@inproceedings{18367223a1c64505a679290f79ae387f,
title = "An energy efficient epileptic seizure detector",
abstract = "Epilepsy is one of the most common neurological disorders affecting up to 1% of the world's population and approximately 2.5 million people in the United States. Seizures in more than 30% of epilepsy patients are resistant to anti-epileptic drugs. A significant biomedical research is focused on the development of an energy efficient implantable integrated circuit for real-time detection of seizures. In this paper we propose an architecture for an implantable seizure detector using a hyper-synchronous signal detection circuit and signal rejection algorithm (SRA). The proposed seizure detector (SD) continuously monitors neural signals for hyper-synchronous pulses and extracts the seizure onset signal. If the pulses in an epoch exceed a threshold value, a seizure is declared. The design was validated using Simulink{\textregistered}. The signal rejection algorithm (SRA) reduces false detection and minimal circuitry leads to a 12% reduction of power consumption.",
keywords = "Energy Efficient Design, Epilepsy, Hypersynchronous, Seizure",
author = "Sayeed, {Md Abu} and Mohanty, {Saraju P.} and Elias Kougianos and Hitten Zaveri",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Consumer Electronics, ICCE 2018 ; Conference date: 12-01-2018 Through 14-01-2018",
year = "2018",
month = mar,
day = "26",
doi = "10.1109/ICCE.2018.8326063",
language = "English",
series = "2018 IEEE International Conference on Consumer Electronics, ICCE 2018",
pages = "1--4",
editor = "Mohanty, {Saraju P.} and Peter Corcoran and Hai Li and Anirban Sengupta and Jong-Hyouk Lee",
booktitle = "2018 IEEE International Conference on Consumer Electronics, ICCE 2018",
}