Robust and low complexity algorithms for seizure detection

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...

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Vydané v:2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Ročník 2014; s. 4447 - 4450
Hlavní autori: Bandarabadi, Mojtaba, Teixeira, Cesar A., Netoff, Theoden I., Parhi, Keshab K., Dourado, Antonio
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.01.2014
Predmet:
Adolescent
Adult
Algorithms
bipolar analysis
Brain - physiopathology
Child
Databases, Factual
Electrodes
Electroencephalography
Epilepsy
Epilepsy - diagnosis
Feature extraction
Female
Humans
Neurophysiology
power spectral density
Seizure detection
Sensitivity
Sensitivity and Specificity
ISSN:1094-687X, 1557-170X, 2694-0604, 2694-0604
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1094-687X
1557-170X
2694-0604
2694-0604
DOI:10.1109/EMBC.2014.6944611