Scale invariance properties of intracerebral EEG improve seizure prediction in mesial temporal lobe epilepsy.

Daniele Marinazzo,Jean Marc Lina,Jean Gotman,Kais Gadhoumi

doi:10.1371/journal.pone.0121182

Abstract

Although treatment for epilepsy is available and effective for nearly 70 percent of patients, many remain in need of new therapeutic approaches. Predicting the impending seizures in these patients could significantly enhance their quality of life if the prediction performance is clinically practical. In this study, we investigate the improvement of the performance of a seizure prediction algorithm in 17 patients with mesial temporal lobe epilepsy by means of a novel measure. Scale-free dynamics of the intracerebral EEG are quantified through robust estimates of the scaling exponents—the first cumulants—derived from a wavelet leader and bootstrap based multifractal analysis. The cumulants are investigated for the discriminability between preictal and interictal epochs. The performance of our recently published patient-specific seizure prediction algorithm is then out-of-sample tested on long-lasting data using combinations of cumulants and state similarity measures previously introduced. By using the first cumulant in combination with state similarity measures, up to 13 of 17 patients had seizures predicted above chance with clinically practical levels of sensitivity (80.5%) and specificity (25.1% of total time under warning) for prediction horizons above 25 min. These results indicate that the scale-free dynamics of the preictal state are different from those of the interictal state. Quantifiers of these dynamics may carry a predictive power that can be used to improve seizure prediction performance.

Highlights

The existence of a preictal state distinguishable from an interictal state of the epileptic brain is supported by growing evidence of electrophysiological changes preceding the ictal phase [1,2,3,4,5,6]
We examine whether combining measures of two a priori unrelated properties of the EEG, the so-called thermodynamic and scale invariance properties, would result in an improvement of the prediction performance
The current study shows that measures derived from intracerebral EEG (iEEG) scaling analysis used together with iEEG state similarity measures can be useful in the prediction of mesial temporal lobe seizures with practical performance levels

Summary

Introduction

The existence of a preictal state distinguishable from an interictal state of the epileptic brain is supported by growing evidence of electrophysiological changes preceding the ictal phase [1,2,3,4,5,6]. Transitions from interictal to ictal state are likely to be governed by various mechanisms [7,8,9] with different electrophysiological manifestations. Revealing changes in dynamical properties of the electroencephalogram (EEG) related to state transition is probably a multifaceted problem and solving it requires a combination of tools each adapted to reveal changes in a distinct aspect of signal properties. Preictal changes are probably better detected using a PLOS ONE | DOI:10.1371/journal.pone.0121182. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Methods

Results

Discussion

Conclusion