Abstract
| 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 | |
| Publication status | Published - 01 Jan 2010 |
Fingerprint
Cite this
}
Low-power DWT-based quasi-averaging algorithm and architecture for epileptic seizure detection. / Markandeya, H.; Karakonstantis, G.; Raghunathan, S.; Irazoqui, P.; Roy, K.
Proceedings of the International Symposium on Low Power Electronics and Design. 2010. p. 301-306.Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - Low-power DWT-based quasi-averaging algorithm and architecture for epileptic seizure detection
AU - Markandeya, H.
AU - Karakonstantis, G.
AU - Raghunathan, S.
AU - Irazoqui, P.
AU - Roy, K.
PY - 2010/1/1
Y1 - 2010/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-77957937484&partnerID=8YFLogxK
U2 - 10.1145/1840845.1840907
DO - 10.1145/1840845.1840907
M3 - Chapter
AN - SCOPUS:77957937484
SN - 9781450301466
SP - 301
EP - 306
BT - Proceedings of the International Symposium on Low Power Electronics and Design
ER -