Track: 2. Translational Research / 2E. Other
Data Scientist / Research Scientist
Expesicor is developing PrestEEG, a secure cloud-based EEG analysis tool that automates seizure detection in preclinical EEG data. This platform is unique among existing approaches for preclinical seizure detection and analysis; the user-friendly platform is intuitive enough to be used by non-scientists and incorporates advanced functionality to accommodate experienced preclinical epilepsy researchers. The platform’sproprietary methodologies automatically mark and analyze seizures. A machine learning algorithm runs a secondary validation to identify false positives and provide support for identification of seizures or other neurological biomarkers. Users can scroll through EEG data and review the identified seizures and view summary statistics for the entire dataset. Our machine learning techniques are partnered with advanced data management to enable seizure detection with a validation system that provides reliable results. Additionally, our platform offers a secure and large-scale location to store data accessible around the globe.
Preliminary data has demonstrated that PrestEEG is able to detect seizures and differentiate non-seizure artifacts in one rodent epilepsy model—the rat kainic acid and lorazepam (KaL) model of temporal lobe epilepsy.1 More than 20 hours of data from the KaL model have resulted in seizure detection with approximately 85% accuracy and within +/- 5 seconds of the validated start and finish of the event. Machine learning will increase accuracy and assist in artefact detection for a much larger data set in transit from an Expesicor collaborator.
1. Kienzler-Norwood F, C, et al. A novel animal model of acquired human temporal lobe epilepsy based on the simultaneous administration of kainic acid and lorazepam. Epilepsia 2017;58:222-30.
Conclusions: Expesicor’s versatile, cloud-based electroencephalogram analysis tool reliably identifies epileptiform activity for preclinical epilepsy models. In particular, the ability to reduce false positives will greatly improve the accuracy, pace, and reproducibility of preclinical research. Optimization of PrestEEG across additional models will be followed by further development to adapt the platform to additional epilepsy models, diverse collection methods, and clinical data.
Funding: Please list any funding that was received in support of this abstract.: Private seed funding and indirect funding from an NIH grant.