Automated Detection of Epileptic Biomarkers in Resting-State Interictal MEG Data

Certain differences between brain networks of healthy and epilectic subjects have been reported even during the interictal activity, in which no epileptic seizures occur. Here, magnetoencephalography (MEG) data recorded in the resting state is used to discriminate between healthy subjects and patients with either idiopathic generalized epilepsy or frontal focal epilepsy. Signal features extracted from interictal periods without any epileptiform activity are used to train a machine learning algorithm to draw a diagnosis. This is potentially relevant to patients without frequent or easily detectable spikes. To analyze the data, we use an up-to-date machine learning algorithm and explore the benefits of including different features obtained from the MEG data as inputs to the algorithm. We find that the relative power spectral density of the MEG time-series is sufficient to distinguish between healthy and epileptic subjects with a high prediction accuracy. We also find that a combination of features such as the phase-locked value and the relative power spectral density allow to discriminate generalized and focal epilepsy, when these features are calculated over a filtered version of the signals in certain frequency bands. Machine learning algorithms are currently being applied to the analysis and classification of brain signals. It is, however, less evident to identify the proper features of these signals that are prone to be used in such machine learning algorithms. Here, we evaluate the influence of the input feature selection on a clinical scenario to distinguish between healthy and epileptic subjects. Our results indicate that such distinction is possible with a high accuracy (86%), allowing the discrimination between idiopathic generalized and frontal focal epilepsy types.The authors acknowledge the financial support of the Spanish Ministerio de Economía, Industria y Competitividad (MINECO), grants TEC2016-80063-C3 (AEI/FEDER, UE), FIS2015-71929-REDT (AEI/FEDER, UE), TEC2012-38453-CO4 (AEI/FEDER, UE), and the Castilla La Mancha Sanitary System through grant FISCAM 2009. MS and SO were supported by the Conselleria d'Innovació, Recerca i Turisme del Govern de les Illes Balears and the European Social Fund. GN received financial support of the Spanish MEC through the FPU grant AP2008-02383. The work of MS has also been supported by the Spanish Ministerio de Economía, Industria y Competitividad through a “Ramon y Cajal” Fellowship (RYC-2015-18140).Peer reviewe