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 patient-specific models.
|Status||Published - 27-dec-2018|
|Evenement||2018 International Conference on Cyberworlds - Singapore, Singapore|
Duur: 3-okt-2018 → 5-okt-2018
|Conference||2018 International Conference on Cyberworlds|
|Periode||03/10/2018 → 05/10/2018|