Imperial College London
Alternate

ProfessorEstherRodriguez Villegas

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor in Low Power Electronics
 
 
 
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Contact

 

+44 (0)20 7594 6193e.rodriguez

 
 
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Location

 

914Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Imtiaz:2019:10.1109/ACCESS.2019.2920006,
author = {Imtiaz, S and Iranmanesh, S and Rodriguez, Villegas E},
doi = {10.1109/ACCESS.2019.2920006},
journal = {IEEE Access},
pages = {71195--71208},
title = {A low power system with EEG data reduction for long-term epileptic seizures monitoring},
url = {http://dx.doi.org/10.1109/ACCESS.2019.2920006},
volume = {7},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Long-term monitoring of epilepsy patients requires low-power systems that can record and transmit electroencephalogram data over extended periods of time. Since seizure events are rare, long-term monitoring inherently results in large amounts of data that are recorded and hence need to be reduced. This paper presents an ultra-low power integrated circuit implementation of a data reduction algorithm for epilepsy monitoring, specific to seizure events. The algorithm uses line length of the electroencephalogram signals as the key discriminating feature to classify epochs of data as seizure or non-seizure events. It is implemented in AMS 0.18- $\mu \text{m}$ CMOS technology and its output is connected to a Bluetooth low energy transceiver to wirelessly transmit potential seizure events. All the modules of the algorithm have been implemented on chip to use a small number of clock cycles and remain mostly in an idle mode. The algorithm, on the chip, achieves 50% of data reduction with a sensitivity of 80% for capturing seizure events. The overall power consumption of the chip is measured to be 23 $\mu \text{W}$ , while the full system with wireless transmission consumes 743 $\mu \text{W}$ . The results in this paper demonstrate the feasibility of a long-term seizure monitoring system capable of running autonomously for over two weeks.
AU  - Imtiaz,S
AU  - Iranmanesh,S
AU  - Rodriguez,Villegas E
DO  - 10.1109/ACCESS.2019.2920006
EP  - 71208
PY  - 2019///
SN  - 2169-3536
SP  - 71195
TI  - A low power system with EEG data reduction for long-term epileptic seizures monitoring
T2  - IEEE Access
UR  - http://dx.doi.org/10.1109/ACCESS.2019.2920006
UR  - http://hdl.handle.net/10044/1/70722
VL  - 7
ER  -
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