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Low-Power Architecture for Epileptic Seizure Detection Based on Reduced Complexity DWT

Authors:

Kaushik RoyAuthors Info & Claims

ACM Journal on Emerging Technologies in Computing Systems (JETC), Volume 8, Issue 2

Article No.: 10, Pages 1 - 14

https://doi.org/10.1145/2180878.2180882

Published: 01 June 2012 Publication History

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Abstract

In this article, we present a low-power, user-programmable architecture for discrete wavelet transform (DWT) based epileptic seizure detection algorithm. A simplified, low-pass filter (LPF)-only-DWT technique is employed in which energy contents of different frequency bands are obtained by subtracting quasi-averaged, consecutive LPF outputs. Training phase is used to identify the range of critical DWT coefficients that are in turn used to set patient-specific system level parameters for minimizing power consumption. The proposed optimizations allow the design to work at significantly lower power in the normal operation mode. The system has been tested on neural data obtained from kainate-treated rats. The design was implemented in TSMC-65nm technology and consumes less than 550-nW power at 250-mV supply.

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Cited By

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Gao XYang YZhang FZhou FZhu JSun JXu KChen Y(2022)A Feature Extraction Method for Seizure Detection Based on Multi-Site Synchronous Changes and Edge Detection AlgorithmBrain Sciences10.3390/brainsci1301005213:1(52)Online publication date: 27-Dec-2022
https://doi.org/10.3390/brainsci13010052
Mittal RPrince A(2022)Catalogic Systematic Literature Review of Hardware-Accelerated Neurodiagnostic SystemsBiomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders10.1007/978-3-030-97845-7_10(187-232)Online publication date: 18-Jun-2022
https://doi.org/10.1007/978-3-030-97845-7_10

Index Terms

Low-Power Architecture for Epileptic Seizure Detection Based on Reduced Complexity DWT
1. Hardware
  1. Emerging technologies
  2. Very large scale integration design

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Low-power DWT-based quasi-averaging algorithm and architecture for epileptic seizure detection
ISLPED '10: Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and design

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EEG-based epileptic seizure detection using deep learning techniques: A survey
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The detection of epileptic seizures by classifying electroencephalography (EEG) signals into ictal and interictal classes is a demanding challenge, because it identifies the seizure and seizure-free states of an epileptic patient. In previous ...

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Published In

cover image ACM Journal on Emerging Technologies in Computing Systems

ACM Journal on Emerging Technologies in Computing Systems Volume 8, Issue 2

Special Issue on Implantable Electronics

June 2012

94 pages

ISSN:1550-4832

EISSN:1550-4840

DOI:10.1145/2180878

Issue’s Table of Contents

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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Association for Computing Machinery

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 01 June 2012

Accepted: 01 September 2011

Revised: 01 August 2011

Received: 01 April 2011

Published in JETC Volume 8, Issue 2

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Gao XYang YZhang FZhou FZhu JSun JXu KChen Y(2022)A Feature Extraction Method for Seizure Detection Based on Multi-Site Synchronous Changes and Edge Detection AlgorithmBrain Sciences10.3390/brainsci1301005213:1(52)Online publication date: 27-Dec-2022
https://doi.org/10.3390/brainsci13010052
Mittal RPrince A(2022)Catalogic Systematic Literature Review of Hardware-Accelerated Neurodiagnostic SystemsBiomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders10.1007/978-3-030-97845-7_10(187-232)Online publication date: 18-Jun-2022
https://doi.org/10.1007/978-3-030-97845-7_10

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Low-power DWT-based quasi-averaging algorithm and architecture for epileptic seizure detection

EEG-based epileptic seizure detection using deep learning techniques: A survey

Machine learning-based EEG signals classification model for epileptic seizure detection

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