Automatic Identification of Spike-Wave Events and Non-Convulsive Seizures with a Reduced Set of Electrodes

Epileptiform activity in the brain, whether localized or generalized, constitutes an important category of abnormal electroencephalogram (EEG). Seizures are episodes of relatively brief disturbances of mental, motor or sensory activity caused by paroxysmal cerebral activity. They are not always accompanied by the characteristic convulsions that we commonly associate with the word epilepsy. In this case, they may be referred to as non-convulsive status epilepticus (NCSE) [1] or as absence seizures (formerly called "petit mal" seizures). They often manifest themselves in scalp-recorded EEG as large-amplitude spike-wave "patterns" (or "events"), usually occurring in bursts. If left undetected and untreated, they can potentially cause significant brain and behavioral dysfunctions, interfere with information processing, or otherwise contribute to altered mental status. In this paper, we describe an algorithm to be implemented in a prototype BrainScope_ED instrument meant to alert to a detected seizure in an emergency department (ED) or other clinical setting. BrainScope_ED uses a reduced electrode set (8 instead of 19). The proposed signal processing algorithm is based on the detection of spike-wave events obtained from a wavelet analysis of the EEG signal, combined with an analysis of the complexity of the EEG using fractal dimension estimates. We show that this algorithm has excellent sensitivity and specificity. In particular, the fractal analysis is a key factor in the removal of falsely detected spike-wave events (false positives) that can be caused by voluntary or involuntary artifacts such as fast eyelid flutter.