S87. Sedation prediction using EEG spectrogram in ICU patients. (May 2018)

• Record Type: Journal Article
• Authors: Sun, Haoqi; Nagaraj, Sunil; Purdon, Patrick L.; Westover, M. Brandon
• Is Part Of: Clinical neurophysiology. Volume 129(2018)Supplement 1
• Page Start: e174
• Page End: e175
• Publication Date: 2018-05
• Subjects: Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2018.04.447
• Languages: English
• ISSNs: 1388-2457

Abstract

Abstract : Introduction: Most mechanically ventilated ICU patients receive sedatives to relieve pain and anxiety, and to provide cardiopulmonary stability. Unfortunately, both excessive and insufficient sedation are common. Brain monitors that track electroencephalogram (EEG) features have been proposed as a real-time, physiologically-based alternative to clinical sedation assessments. However, existing monitors have been tested almost exclusively in the surgical setting, without being optimized for ICU patients. Methods: Patients: We analyzed prospectively collected data from 115 mechanically ventilated patients receiving usual ICU care. The Richmond agitation sedation scale (RASS), assessed every 2 h, provides reference sedation levels. In the present work, we consider only assessments with RASS −5 and −4 (deeply sedated) vs −1 and 0 (not sedated). In total, there are 664 RASS assessments. The dataset is split into 69 training, 23 validation and 23 testing patients. Label denoising: RASS assessments are sometimes recorded after a delay or in anticipation of a change in the level of consciousness following adjustment in sedative infusion rate. To reduce such "annotation noise", before training a classifier we first "denoise" EEG segments whose spectra have a different label than the 10 most similar training segments.Classifier training and testing: We extract EEG power spectra from the 10 min period preceding each RASS assessment, using 10s windows sliding windows spaced 2s apart. The sequence of spectra is used to train a recurrent neural network (LSTM), which performs binary classification (RASS −5 and −4 vs −1 and 0). Performance is measured by area the under the receiver operator curve (AUC). The reported results are the average performance on the testing set from 10 random splits of patients. Strict separation of training and validation data from testing data is maintained throughout all experiments. Results: The label denoising procedure alters 15% of RASS scores. The AUC in the testing set is 0.91 (SD 0.02). Visualization of the EEG spectrograms reveals lower total power and higher relative delta power for episodes of RASS −5 and −4; and higher total power and higher relative beta power for RASS −1 and 0. Conclusion: Despite heterogeneous medical conditions and varying severity of medical illness in our ICU cohort, our model learns to accurately discriminate deep sedation (RASS −5 and −4) from the awake state (RASS −1and 0). The classifier achieves AUC at 0.91 in a patient-independent manner. The use of recurrent network architecture allows our model to take advantage of long-range temporal information in the EEG, and will allow the extension to take pharmacokinetics and pharmacodynamics information into account, which may further enhance performance and robustness.