Matt Stead

TL;DR: A state-of-the-art, scalable, electrophysiology platform designed for acquisition, compression, encryption, and storage of large-scale data is described that incorporates lossless data compression using range-encoded differences, a 32-bit cyclically redundant checksum to ensure data integrity, and 128-bit encryption for protection of patient information.

TL;DR: In this article, an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics was proposed to identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy.

TL;DR: This report reports an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics as a way of accounting for the above barriers and shows that it can reliably identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy.

TL;DR: A novel file format that employs range encoding to provide a high degree of data compression, a three-tiered 128-bit encryption system for patient information and data security, and a 32-bit cyclic redundancy check to verify the integrity of compressed data blocks is presented.

TL;DR: The Multi-scale Annotation Format (MAF) provides an integrated metadata and annotation environment that maximizes code reuse, minimizes error probability and encourages future changes by reducing the tendency to over-fit information technology solutions to current problems.