Name
Abstract | ||
|---|---|---|
This paper presents two low complexity and yet robust methods for automated seizure detection using a set of 2 intracranial Electroencephalogram (iEEG) recordings. Most current seizure detection methods suffer from high number of false alarms, even when designed to be subject-specific. In this study, the ratios of power between pairs of frequency bands are used as features to detect epileptic seizures. For comparison, these features are calculated from monopolar and bipolar iEEG recordings. Optimal thresholds are individually determined and used for each feature. Alarms are generated when the measure passes the threshold. The detector was applied to long-term continuous invasive recordings from 5 patients with refractory partial epilepsy, containing 54 seizures in 780 hours. On average, the results revealed 88.9% sensitivity, a very low false detection rate of 0.041 per hour (h(-1)) and detection latency of 9.4 seconds. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6944611 | EMBC |
Keywords | Field | DocType |
robust complexity algorithms,medical disorders,bioelectric potentials,medical signal detection,bipolar ieeg recordings,time 780 hour,intracranial electroencephalogram recordings,electroencephalography,low complexity algorithms,bipolar analysis,medical signal processing,feature extraction,automated epileptic seizure detection,power spectral density,seizure detection,monopolar ieeg recordings | Seizure detection,Computer science,Speech recognition,Electronic engineering,Spectral density | Conference |
Volume | ISSN | Citations |
2014 | 1557-170X | 2 |
PageRank | References | Authors |
0.45 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mojtaba Bandarabadi | 1 | 15 | 2.06 |
| César A. Teixeira | 2 | 71 | 10.31 |
| Theoden I Netoff | 3 | 126 | 15.30 |
| keshab k parhi | 4 | 3235 | 369.07 |
| António Dourado | 5 | 43 | 9.12 |