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 |
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Information Systems
- Control and Systems Engineering
- Electrical and Electronic Engineering
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Cite this
Mansouri, A., Singh, S., & Sayood, K. (2017). Hierarchal online temporal and spatial EEG seizure detection. In 2017 IEEE International Conference on Electro Information Technology, EIT 2017 (pp. 416-421). [8053397] IEEE Computer Society. https://doi.org/10.1109/EIT.2017.8053397