Personalized prediction model for seizure-free epilepsy with levetiracetam therapy: a retrospective data analysis using support vector machine

Abstract

Aims: To predict the probability of a seizure-free (SF) state in patients with epilepsy (PWEs) after treatment with levetiracetam and to identify the clinical and electroencephalographic (EEG) factors that affect outcomes.

Methods: Retrospective analysis of PWEs treated with levetiracetam for 3 years identified 22 patients who were SF and 24 who were not. Before starting levetiracetam, 11 clinical factors and four EEG features (sample entropy of α, β, θ, δ) were identified. Overall, 80% of each the two groups were chosen to establish a support vector machine (SVM) model with 5-fold cross-validation, hold-out validation and jack-knife validation. The other 20% were used to predict the efficacy of levetiracetam. The mean impact value (MIV) algorithm was used to rank the relativity between factors and outcomes.

Results: Compared with SF patients, not SF patients displayed a specific decrease in EEG sample entropy in α band from the F4 channel, β band from Fp2 and F8 channels, θ band from C3 channel (P < 0.05). The SVM model based on the clinical and EEG features yielded 72.2% accuracy of 5-fold cross-validation, 75.0% accuracy of jack-knife validation, 67.7% accuracy of hold-out validation in the training set and had a high prediction accuracy of 90% in test set (sensitivity was 100%, area under the receiver operating characteristic curve was 0.96). The feature of β band from Fp2 weighs heavily in the prediction model according to the mean impact value algorithm.

Conclusions: The efficacy of levetiracetam on newly diagnosed PWEs could be predicted using an SVM model, which could guide antiepileptic drug selection.

Keywords: effectiveness; epilepsy; methodology; neuroscience; therapeutics.

Figure 1

Architecture of the support vector machine (SVM)‐based outcome prediction system. Various stages of the algorithm such as feature extraction, classification and post‐processing are schematically shown. The detailed process of electroencephalography (EEG) features extraction is present in dashed box which includes converting raw EEG outputs to Sample entropy. LEV, levetiracetam

Figure 2

Sample entropy of the bands with a significant difference between seizure‐free (SF) and not seizure‐free (NSF) groups. Graphic presentation (box‐plot diagrams) of relative sample entropy within each frequency band in channels FP2, F4, C3, and F8, which are significantly different between the SF and NSF patients. The lower and upper borders of the rectangular box correspond to the 25% and 75% percentiles of the data, with the median indicated by the black line. The red box represents the SF group, and the blue box represents the NSF group. *indicate statistically significant results. Compared with SF patients, the NSF patients had significantly decreased Sample Entropy in the β frequency band of Fp2, α frequency band of F4, θ frequency band of C3, and β frequency band of F8 (Mann–Whitney U test, P < 0.05)

Figure A1

Diagram of patients across the study period. PWEs, patients with epilepsy; AED, antiepileptic drug.

Figure A2

The result of prediction outcome with decrease of sample size. Blue line = training set; red line = test set; green line = 5‐fold cross validation. With the decrease of sample size, the gap of accuracy between the training set and the test set is obvious widening, the result of 5‐fold cross‐validation become lower and lower.