Improving Electroencephalogram-based Neurological Disease Diagnosis through the Development of Artificial Intelligence (AI) Methods for Clinical Decision Support

Yogatheesan Varatharajah, University of Illinois, Department of Bioengineering

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Despite the nationwide shortage of neurologists, the present-day neurological care relies heavily on time-consuming visual review of patient data by trained neurologists. This is particularly emphasized in the field of epileptology where epileptologists spend a substantial amount of their time on visually reviewing and interpreting lengthy multi-channel time series of brain electrical activity, called electroencephalography (EEG). This burden not only contributes to the escalation of epileptologist burnout, but also introduces reviewer bias and potential errors in clinical decisions. In fact, several studies have reported that the inter-reviewer agreement in reviewing EEGs is surprisingly poor and that the sensitivity of EEG-based expert visual diagnosis of epilepsy remains, unfortunately, at 50%. As such, the field of neurology, in particular, epileptology, is in need of solutions that can, a) augment expert visual review of EEGs, b) reduce the workload, and c) enhance the reliability, reproducibility, and scalability of EEG review.

The goal of this proposal is to develop a machine-learning(ML)-based decision support framework that works hand-in-hand with epileptologists and focuses their attention to actionable information extracted from lengthy EEG recordings using domain-guided machine learning. This framework can reduce the need for visual review of EEG data and increase the availability of epileptologists to patient care.  Furthermore, we will develop this framework in the context of EEG-based diagnosis of epilepsy and investigate whether our framework can improve the sensitivity of diagnosing epilepsy. A successful demonstration of this framework will require significant innovations across the data-science lifecycle, by leveraging the  spatio-temporal  characteristics  of  EEG  data  and  clinical  domain  expertise.