Byte-Pair Encoding for classifying routine clinical electroencephalograms in adults over the lifespan
Our study considered the problem of classifying routine clinical EEG. We incorporated NLP tools into the workflow for time series classification. We transformed EEG signals into strings of symbols. We then applied byte-pair encoding (BPE) to split the new text into combinations of symbols or tokens, each associated with different patterns of changes in EEG amplitude. We validated the proposed workflow under the framework of predicting patients' biological age.
National Research Council (NRC) of Canada, Collaborative Research and Development Grant DHGA-116-1
Digital Research Alliance of Canada, Research Platforms and Portals (RPP) grant
Email Address of Submitting Authorthe.firstname.lastname@example.org
ORCID of Submitting Author0000-0002-3059-9363
Submitting Author's InstitutionSimon Fraser University, BC, Canada
Submitting Author's Country