Joining the benefits: Combining epileptic seizure prediction methods

Abstract

Summary Purpose: In recent years, a variety of methods developed in the field of linear and nonlinear time series analysis have been used to obtain reliable predictions of epileptic seizures. Because individual methods for seizure prediction so far have shown statistical significance but insufficient performance for clinical applications, we investigated possible improvements by combining algorithms capturing different aspects of electroencephalogram (EEG) dynamics. Methods: We applied the mean phase coherence and the dynamic similarity index to long‐term continuous intracranial EEG data. The predictive performance of both methods was assessed and statistically evaluated separately, as well as by using logical “AND” and “OR” combinations. Results: Used independently, either method resulted in a statistically significant prediction performance in only a few patients. Particularly the “AND” combination led to improved prediction performances, leading to an increase in sensitivity and/or specificity. For a maximum false prediction rate of 0.15/h, the mean sensitivity improved from about 25% for the individual methods to 43.2% for the “AND” and to 35.2% for the “OR” combination. Discussion: This study shows that combinations of prediction methods are promising new approaches to enhance seizure prediction performance considerably. It allows merging the individual benefits of prediction methods in a complementary manner. Because either sensitivity or specificity of seizure prediction methods can be improved depending on the needs of the desired clinical application, the combination opens a new window for future use in a clinical setting.

On the proper selection of preictal period for seizure prediction

“…Preictal period (min) 20 within the global state of the brain. In contrast, invasive EEG is strongly localized on a limited region of the brain, probably providing higher C ADHs for the seizures developed far from the implanted region.…”

“…Therefore, we investigated high-quality iEEG and sEEG recordings (with the sampling rates of 1024 Hz and 2500 Hz) and divided the gamma band into several narrower subbands to study the spectral behavior more precisely. Instead of using the well-known frequency bands, the subbands were selected as (0.5-4], (4)(5)(6)(7)(8), (8)(9)(10)(11)(12)(13)(14)(15), (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48) The spectral powers were obtained using power spectral density (PSD) estimated through Welch's method [47]. The PSD ...…”

Application of a dissimilarity index of EEG and its sub-bands on prediction of induced epileptic seizures from rat's EEG signals

“…In the second level of decomposition, the a1 component was further decomposed into higher resolution components, d2 (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and lower resolution components, a2 (0-15 Hz). Following this process, after four levels of decomposition, the components retained were a4 (0-4 Hz), d4 (4-8 Hz), d3 (8)(9)(10)(11)(12)(13)(14)(15), d2 (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and d1 (30-60 Hz). Reconstructions of these five components using the inverse wavelet transform approximately correspond to the five physiological EEG subbands delta, theta, alpha, beta, and gamma [8].…”

“…In a recent study [17] dynamical similarity index was also combined with mean phase coherence to improve the results of seizure prediction. However, both dynamical and fuzzy similarity indices suffer intrinsic problems.…”

A Novel Dynamic Update Framework for Epileptic Seizure Prediction

“…Some research used nonlinear measures including the dynamic similarity index with MPC [44, 45], the wavelet-based nonlinear similarity index [46], and the lag synchronization index with MPC [47]. By using single bivariate feature of [45], the average seizure prediction sensitivity achieved 35.2% and 43.2% with “OR” and “AND” combination system, respectively, when SOP (seizure occurrence period) is 30 min under a maximum false prediction rate of 0.15 h −1 . Averaged sensitivity values of 60% were obtained for fpr of 0.15 h −1 by replacing dynamic similarity index with lag synchronization index in [47].…”