|
|
|
|
| LEADER |
01524 am a22002173u 4500 |
| 001 |
61654 |
| 042 |
|
|
|a dc
|
| 100 |
1 |
0 |
|a Shoeb, Ali H.
|e author
|
| 100 |
1 |
0 |
|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
|e contributor
|
| 100 |
1 |
0 |
|a Shoeb, Ali H.
|e contributor
|
| 100 |
1 |
0 |
|a Shoeb, Ali H.
|e contributor
|
| 700 |
1 |
0 |
|a Carlson, Dave
|e author
|
| 700 |
1 |
0 |
|a Panken, Eric
|e author
|
| 700 |
1 |
0 |
|a Denison, Timothy
|e author
|
| 245 |
0 |
0 |
|a A micropower support vector machine based seizure detection architecture for embedded medical devices
|
| 260 |
|
|
|b Institute of Electrical and Electronics Engineers,
|c 2011-03-10T20:57:40Z.
|
| 856 |
|
|
|z Get fulltext
|u http://hdl.handle.net/1721.1/61654
|
| 520 |
|
|
|a 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.
|
| 546 |
|
|
|a en_US
|
| 655 |
7 |
|
|a Article
|
| 773 |
|
|
|t 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. EMBC 2009
|
