poster

A low-power "near-threshold" epileptic seizure detection processor with multiple algorithm programmability

Authors:

Himanshu S. Markandeya,

Shriram Raghunathan,

Pedro P. Irazoqui,

Kaushik RoyAuthors Info & Claims

ISLPED '12: Proceedings of the 2012 ACM/IEEE international symposium on Low power electronics and design

Pages 285 - 290

https://doi.org/10.1145/2333660.2333725

Published: 30 July 2012 Publication History

ISLPED '12: Proceedings of the 2012 ACM/IEEE international symposium on Low power electronics and design

A low-power "near-threshold" epileptic seizure detection processor with multiple algorithm programmability

Pages 285 - 290

Abstract
References

Abstract

In this paper, we developed and implemented a low-power multiple-algorithm processor for detection of epileptic seizures with high efficacy. Four different algorithms - The Coastline, Hjorth parameter, Energy and Non-linear energy metric based algorithms - are used in our analysis. The Boolean logical combinations of these algorithms allow programmability according to the patient specific needs and improve efficacy by almost 10%. The system implemented in 65-nm TSMC technology consumes about 13.1 μW (measured) at scaled supply voltage of 400mV. Programmability using Boolean logic helps in improving the efficacy resulting in almost an error-free detection.

References

[1]

http://www.eilepsyfoundation.org

[2]

Begley, C. E., Famulari, M., Annegers, J. F., Lairson, D. R., Reynolds, T. F., Coan, S, et al., 2000, The cost of epilepsy in the United States: an estimate from population-based clinical and survey data. Epilepsia, 41(3), 342--351.

[3]

Ghai, R. S., Bikson, M., Durand, D. M., 2000, Effects of applied electric fields on low-calcium epileptiform activity in the CA1 region of rat hippocampal slices. J. of Neurophysiology, 84,274--280.

[4]

Sun, F. T., Morrell, M. J., Wharen, R. E., 2008, Responsive cortical stimulation for the treatment of epilepsy. Neurotherapeutics, 5, 68--74.

[5]

Labar, D., Murphy, J. and Tecoma, E., 1999, Vagus nerve stimulation for medication-resistant generalized epilepsy, Neurology, 52, 1510--1512.

[6]

Politsky, J. M., Estellar, R., Murro, A. M., Smith, J. R., Ray, P., Park, Y. D., et al., 2005, Effects of electrical stimulation paradigm on seizure frequency in medically intractable partial seizure patients with a cranially implanted responsive cortical neurostimulator. In Proc. of the Annual Meeting of American Epilepsy Society (AES).

[7]

D'Allesandro, M., Esteller, R., Vachtsevanos, G., Hinson, A., Echauz, J., Litt, B., 2003, Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients. IEEE Transactions on Biomedical Engineering; 50, 603--613.

[8]

Zarjam, P.; Mesbah, M. Discrete wavelet transform based seizure detection in newborns EEG signals, 2003, In: Proc. 7th International Symposium on Signal Processing and Its Applications, 2, 459--462.

[9]

Webber, W. R. S., Ronald, P., Lesser, Richardson, R. T., Wilson, K., An approach to seizure detection using an artificial neural network (ANN), 1996, Electroencephalography and Clinical Neurophysiology, 98(4), 250--272.

[10]

Raghunathan, S., Gupta, S. K., Ward, M. P., Worth, R. M., Roy, K., Irazoqui, P. P., 2009, The design and hardware implementation of low-power real-time seizure detection algorithm, J. Neural engineering, 6( 5).

[11]

Markandeya, H., Karakonstantis, G., Raghunathan, S., Irazoqui, P. P., and Roy, K., 2010, Low-power DWT-based quasi-averaging algorithm and architecture for epileptic seizure detection, Proc. of the 16th ACM/IEEE international symposium on Low power electronics and design (ISLPED '10), 301--306.

Digital Library

[12]

Raghunathan, S., Gupta, S. K., Markandeya, H. S., Roy, K., Irazoqui, P. P., 2010, A hardware-algorithm co-design approach to optimize seizure detection algorithms for implantable applications, J. of Neuroscience Methods, 193(1), 106--117.

[13]

White, A. M., Williams, P. A., Ferraro, D. J., Clark, S., Kadam, S. D., Dudek, F. E. et al., 2006, Efficient unsupervised algorithms for the detection of seizures in continuous EEG recordings from rats after brain injury, J. of Neuroscience Methods, 152, 255--266.

[14]

Kaiser, J. F., 1990 "On a simple algorithm to calculate the 'energy' of a signal," International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 1, 381--384.

[15]

Zaveri, H. P., Williams, W. J., and Sackellares, J. C., Energy Based Detection of Seizures, 1993, 15th Annual International Conference on Engineering and Medicine in Biology, 363--364.

[16]

Hjorth, B., An on-line transformation of EEG scalp potentials into orthogonal source derivations, 1975, Electroencephalography and Clinical Neurophysiology, 39, 526--530.

[17]

Pfurtscheller, G., Lopes da Silva, F.H., 1999, Event-related EEG/MEG synchronization and desynchronization: basic principles, Clinical Neurophysiology, 110, 1842--1857.

Cited By

Markandeya HRoy K(2016)Low-Power System for Detection of Symptomatic Patterns in Audio Biological SignalsIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2016.252186924:8(2679-2688)Online publication date: Aug-2016
https://doi.org/10.1109/TVLSI.2016.2521869
Markandeya HIrazoqui PRoy K(2015)Low-Energy Two-Stage Algorithm for High Efficacy Epileptic Seizure DetectionIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2014.230279823:1(208-212)Online publication date: Jan-2015
https://doi.org/10.1109/TVLSI.2014.2302798

Index Terms

A low-power "near-threshold" epileptic seizure detection processor with multiple algorithm programmability
1. Hardware
  1. Integrated circuits
    1. Interconnect
      1. Input / output circuits
  2. Very large scale integration design
    1. Application-specific VLSI designs
      1. Application specific processors

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Published In

cover image ACM Conferences

ISLPED '12: Proceedings of the 2012 ACM/IEEE international symposium on Low power electronics and design

July 2012

438 pages

ISBN:9781450312493

DOI:10.1145/2333660

General Chairs:
Naresh Shanbhag
University of Illinois at Urbana-Champaign, USA
,
Massimo Poncino
Politecnico di Torino, Italy
,
Program Chairs:
Pai H. Chou
University of California, Irvine / NTHU
,
Ajith Amerasekera
Texas Instruments, USA

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 30 July 2012

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ISLPED'12

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SIGDA

ISLPED'12: International Symposium on Low Power Electronics and Design

July 30 - August 1, 2012

California, Redondo Beach, USA

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Overall Acceptance Rate 398 of 1,159 submissions, 34%

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Cited By

Markandeya HRoy K(2016)Low-Power System for Detection of Symptomatic Patterns in Audio Biological SignalsIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2016.252186924:8(2679-2688)Online publication date: Aug-2016
https://doi.org/10.1109/TVLSI.2016.2521869
Markandeya HIrazoqui PRoy K(2015)Low-Energy Two-Stage Algorithm for High Efficacy Epileptic Seizure DetectionIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2014.230279823:1(208-212)Online publication date: Jan-2015
https://doi.org/10.1109/TVLSI.2014.2302798

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