• Sunil B Nagaraj, Lauren M McClain, David W Zhou, Siddharth Biswal, Eric S Rosenthal, Patrick L Purdon, and M Brandon Westover.
• 1Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.2Department of Anaesthesia, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
• Crit. Care Med. 2016 Sep 1; 44 (9): e782-9.

ObjectiveTo explore the potential value of heart rate variability features for automated monitoring of sedation levels in mechanically ventilated ICU patients.DesignMulticenter, pilot study.SettingSeveral ICUs at Massachusetts General Hospital, Boston, MA.PatientsElectrocardiogram recordings from 40 mechanically ventilated adult patients receiving sedatives in an ICU setting were used to develop and test the proposed automated system.Measurements And Main ResultsRichmond Agitation-Sedation Scale scores were acquired prospectively to assess patient sedation levels and were used as ground truth. Richmond Agitation-Sedation Scale scores were grouped into four levels, denoted "unarousable" (Richmond Agitation- Sedation Scale = -5, -4), "sedated" (-3, -2, -1), "awake" (0), "agitated" (+1, +2, +3, +4). A multiclass support vector machine algorithm was used for classification. Classifier training and performance evaluations were carried out using leave-oneout cross validation. An overall accuracy of 69% was achieved for discriminating between the four levels of sedation. The proposed system was able to reliably discriminate (accuracy = 79%) between sedated (Richmond Agitation-Sedation Scale < 0) and nonsedated states (Richmond Agitation-Sedation Scale > 0).ConclusionsWith further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and undersedation.

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