Abstract
Seizures affect a significant portion of the world's population and while a small proportion of the seizures are easy to detect, the vast majority are subtle enough to require the expertise of a neurologist. In this paper, we present a multifaceted computational approach to detecting the presence and locality of a seizure using Electroencephalography (EEG) signals. We test the proposed approach using a variety of signals and demonstrate the efficacy of the approach.
| Original language | English (US) |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Electro Information Technology, EIT 2017 |
| Publisher | IEEE Computer Society |
| Pages | 416-421 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509047673 |
| DOIs | |
| State | Published - Sep 27 2017 |
| Event | 2017 IEEE International Conference on Electro Information Technology, EIT 2017 - Lincoln, United States Duration: May 14 2017 → May 17 2017 |
Other
| Other | 2017 IEEE International Conference on Electro Information Technology, EIT 2017 |
|---|---|
| Country | United States |
| City | Lincoln |
| Period | 5/14/17 → 5/17/17 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Information Systems
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
Hierarchal online temporal and spatial EEG seizure detection. / Mansouri, Amirsalar; Singh, Sanjay; Sayood, Khalid.
2017 IEEE International Conference on Electro Information Technology, EIT 2017. IEEE Computer Society, 2017. p. 416-421 8053397.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Hierarchal online temporal and spatial EEG seizure detection
AU - Mansouri, Amirsalar
AU - Singh, Sanjay
AU - Sayood, Khalid
PY - 2017/9/27
Y1 - 2017/9/27
N2 - Seizures affect a significant portion of the world's population and while a small proportion of the seizures are easy to detect, the vast majority are subtle enough to require the expertise of a neurologist. In this paper, we present a multifaceted computational approach to detecting the presence and locality of a seizure using Electroencephalography (EEG) signals. We test the proposed approach using a variety of signals and demonstrate the efficacy of the approach.
AB - Seizures affect a significant portion of the world's population and while a small proportion of the seizures are easy to detect, the vast majority are subtle enough to require the expertise of a neurologist. In this paper, we present a multifaceted computational approach to detecting the presence and locality of a seizure using Electroencephalography (EEG) signals. We test the proposed approach using a variety of signals and demonstrate the efficacy of the approach.
UR - http://www.scopus.com/inward/record.url?scp=85033732389&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85033732389&partnerID=8YFLogxK
U2 - 10.1109/EIT.2017.8053397
DO - 10.1109/EIT.2017.8053397
M3 - Conference contribution
AN - SCOPUS:85033732389
SP - 416
EP - 421
BT - 2017 IEEE International Conference on Electro Information Technology, EIT 2017
PB - IEEE Computer Society
ER -