A novel spatiotemporal analysis of peri-ictal spiking to probe the relation of spikes and seizures in epilepsy

Abstract

The relation between epileptic spikes and seizures is an important but still unresolved question in epilepsy research. Preclinical and clinical studies have produced inconclusive results on the causality or even on the existence of such a relation. We set to investigate this relation taking in consideration seizure severity and spatial extent of spike rate. We developed a novel automated spike detection algorithm based on morphological filtering techniques and then tested the hypothesis that there is a pre-ictal increase and post-ictal decrease of the spatial extent of spike rate. Peri-ictal (around seizures) spikes were detected from intracranial EEG recordings in 5 patients with temporal lobe epilepsy. The 94 recorded seizures were classified into two classes, based on the percentage of brain sites having higher or lower rate of spikes in the pre-ictal compared to post-ictal periods, with a classification accuracy of 87.4%. This seizure classification showed that seizures with increased pre-ictal spike rate and spatial extent compared to the post-ictal period were mostly (83%) clinical seizures, whereas no such statistically significant (α = 0.05) increase was observed peri-ictally in 93% of sub-clinical seizures. These consistent across patients results show the existence of a causal relation between spikes and clinical seizures, and imply resetting of the preceding spiking process by clinical seizures.

FIGURE 1

Epileptic spike detection via DAMF. (a) EEG raw signal (x(t)) recorded from a single channel, intermediate (OC(t) and CO(t) signals) and output (x̂(t)) signal. (b) Data adaptive structure elements for DAMF from EEG data; EEG epochs in (a) and (c), and the derived structure elements from them in (b) and (d) respectively.

FIGURE 2

Performance over time of the epileptic spike detection by DAMF. Top panel: A 3 min segment from a 2 h interictal, single channel, EEG data x(t) from patient 1. Bottom panel: The output signal x̂(t) from DAMF. DAMF was applied to 10.24 s non-overlapping EEG epochs from x(t) for structure element selection and optimization and with a final threshold T_th = 2σ (dotted horizontal lines) for spike extraction from each epoch. Green dots (7 of them) are placed over detected spikes that also qualified as epileptic spikes by our expert physician (true positives). Black dots (2 of them) are placed where the algorithm failed to detect spikes that were however recognized by the physician. Red dots (1 of them) are placed where the algorithm detected spikes that were not recognized as spikes by the physician (false positives). The performance of DAMF in terms of sensitivity and specificity in this EEG segment was: sensitivity = 7/9 = 0.78 and specificity = 1 false positive every 3 min = 0.33 false positives per minute.

FIGURE 3

ROC curves for evaluation of the performance of DAMF and Persyst-based spike detection. The algorithms were run on a 2-h single channel EEG recorded interictally from patient 1. The sensitivity and specificity values for the DAMF ROC curve were estimated with threshold T_th values from 1.5σ to 4σ in steps of 0.001σ. The sensitivity and specificity values for the Persyst ROC curve were estimated with their perception threshold T_th values from 0.5 to 1 in steps of 0.01. It is observed that DAMF presents a superior performance over Persyst spike detection algorithm for any specified pair of sensitivity and specificity values. Based on these results, we decided to run DAMF for spike detection in the peri-ictal periods of all recorded seizures with T_th = 2σ, thus expecting a sensitivity of 0.79 and corresponding specificity of 0.30 false spike detections per minute (dotted lines in the figure).

FIGURE 4

Peri-ictal spike rate 20 min before $\left(S_{\mathrm{PRE}}^{i,k}\right)$ and 20 min after $\left(S_{\mathrm{POST}}^{i,k}\right)$ seizure k = 1 from patient 1 with an epileptogenic focus in right hippocampus (RTD1, 2 and 3). Spike rates inside the 5 min gray shaded vertical zone in the graphs (ictal period) are not shown. Panel a: At electrode RTD1 [most anterior right hippocampus—focal electrode (i = 7)]. Panel b: At electrode LTD1 [most anterior left hippocampus—non-focal electrode (i = 1)]. Panel c: At all electrodes (i = 1–28) and color-coded (maximum value of spike rate corresponds to red color and minimum value to blue color). We observe that pre-ictal spike rates per electrode may be higher (panel a) or lower (panel b) than the corresponding post-ictal rates. They may also stay unchanged [e.g., right subtemporal area (RST) in panel c].

FIGURE 5

Single channel analysis of peri-ictal spiking ${\stackrel{‒}{S}}_{\mathrm{PRE}}^i$ vs. ${\stackrel{‒}{S}}_{\mathrm{POST}}^i$ in the EEG from five patients (P1–P5). Channel nomenclature is on the horizontal axis (28 channels: from LTD1 through ROF4) and patient number on the vertical axis. The results of analysis are presented in (a) for clinical seizures and (b) for subclinical seizures. The results are color-coded for visualization purposes. Red denotes significantly higher pre-ictal spike rate than average post-ictal spike rate at a particular brain site averaged over the seizures in the same patient $({\stackrel{‒}{S}}_{\mathrm{PRE}}^i>{\stackrel{‒}{S}}_{\mathrm{POST}}^i)$. Blue color denotes significantly lower average pre-ictal spike rate than average post-ictal spike rate at a particular brain site over the seizures in the same patient $({\stackrel{‒}{S}}_{\mathrm{PRE}}^i>{\stackrel{‒}{S}}_{\mathrm{POST}}^i)$. Green color denotes no significant change in spike rate between pre-ictal and post-ictal periods for brain site i over all seizures in the same patient $({\stackrel{‒}{S}}_{\mathrm{PRE}}^i>{\stackrel{‒}{S}}_{\mathrm{POST}}^i)$. Wilcoxon signed-rank test was performed on the peri-ictal spike rates with statistical significance level of α = 0.05.

FIGURE 6

Classification of seizures per patient using the fraction of brain sites that have higher pre-ictal than post-ictal spike rate (F₊) and the fraction of brain sites that have lower pre-ictal than post-ictal spike rate (F₋). Each seizure in the feature space is denoted by a star, where the coordinates of its position are its F₊ and F₋ values, and its color is red if it is a clinical seizure and blue if it is a subclinical seizure. Classification into two classes was performed using a K-means clustering algorithm for all seizures per patient. The red cloud includes seizures classified as Class 1 (mostly clinical seizures) and the blue cloud seizures classified as Class 2 (mostly sub-clinical seizures).

FIGURE 7

Distribution of duration of subclinical and clinical seizures for the five patients. Clinical seizures tend to have significantly (p < 0.05) longer duration compared to clinical seizure for all patients except P3.