Seizure prediction: methods

Abstract

Epilepsy, one of the most common neurological diseases, affects over 50 million people worldwide. Epilepsy 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 have seizures that are refractory to pharmacological intervention. The evolution of our understanding of this dynamic disease leads to new treatment possibilities. There is great interest in the development of devices that incorporate algorithms capable of detecting early onset of seizures or even predicting them hours before they occur. The lead time provided by these new technologies will allow for new types of interventional treatment. In the near future, seizures may be detected and aborted before physical manifestations begin. In this chapter we discuss the algorithms that make these devices possible and how they have been implemented to date. We also compare and contrast these measures, and review their individual strengths and weaknesses. Finally, we illustrate how these techniques can be combined in a closed-loop seizure prevention system. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Figure 1

Ten second EEG trace showing the critical time window for EEG based seizure prediction. A is the time of earliest EEG change associated with the seizure (green bar); B is the unequivocal EEG seizure onset (blue bar); C is the time of earliest clinical change (red bar). The prediction horizon (black box) is the time from the processing window to the unequivocal EEG onset of the seizure.

Figure 2

Space state embedding. An example of a state space computed from a single channel of EEG showing changes in the state space during a seizure. A is the interictal space state. B is the 10-second preseizure space state. C is the seizure space state. p is the embedding dimension of the state space; t is the time delay for the construction of the state space [(x,y,x,ẋẍ)].

Figure 3

Display of entrainment of two sites prior to a left onset temporal lobe seizure with the left temporal depth electrode (LTD3, black trace) within the seizure focus and the left scalp mandibular surface electrode (MN1, red trace) nearby. The short-term-maximum Lyapunov Exponent (STLmax) was calculated in overlapping 10.24 minutes windows. The convergence between the sites occurs approximately 25 minutes before the onset of the EEG seizure (B).