title: 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.
  
  
  METHODS
  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.
authors.affiliation: Department of Psychology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States. Electronic address: nmemarian@ucla.edu.
authors.forename: Negar
authors.initials: N
authors.lastname: Memarian
authors.affiliation: Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States.
authors.forename: Sally
authors.initials: S
authors.lastname: Kim
authors.affiliation: Department of Neurosurgery, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States.
authors.forename: Sandra
authors.initials: S
authors.lastname: Dewar
authors.affiliation: Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Neurosurgery, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Neurobiology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States.
authors.forename: Jerome
authors.initials: J
authors.lastname: Engel
authors.affiliation: Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Staba
mesh_terms: D000328:Adult; D000069280:Electrocorticography; D004833:Epilepsy, Temporal Lobe / Q000175:diagnosis / Q000503:physiopathology / Q000601:surgery; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D011237:Predictive Value of Tests; D011379:Prognosis; D012189:Retrospective Studies; D012815:Signal Processing, Computer-Assisted; D016896:Treatment Outcome; D055815:Young Adult
keywords: Mesial temporal epilepsy; Mutual information; Supervised learning; Surgical outcome prediction