An interactive framework for the detection of ictal and interictal activities: Cross-species and stand-alone implementation.
Journal:
Computer methods and programs in biomedicine
Published Date:
Mar 2, 2022
Abstract
BACKGROUND AND OBJECTIVE: Despite advances on signal analysis and artificial intelligence, visual inspection is the gold standard in event detection on electroencephalographic recordings. This process requires much time of clinical experts on both annotating and training new experts for this same task. In scenarios where epilepsy is considered, the need for automatic tools is more prominent, as both seizures and interictal events can occur on hours- or days-long recordings. Although other solutions have already been proposed, most of them are not integrated on clinical and basic science environments due to their complexity and required specialization. Here we present a pipeline that arises from coordinated efforts between life-science researchers, clinicians and data scientists to develop an interactive and iterative workflow to train machine-learning tools for the automatic detection of electroencephalographic events in a variety of scenarios.