Predicting ordinal level of sedation from the spectrogram of electroencephalography

Haoqi Sun, Sunil B. Nagaraj, M. Brandon Westover

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    1 Citation (Scopus)

    Abstract

    In Intensive Care Unit, the sedation level of patients is usually monitored by periodically assessing the behavioral response to stimuli. However, these clinical assessments are limited due to the disruption with patients' sleep and the noise of observing behaviors instead of the brain activity directly. Here we train a Gated Recurrent Unit using the spectrogram of electroencephalography (EEG) based on 166 mechanically ventilated patients to predict the Richmond Agitation-Sedation Score, scored as ordinal levels of-5,-4,. Up to 0. The model is able to predict 50% accurate with an error not larger than 1 level; and 80% accurate with an error not larger than 2 levels on hold-out testing patients. We show typical spectrograms in each sedation level and interpret the results based on the visualization of the gradient with respect to the spectrogram. Future improvements include utilizing the EEG waveforms since waveform patterns are clinically thought to be associated with sedation levels, as well as training patientspecific models.

    Original languageEnglish
    Title of host publicationProceedings - 2018 International Conference on Cyberworlds, CW 2018
    EditorsAlexei Sourin, Olga Sourina, Marius Erdt, Christophe Rosenberger
    PublisherIEEE
    Pages292-295
    Number of pages4
    ISBN (Electronic)9781538673157
    DOIs
    Publication statusPublished - 26 Dec 2018
    Event17th International Conference on Cyberworlds, CW 2018 - Singapore, Singapore
    Duration: 3 Oct 20185 Oct 2018
    Conference number: 17

    Conference

    Conference17th International Conference on Cyberworlds, CW 2018
    Abbreviated titleCW 2018
    CountrySingapore
    CitySingapore
    Period3/10/185/10/18

    Keywords

    • EEG
    • Machine learning
    • Ordinal
    • Sedation

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