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Review
. 2019 Oct;60(10):2037-2047.
doi: 10.1111/epi.16333. Epub 2019 Sep 3.

Machine learning applications in epilepsy

Bardia Abbasi  1 , Daniel M Goldenholz  1
Affiliations
Review

Machine learning applications in epilepsy

Bardia Abbasi et al. Epilepsia. 2019 Oct.

Abstract

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre-surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.

Keywords: artificial intelligence; deep learning; epilepsy imaging; epilepsy surgery; seizure detection.

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Figures

Figure 1.
Figure 1.
Overview of machine learning concepts. A, In supervised learning (top), an algorithm is trained on labeled input data to generate predicted outputs, while in unsupervised learning (bottom), the algorithm uncovers subgroups or outliers in unlabeled data. B, The random forest algorithm generates a forest of decision trees, each utilizing subsets of input features as bifurcation points to differentiate the training data into expected outputs; the output of the ensemble (e.g., the majority vote) is reported for new inputs. C, In k-nearest neighbor classification, an input is plotted as a vector within a feature space alongside labeled data, and is subsequently assigned to the class of its k nearest neighbors (here, k = 4). D, Support vector machines generate a hyperplane in higher-dimensional feature space to maximally separate labeled training data. E, In artificial neural networks, input data (far left) is passed through a non-linear activation function and assigned a weight, projecting through intermediary nodes until reaching the output node (far right) for classification. F, In cross-validation, a subset of the training data is withheld as the validation set (yellow), allowing for fine-tuning of an algorithm parametrized on the training set (light green); after multiple iterations (here showing K-fold cross-validation with K = 5), the algorithm may be tested on an initially withheld testing set (dark green) to assess accuracy and generalizability of the finalized model.
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