title: Balancing Clinical and Pathologic Relevence in the Machine Learning Diagnosis of Epilepsy.
abstract: The application of machine learning to epilepsy can be used both to develop clinically useful computer-aided diagnostic tools, and to reveal pathologically relevant insights into the disease. Such studies most frequently use neurologically normal patients as the control group to maximize the pathologic insight yielded from the model. This practice yields potentially inflated accuracy because the groups are quite dissimilar. A few manuscripts, however, opt to mimic the clinical comparison of epilepsy to non-epileptic seizures, an approach we believe to be more clinically realistic. In this manuscript, we describe the relative merits of each control group. We demonstrate that in our clinical quality FDG-PET database the performance achieved was similar using each control group. Based on these results, we find that the choice of control group likely does not hinder the reported performance. We argue that clinically applicable computer-aided diagnostic tools for epilepsy must directly address the clinical challenge of distinguishing patients with epilepsy from those with non-epileptic seizures.
authors.affiliation: Laboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
authors.forename: Wesley T
authors.initials: WT
authors.lastname: Kerr
authors.affiliation: Laboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
authors.forename: Andrew Y
authors.initials: AY
authors.lastname: Cho
authors.affiliation: Laboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
authors.forename: Ariana
authors.initials: A
authors.lastname: Anderson
authors.affiliation: Laboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
authors.forename: Pamela K
authors.initials: PK
authors.lastname: Douglas
authors.affiliation: Laboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
authors.forename: Edward P
authors.initials: EP
authors.lastname: Lau
authors.affiliation: Laboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
authors.forename: Eric S
authors.initials: ES
authors.lastname: Hwang
authors.affiliation: Laboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
authors.forename: Kaavya R
authors.initials: KR
authors.lastname: Raman
authors.affiliation: Laboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
authors.forename: Aaron
authors.initials: A
authors.lastname: Trefler
authors.affiliation: Laboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
authors.forename: Mark S
authors.initials: MS
authors.lastname: Cohen
authors.affiliation: Ahmanson Translational Imaging Division University of California, Los Angeles Los Angeles, USA.
authors.forename: Stefan T
authors.initials: ST
authors.lastname: Nguyen
authors.affiliation: Ahmanson Translational Imaging Division University of California, Los Angeles Los Angeles, USA.
authors.forename: Navya M
authors.initials: NM
authors.lastname: Reddy
authors.affiliation: Ahmanson Translational Imaging Division University of California, Los Angeles Los Angeles, USA.
authors.forename: Daniel H
authors.initials: DH
authors.lastname: Silverman
keywords: FDG-PET; controls; epilepsy; machine learning; neuroimaging; non-epileptic seizures