Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?  
[PERSON]  
1 Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; [EMAIL_ADDRESS] (N.M.); [EMAIL_ADDRESS] (S.G.); [EMAIL_ADDRESS] (M.P.); [EMAIL_ADDRESS] (P.E.G.)  
[PERSON]  
4 Dipartimento di Matematica, Universita di Milano,  
variety of diagnostic tools that allow defining different cortical zones, each of which is an estimate of the EZ. Firstly, non-invasive electrophysiological tests are performed to estimate the EZ, which include the conventional EEG, high-density EEG, magnetoencephalography, magnetic resonance imaging (MRI), functional MRI, positron emission tomography, and single photon emission computed tomography. The results of these non-invasive tests can be insufficiently concordant and fail to derive a clear hypothesis about the location of the EZ in many children (more often than in adults primarily because the syndromes amenable to surgery are much more heterogeneous in pediatric epilepsy). In these cases, long-term monitoring with intracranial EEG (iEEG) is needed in order to evaluate the possibility of surgery and/or delineate the exact location and extent of the EZ. Direct exploration of the brain with iEEG over several days is, to date, the gold standard to capture seizures and pinpoint the origin of the seizures in the brain, also named the seizure onset zone (SOZ), which represents the best estimator of the EZ. The SOZ identification is typically performed by reviewing ictal data (recorded during seizures), yet seizures are unpredictable; thus, the majority of the recorded iEEG data correspond to the resting state (or absence of a seizure), which is termed an interictal state. This suggests that the development of reliable, interictal methods to estimate the SOZ electrodes without depending on the occurrence of seizures is essential. Interictal data contain vast amounts of information regarding the underlying pathology and can be explored to estimate the EZ and provide surgical guidance. One of the most traditional interictal markers on the EEG is the interictal epileptic discharges, also named interictal spikes, which are seen in the 1-70 Hz frequency range. Although several deep learning approaches (convolutional neural network-based algorithms) have been developed to identify interictal epileptic discharges on the iEEG signals in the time domain [1], the human reading and marking remain the gold standard in clinical practice to identify these signal patterns in the time domain. Reviewing interictal data, though, is extremely time demanding and challenging to human readers who have to look at days of iEEG recording from numerous electrodes in order to identify the contacts showing epileptiform activity.  
Visual scrutiny of the iEEG time-frequency (TF) images can be an alternative way to review iEEG signals, allowing a detailed inspection in both the time and frequency domain. Yet, this can be arduous for the human reader: subtle features of the TF image may be interictal indicators of the EZ that are not perceptible by the human eye. For this reason, the development of novel algorithms and computerized tools to interpret interictal iEEG TF images may be extremely useful in locating the EZ in the brains of patients with drug-resistant epilepsy.