Interictal scalp EEG spike asymmetry to predict seizure onset zone laterality in temporal lobe epilepsy

Calculator Information:

Purpose: Use the relative proportion of interictal spikes in the left and right hemispheres to predict the side (left or right) of the seizure onset zone in patients with temporal lobe epilepsy undergoing scalp EEG.

Instructions for use: Choose scalp EEG data in wake and sleep. Count (using an automated spike detector, SpikeNet2 or Persyst) the number of spikes in the left and right hemisphere contacts. Input these values in the calculator. The units (spikes/minute or spikes/hour, etc.) do not matter in the calculation.

Interpretation of results

The equation for spike asymmetry index (SAI) is:

\[\frac{Spikes_{Left} - Spikes_{Right}}{Spikes_{Left} + Spikes_{Right}}\]

Where Spikes_Left is the number of spikes in the left hemisphere, and Spikes_Right is the number of spikes in the right hemisphere. SAI ranges between -1 and 1. A positive SAI implies more spikes on the left, a negative AI implies more spikes on the right, and an SAI of zero implies an equal number of spikes in the left and right hemispheres. We performed bootstrapping on our data and we used 1000 model coefficients for each model to generate 95% confidence intervals.

The formula for Model 1 (predicting the probability of left-sided seizure onset zone) is:

\[Probabililty_{Left} = \frac{1}{1+exp(-coef_{1} - SAI \times coef_{2})}\]

The formula for Model 2 (predicting the probability of right-sided seizure onset zone) is:

\[Probabililty_{Right} = \frac{1}{1+exp(-coef_{3} - SAI \times coef_{4})}\]

These probabilities are the outputs of two separate logistic regression models trained on data from patients at the Hospital of the University of Pennsylvania. The models separately predict the probabilities of left- and right-sided seizure onset zones.

Accuracy of the results:

When the models were tested on a distinct cohort of patients from the Hospital of the University of Pennsylvania, the performance of the two models was:

Model 1: AUC = 0.78 (95% CI 0.69-0.87), accuracy = 70.0%

Model 2: AUC = 0.80 (95% CI 0.70-0.88), accuracy = 75.0%