Detection of epileptiform spikes in the EEG using a patient-independent neural network

Genetic programming of conventional features to detect seizure precursors

“…Despite the large number of attempts over decades to accurately detect precursors, previous schemes to detect precursors have exhibited inconsistent performance (i.e., 74-94% sensitivity, 84-90% specificity, 54-88% selectivity) across small databases of testing records (Clavango 2000, Hassanpour 2003, Hassanpour 2004, Ossadtchi 2002, Pon 2002, Adjouadi, 2004, Jones, 1996, Ko, 1998, Tarassenko, 1998, Van Hoey, 1998, Van Hese, 2003, Wilson, 1991, Zhang, 1999 without reporting level of confidence in those results. For instance, initial work in precursor-detection was focused on detecting spikes and sharp-wave transients within the EEG, yet the various approaches for detection and features for extraction have not cultivated connections between the precursors and localized epileptogenic zones (Clavango 2000, Hassanpour 2003, Hassanpour 2004, Ossadtchi 2002, Pon 2002, Adjouadi, 2004, Jones, 1996, Ko, 1998, Tarassenko, 1998, Van Hoey, 1998, Van Hese, 2003, Wilson, 1991, Zhang, 1999. On the other hand, the recent efforts of a few groups have reported more promising findings concerning precursors other than spikes and sharp-waves.…”

Intelligent medical systems and the interface with statistics

“…The evaluation of such systems was quantified using expert comparisons and statistical methods from epidemiology such as sensitivity and specificity or ROC curves (14,15). Consideration of the multiple comparisons involved in the evaluations was not contemplated.…”