Automated rapid seizure detection in the human ECoG

Abstract

Summary form only given. Automated seizure detection with high specificity and sensitivity is a highly desirable but elusive goal. The failure to develop a reliable system despite decades of effort is due in part to the non-stationary and noise in the EEG/ECoG signals, as well as to the rudimentary mathematical treatment it has received. We have developed a method of automated seizure detection based on a combination of linear and nonlinear filtering techniques, including the discrete wavelet transform. To minimize noise, this method was first developed for intracranial signals, then later adapted to scalp recordings. Preliminary results indicate that this new method may be the fastest and most reliable to date. The generic algorithm has been tested on 5 patients and a total of 20 seizure segments and 7 interictal segments recorded form intracranial electrodes. We have also compared the method to expert visual analysis, performed through a review of polygraph tracings of the signals, and have found our method to be both fast and highly accurate. We are generally able to detect the electrographic seizure within a second of the time marked by the EEGer. The method is also highly adaptable-it automatically accounts for signal changes over time, and has a number of parameters which may be tuned to further improve accuracy for a given individual patient or for a particular signal or group of signals being monitored. The algorithm has been implemented and now allows real-time monitoring and detection on a 486/DX 33 MHz PC.<>