Use of Electronic Seizure Diaries and Decision Trees to Predict Seizure Outcome for Patients with Epilepsy

Abstract

- Epilepsy is a neurological disorder that causes unpredictable recurrent seizures. Most people with epilepsy dwell in fear of having unpredictable seizures. In attempts to predict future seizure occurrences, investigators have used data from electronic seizure diaries and machine-learning methods, like decision trees. Using individual patient e-diary data, the purpose of this study is to build patient specific decision trees to 1) determine decision trees overall accuracy in predicting seizures and depicting seizure predictors that influence seizure outcome, and 2) identify seizure predictors that have the most influence on seizure outcome. Patients (n=64) were examined, and their e-diary data was used to build patient specific decision trees. Using a 5-point Likert scale, patients e-diaries entailed information on how they rated the probability of experiencing subsequent seizures and rated their mood, predictive symptoms, stress, and seizure counts. Since e-diaries were recorded in the morning and in the evening, seizures for each patient were assessed by half days. R Programming software was used to generate the decision trees and depict seizure predictors that had the most influence on patient’s seizure outcome. A confusion matrix was performed to obtain the decision trees performance accuracy. Patients were categorized into groups based on certain seizure predictors that they shared. The results showed that for decision trees overall accuracy in predicting seizures and depicting seizure predictors that influenced seizure outcome, 49% of decision trees had an accuracy of 100%; 37% of decision trees had an accuracy ranging between 90-99%; and 13% of decision trees had an accuracy of <90%. Additionally, the results showed that there were more seizure predictors that had influence on patient’s seizure outcome in the morning than in the evening. This work introduces non-invasive precision medicine, with intentions to develop more personalized and reliable health care treatments for people with epilepsy.