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Byte-Pair Encoding for classifying routine clinical electroencephalograms in adults over the lifespan
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  • Mykola Klymenko ,
  • Sam M. Doesburg ,
  • George Medvedev ,
  • Pengcheng Xi ,
  • Urs Ribary ,
  • Vasily A. Vakorin
Mykola Klymenko
Simon Fraser University

Corresponding Author:[email protected]

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Sam M. Doesburg
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George Medvedev
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Pengcheng Xi
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Urs Ribary
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Vasily A. Vakorin
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Abstract

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.
2023Published in IEEE Journal of Biomedical and Health Informatics on pages 1-11. 10.1109/JBHI.2023.3236264