Casson, AJ

Logesparan, L

Rodriguez-Villegas, E

Medical & Biological Engineering & Computing

English

This study identifies characteristic features in scalp EEG that simultaneously give the best discrimination between epileptic seizures and background EEG in minimally pre-processed scalp data; and have minimal computational complexity to be suitable for online, real-time analysis. The discriminative performance of 65 previously reported features has been evaluated in terms of sensitivity, specificity, area under the sensitivity-specificity curve (AUC), and relative computational complexity, on 47 seizures (split in 2,698 2 s sections) in over 172 h of scalp EEG from 24 adults. The best performing features are line length and relative power in the 12.5-25 Hz band. Relative power has a better seizure detection performance (AUC = 0.83; line length AUC = 0.77), but is calculated after the discrete wavelet transform and is thus more computationally complex. Hence, relative power achieves the best performance for offline detection, whilst line length would be preferable for online low complexity detection. These results, from the largest systematic study of seizure detection features, aid future researchers in selecting an optimal set of features when designing algorithms for both standard offline detection and new online low computational complexity detectors. © International Federation for Medical and Biological Engineering 2012

Logesparan, Lojini

Casson, Alexander J.

Rodriguez-Villegas, Esther

The University of Manchester - Institutional Repository

Optimal features for online seizure detection

Spiral - Imperial College Digital Repository

A new muscle artifact removal technique to improve the interpretation of the ictal scalp electroencephalogram. In:

Epileptic Seizure Detection in EEG Recordings using Phase Congruency. In:

Seizure Detection in Temporal Lobe Epileptic EEGs Using the Best Basis Wavelet Functions.

Optimal features for online seizure detection

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The University of Manchester - Institutional Repository

Spiral - Imperial College Digital Repository