Automatic Classification of Sedation Levels in ICU Patients Using Heart Rate Variability

Abstract

Objective: To explore the potential value of heart rate variability features for automated monitoring of sedation levels in mechanically ventilated ICU patients. Design: Multicenter, pilot study. Setting: Several ICUs at Massachusetts General Hospital, Boston, MA. Patients: Electrocardiogram 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 Results: Richmond 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). Conclusions: With 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.