Wiebe, Samuel

AbstractEpilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.

Sixty-four children, aged 0-17 years, undergoing ambulatory electroencephalography (EEG) were prospectively recruited during a 12-month period. The diagnostic yield of ambulatory electroencephalography was determined for each of the following groups: group 1: differentiation of seizures from nonepileptic events; group 2: determination of seizure/interictal discharge frequency; and group 3: classification of seizure type or localization. The ambulatory electroencephalography answered the clinical question in 61% of group 1 (27/44) and 100% of groups 2 (16/16) and 3 (4/4). Of 44 cases in Group 1, clinical events were recorded in 61%; the ambulatory electroencephalography result changed the diagnosis from epileptic to nonepileptic or vice versa in 27%. When clinicians suspected that events were epileptic, ambulatory electroencephalography changed the clinical impression in 50%, whereas when events were suspected to be nonepileptic, ambulatory electroencephalography confirmed that impression in 83%.