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

H. Markandeya; G. Karakonstantis; S. Raghunathan; P. Irazoqui; K. Roy

doi:10.1145/1840845.1840907

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

H. Markandeya, G. Karakonstantis, S. Raghunathan, P. Irazoqui, K. Roy

Research output: Chapter in Book/Report/Conference proceeding › Chapter

14 Citations (Scopus)

Abstract

In this paper, we have developed a low-complexity algorithm for epileptic seizure detection with a high degree of accuracy. The algorithm has been designed to be feasibly implementable as battery-powered low-power implantable epileptic seizure detection system or epilepsy prosthesis. This is achieved by utilizing design optimization techniques at different levels of abstraction. Particularly, user-specific critical parameters are identified at the algorithmic level and are explicitly used along with multiplier-less implementations at the architecture level. The system has been tested on neural data obtained from in-vivo animal recordings and has been implemented in 90nm bulk-Si technology. The results show up to 90 % savings in power as compared to prevalent wavelet based seizure detection technique while achieving 97% average detection rate.

Original language	English
Title of host publication	Proceedings of the International Symposium on Low Power Electronics and Design
Pages	301-306
Number of pages	6
DOIs	https://doi.org/10.1145/1840845.1840907
Publication status	Published - 01 Jan 2010

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abstract = "In this paper, we have developed a low-complexity algorithm for epileptic seizure detection with a high degree of accuracy. The algorithm has been designed to be feasibly implementable as battery-powered low-power implantable epileptic seizure detection system or epilepsy prosthesis. This is achieved by utilizing design optimization techniques at different levels of abstraction. Particularly, user-specific critical parameters are identified at the algorithmic level and are explicitly used along with multiplier-less implementations at the architecture level. The system has been tested on neural data obtained from in-vivo animal recordings and has been implemented in 90nm bulk-Si technology. The results show up to 90 % savings in power as compared to prevalent wavelet based seizure detection technique while achieving 97% average detection rate.",

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AU - Roy, K.

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

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