Seizure Prediction in Epilepsy

Epilepsy is the second most common serious neurological disease after stroke. This disease affects approximately 50 million people worldwide and 50-70 cases per 100,000 in the developed countries. In approximately 40% of patients with so-called partial seizures, current medications are unable to control their symptoms. One of the most devastating aspects of epilepsy is the anxiety and apprehension associated with the inability to predict when a seizure will occur. The inability to predict the time of seizure onset also implies the need for continuous medication therapy with the associated continuous side effects. For a number of years investigators and commercial-interest groups have sought methods for the early detection and anticipation of seizures so that “discontinuous” therapies could be introduced (e.g., Milton and Jung, 2003). At the heart of most predictive efforts is the description and analysis of the cerebral electrical activity reflected in the electroencephalogram (EEG). The brain electrical activity of a patient with epilepsy shows abnormal and often rhythmic discharges during the seizure. This activity pattern is called an electrographic seizure. Between such electrographic seizures, short discharges (spikes) are also frequently observed in the EEG of these patients. Identification of these activity patterns in clinical practice has typically been a subjective process. The introduction of computer-based instrumentation and analysis to the field of electroencephalography made evaluation of automated spike and seizure detection techniques possible (e.g., Gotman and Gloor, 1976; Gotman et ai, 1979; Gotman, 1982). During the 1980s, the EEG during seizure activity was characterized using more complex measures such as those derived from chaos theory (e.g., Babloyantz and Destexhe, 1986; van Erp, 1988). There were a number of “early” reports of the successful application of frequency-domain template analyses and auto-regressive models to the problem of seizure prediction (e.g., Viglione and Walsh, 1975; Rogowski et al, 1981).