Flow chart of the seizure prediction method.

<p>Steps 1 to 3 are training procedures and 4 to 6 are testing procedures. Boxes in orange show procedures used instead when combination of features are used.</p

Scale Invariance Properties of Intracerebral EEG Improve Seizure Prediction in Mesial Temporal Lobe Epilepsy

<div><p>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.</p></div

Performance of the seizure prediction method using cumulants and their combination.

<p>a-c top: Performance values for the range of persistence-<i>τ</i> values analyzed. Orange circles indicate interpolated values of sensitivity, proportion of time under warning and warning rate corresponding to the critical false prediction rate of 0.15/h. a-c bottom: Number of patients in whom seizures are predicted above chance as a function of the persistence-<i>τ</i> parameter. d. Average prediction time per patient as a function of the persistence-<i>τ</i> parameter.</p

Cumulant vs. spectral power.

<p>Box-and-whisker plot (minimum-maximum range) indicating differences in three patients (P2, P10 and P17) between preictal and interictal average 5-min observations of cumulant <i>c</i>₁, cumulant <i>c</i>₂ and the spectral power in the conventional EEG bands. Boxes in orange represent observations from the most discriminating channel in cumulant <i>c</i>₁. Boxes in blue represent observations from the most discriminating channel in cumulant <i>c</i>₂. Significant differences between preictal and interictal observations are denoted by asterisks (* <i>p</i>-value < 0.01, ** <i>p</i>-value < 0.001).</p

Widespread EEG Changes Precede Focal Seizures

<div><p>The process by which the brain transitions into an epileptic seizure is unknown. In this study, we investigated whether the transition to seizure is associated with changes in brain dynamics detectable in the wideband EEG, and whether differences exist across underlying pathologies. Depth electrode ictal EEG recordings from 40 consecutive patients with pharmacoresistant lesional focal epilepsy were low-pass filtered at 500 Hz and sampled at 2,000 Hz. Predefined EEG sections were selected immediately before (immediate preictal), and 30 seconds before the earliest EEG sign suggestive of seizure activity (baseline). Spectral analysis, visual inspection and discrete wavelet transform were used to detect standard (delta, theta, alpha, beta and gamma) and high-frequency bands (ripples and fast ripples). At the group level, each EEG frequency band activity increased significantly from baseline to the immediate preictal section, mostly in a progressive manner and independently of any modification in the state of vigilance. Preictal increases in each frequency band activity were widespread, being observed in the seizure-onset zone and lesional tissue, as well as in remote regions. These changes occurred in all the investigated pathologies (mesial temporal atrophy/sclerosis, local/regional cortical atrophy, and malformations of cortical development), but were more pronounced in mesial temporal atrophy/sclerosis. Our findings indicate that a brain state change with distinctive features, in the form of unidirectional changes across the entire EEG bandwidth, occurs immediately prior to seizure onset. We postulate that these changes might reflect a facilitating state of the brain which enables a susceptible region to generate seizures.</p></div

Significance of the difference in the average cumulant and the spectral power observations between preictal and interictal epochs (5-min length) using the most discriminating channel in <i>c</i>₁ and the most discriminating channel in <i>c</i>₂.

<p>++ (corrected <i>p</i>-value < 0.001), + (corrected <i>p</i>-value < 0.01), —(no significant difference)</p><p>↑ (mean preictal cumulant > mean interictal cumulant)</p><p>↓ (mean preictal cumulant < mean interictal cumulant)</p><p>* Total shown is the number of patients in whom a significant difference is observed (corrected <i>p</i>-value < 0.01) in the spectral band power. None of the spectral bands showed a statistically significant difference in the spectral power for all patients (i.e. no single spectral band showed a correlation between the difference in cumulants and the difference in spectral power in all patients), suggesting that the observed difference in cumulants is likely not the result of a difference in spectral power.</p><p>Significance of the difference in the average cumulant and the spectral power observations between preictal and interictal epochs (5-min length) using the most discriminating channel in <i>c</i>₁ and the most discriminating channel in <i>c</i>₂.</p

Summary of iEEG dataset and seizure onset.

<p>* Based on the International League Against Epilepsy post-surgical outcome classification.</p><p>R: Right. L: Left, A: Amygdala, H: Hippocampus, P: Parahippocampus.</p><p>Bil.: Bilateral, >/< designate preponderance (based on 70% or more of number of seizures originating from one side).</p><p>y: year, m: month.</p><p>Summary of iEEG dataset and seizure onset.</p

Performance of the seizure prediction method using combination of cumulants and state similarity measures.

<p>a-c top: Performance metric values for the range of persistence-<i>τ</i> values analyzed when state similarity measures are combined with respectively cumulant <i>c</i>₁ (feature set <i>FS</i>4), cumulant <i>c</i>₂ (feature set <i>FS</i>5) and both cumulants (feature set <i>FS</i>6). Orange circles indicate interpolated values of sensitivity, proportion of time under warning and warning rate corresponding to the critical false prediction rate of 0.15/h. a-c bottom: Number of patients in whom seizures are predicted above chance level as function of the persistence-<i>τ</i> parameter, for each of the combination feature sets. d. Average prediction time per patient as a function of the persistence-<i>τ</i> parameter for each combination feature set.</p

Estimation of cumulants <i>c</i>₁ and <i>c</i>₂ using wavelet leader and bootstrap based scaling analysis in signals with different scale invariance properties.

<p>Each signal is 4096 samples. 100 bootstrap wavelet leader resamples were used in cumulant estimation. (a) Realization of a self-similar signal (fractional Brownian motion). ζ(<i>q</i>) is linear in <i>q</i>. <i>c</i>₁ ≠ 0 and <i>c</i>₂ ≈ 0. (b) Realization of a multifractal random walk. ζ(<i>q</i>) is nonlinear in <i>q</i>. <i>c</i>₁, <i>c</i>₂ ≠ 0. (c) EEG channel recording showing nonlinear relation of ζ(<i>q</i>) in <i>q</i>. <i>c</i>₁, <i>c</i>₂ ≠ 0. (Each column, top to bottom: Signal plot, regression plot of ζ(<i>q</i>) exponent estimates and boxplot of cumulant estimates).</p

Seizure prediction performance at the critical false prediction rate.

<p>¹<i>SS</i>: State similarity feature set. These results are reported from previous study.</p><p>Values are determined by linear interpolation.</p><p>Seizure prediction performance at the critical false prediction rate.</p