Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy

Abstract

Background This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervised learning methods in patients with drug-resistant focal seizures suspected to begin in temporal lobe. Method We applied machine learning, specifically a combination of mutual information-based feature selection and supervised learning classifiers on multimodal data, to predict surgery outcome retrospectively in 20 presurgical patients (13 female; mean age±SD, in years 33±9.7 for females, and 35.3±9.4 for males) who were diagnosed with mesial temporal lobe epilepsy (MTLE) and subsequently underwent standard anteromesial temporal lobectomy. The main advantage of the present work over previous studies is the inclusion of the extent of ipsilateral neocortical gray matter atrophy and spatiotemporal properties of depth electrode-recorded seizures as training features for individual patient surgery planning. Results A maximum relevance minimum redundancy (mRMR) feature selector identified the following features as the most informative predictors of postsurgical seizure freedom in this study's sample of patients: family history of epilepsy, ictal EEG onset pattern (positive correlation with seizure freedom), MRI-based gray matter thickness reduction in the hemisphere ipsilateral to seizure onset, proportion of seizures that first appeared in ipsilateral amygdala to total seizures, age, epilepsy duration, delay in the spread of ipsilateral ictal discharges from site of onset, gender, and number of electrode contacts at seizure onset (negative correlation with seizure freedom). Using these features in combination with a least square support vector machine (LS-SVM) classifier compared to other commonly used classifiers resulted in very high surgical outcome prediction accuracy (95%). Conclusions Supervised machine learning using multimodal compared to unimodal data accurately predicted postsurgical outcome in patients with atypical MTLE.

Machine learning applications in epilepsy

“…More recently, Armañanzas et al compared k ‐NN and Naïve Bayes classifiers in predicting Engel I vs II–III outcomes using similar presurgical data, although with the notable addition of a larger neuropsychological battery that included personality style as assessed using a Rorschach test; after narrowing the classifiers to the three most informative variables (including personality style), the authors noted predictive accuracies of 89.47% with either classifier . Memarian et al instead compared linear discriminant analysis with Naïve Bayes and SVM classifiers in examining preoperative clinical, electrophysiologic, and structural MRI data from 20 patients, noting highest accuracy (of 95%) in predicting Engel I outcomes using a least‐square SVM classifier . In contrast, a study utilizing an SVM classifier only on T1‐weighted MRI sequences from 49 patients demonstrated 100% sensitivity and 88%‐92% specificity (in male and female cohorts, respectively) in predicting postsurgical seizure freedom .…”

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

“…Nevertheless, those findings had limited clinical applications. Machine learning can be adopted for single subject prediction and has shown significant potential for disease diagnosis [69][70][71][72] at the individual level.…”

Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey

“…18,28,29 To evaluate semiology, movements can be analyzed using accelerometry, surface electromyography, video detection systems, mattress sensor, and seizure-alert dogs; physiological signals can be evaluated by electrocardiography, skin temperature, audio classification, and respiratory rate changes techniques. 33,34 These techniques are used to extract features and to perform a decision using electrophysiological signals. 18 Video detection systems and electroencephalography have been considered the gold standard for the diagnosis of seizures.…”

“…Researchers have implemented machine learning algorithms and different detection devices to increase the performance of epilepsy tasks, including epilepsy diagnosis, 30,31 automatic classification of epilepsy types, 32 and outcome prediction after surgery. 33,34 These techniques are used to extract features and to perform a decision using electrophysiological signals. To assess seizures with motor phenomena, which is the scope of this paper, motion trajectories of markers and computer vision techniques have been used as feasible methods in recognizing kinematic patterns of seizure.…”