor”, 2012 IEEE International Solid-State Circuits Conference, San Francisco, CA, USA, February 19-23, 2012

A. Subasi, J. Kevric, M. A. Canbaz, “Epileptic seizure detection using hybrid machine learning methods”, Neural Computing and Applications, Vol. 31, pp. 317–325, 2017

Y. Elhazek, A. Ibrahim, M. Amer, A. Abubakr, H. Mostafa, “Hardware accelerated epileptic seizure detection system using Support Vector Machine”, 8th International Conference on Modern Circuits and Systems Technologies, Thessaloniki, Greece, May 13-15, 2019

A. N. Tripathi, N. Agrawal, “Epileptic seizure detection using empirical mode decomposition based fuzzy entropy and Support Vector Machine”, in: Proceedings of the Sixth International Conference on Green and Human Information Technology, Springer, 2019

B. Richhariya, M. Tanveer, “EEG signal classification using universum support vector machine”, Expert Systems with Appications, Vol. 106, pp. 169–182, 2018

E. H. Houssein, A. Hamad, A. E. Hassanien, A. A. Fahmy, “Epileptic detection based on whale optimization enhanced support vector machine”, Journal of Information and Optimization Sciences, Vol. 40, No. 3, pp. 699–723, 2019

D. Virmani, N. Jain, A. Srivastav, M. Mittal, S. Mittal, “An enhanced binary classifier incorporating weighted scores”, Engineering, Technology & Applied Science Research, Vol. 8, No. 2, pp. 2853–2858, 2018

EEG Time Series Download Page, available at: http://epileptologie-bonn.de/cms/front_content.php?idcat=193&lang=3

R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, C. E. Elger, “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, Vol. 64, No. 6, Article ID 061907, 2001

K. D. Tzimourta, L. G. Astrakas, A. M. Gianni, A. T. Tzallas, N. Giannakeas, I. Paliokas, D. G. Tsalikakis, M. G. Tsipouras, “Evaluation of window size in classification of epileptic short-term EEG signals using a brain computer interface software”, Engineering, Technology & Applied Science Research, Vol. 8, No. 4, pp. 3093–3097, 2018

B. E. Boser, I. M. Guyon, V. N. Vapnik, “A training algorithm for optimal margin classifiers”, Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, USA, July 27-29, 1992

X. Song, H. Wang, L. Wang, “FPGA implementation of a Support Vector Machine based classification system and its potential application in smart grid”, IEEE 2014 11th International Conference on Information Technology: New Generations, Las Vegas, USA, April 7-9, 2014

M. Dossis, G. Dimitriou, “Are HLS tools healthy ?”, Engineering, Technology & Applied Science Research, Vol. 5, No. 2, pp. 790–794, 2015

S Tamilarasi J, Sundararajan, “FPGA based seizure detection and control for brain computer interface”, Cluster Computing, Vol. 22, No. 5, pp. 11841–11848, 2019

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