A micropower support vector machine based seizure detection architecture embedded medical devices
Name
Shoeb-2009-A micropower support.pdf
Size
800.12 KB
Format
Adobe PDF
Checksum (MD5)
6f14f6d4f456b80deffd5cc8f016b8f5
Author(s)
Denison, Timothy
Panken, Eric
Carlson, Dave
Shoeb, Ali H.
Date Issued
November 2009
Journal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. EMBC 2009.
Publisher
Institute of Electrical and Electronics Engineers
Citation
Shoeb, A. et al. “A micropower support vector machine based seizure detection architecture for embedded medical devices.” Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE. 2009. 4202-4205. © Institute of Electrical and Electronics Engineers.
Version
Final published version
Abstract
Implantable neurostimulators for the treatment of epilepsy that are capable of sensing seizures can enable novel therapeutic applications. However, detecting seizures is challenging due to significant intracranial EEG signal variability across patients. In this paper, we illustrate how a machine-learning based, patient-specific seizure detector provides better performance and lower power consumption than a patient non-specific detector using the same seizure library. The machine-learning based architecture was fully implemented in the micropower domain, demonstrating feasibility for an embedded detector in implantable systems.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1109/IEMBS.2009.5333790
