Epilepsy Detection and Monitoring

Epilepsy is one of the world’s most common neurological diseases, affecting more than 40 million people worldwide. Epilepsy’s hallmark symptom, seizures, can have a broad spectrum of debilitating medical and social consequences. Although antiepileptic drugs have helped treat millions of patients, roughly a third of all patients are unresponsive to pharmacological intervention. As our understanding of this dynamic disease evolves, new possibilities for treatment are emerging. An area of great interest is the development of devices that incorporate algorithms capable of detecting early onset of seizures or even predicting them hours before they occur. This lead time will allow for new types of interventional treatment. In the near future a patient’s seizure may be detected and aborted before physical manifestations begin. In this chapter we discuss the algorithms that will make these devices possible and how they have been implemented to date. We investigate how wavelets, synchronization, Lyapunov exponents, principal component analysis, and other techniques can help investigators extract information about impending seizures. We also compare and contrast these measures, and discuss their individual strengths and weaknesses. Finally, we illustrate how these techniques can be brought together in a closed-loop seizure prevention system.