Epileptic seizure prediction and control

By L. Iasemidis. IEEE Transactions on Biomedical Engineering, 2003

392 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. (2005, background): “In the last few years many reviews of nonlinear seizure anticipation have appeared (Iasemidis, 2003; Lehnertz et al., 2001, 2003; Le van Quyen, 2005; Le van Quyen et al., 2001a; Litt and Echauz, 2002; Litt and Lehnertz, 2002).”
Novel Optimization Models for Abnormal Brain Activity Classification (2008, background): “An emerging view in recent epilepsy research suggests that four stages evolve in the seizure process: normal, preseizure, seizure onset, and postseizure (Iasemidis 2003).”
Mixed-Band Wavelet-Chaos-Neural Network Methodology for Epilepsy and Epileptic Seizure Detection (2007, background): “Primarily due to a relatively low understanding of the mechanisms underlying the problem, most existing methods suffer from the drawback of low accuracy which leads to higher false alarms and missed detections [11].”
A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy (2007, background): “A major shortcoming of existing seizure detection algorithms is their low accuracy resulting in high missed detection and false alarm rates [15].”