کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
9988824 1579806 2018 8 صفحه PDF سفارش دهید دانلود رایگان
عنوان انگلیسی مقاله ISI
Machine learning as a new paradigm for characterizing localization and lateralization of neuropsychological test data in temporal lobe epilepsy
ترجمه فارسی عنوان
یادگیری ماشین به عنوان یک پارادایم جدید برای توصیف مکانیزاسیون و لترالیزاسیون داده های آزمون نوروپسیکتیک در صرع لوب صرع
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موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب رفتاری
چکیده انگلیسی
In this study, we employed a kernel support vector machine to predict epilepsy localization and lateralization for patients with a diagnosis of epilepsy (n = 228). We assessed the accuracy to which indices of verbal memory, visual memory, verbal fluency, and naming would localize and lateralize seizure focus in comparison to standard electroencephalogram (EEG). Classification accuracy was defined as models that produced the least cross-validated error (CVϵ). In addition, we assessed whether the inclusion of norm-based standard scores, demographics, and emotional functioning data would reduce CVϵ. Finally, we obtained class probabilities (i.e., the probability of a particular classification for each case) and produced receiver operating characteristic (ROC) curves for the primary analyses. We obtained the least error assessing localization data with the Gaussian radial basis kernel function (RBF; support vectors = 157, CVϵ = 0.22). There was no overlap between the localization and lateralization models, such that the poorest localization model (the hyperbolic tangent kernel function; support vectors = 91, CVϵ = 0.36) outperformed the strongest lateralization model (RBF; support vectors = 201, CVϵ = 0.39). Contrary to our hypothesis, the addition of norm, demographics, and emotional functioning data did not improve the accuracy of the models. Receiver operating characteristic curves suggested clinical utility in classifying epilepsy lateralization and localization using neuropsychological indicators, albeit with better discrimination for localizing determinations. This study adds to the existing literature by employing an analytic technique with inherent advantages in generalizability when compared to traditional single-sample, not cross-validated models. In the future, class probabilities extracted from these and similar analyses could supplement neuropsychological practice by offering a quantitative guide to clinical judgements.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Epilepsy & Behavior - Volume 86, September 2018, Pages 58-65
نویسندگان
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