Daily interruption of sedative infusions in critically ill patients undergoing mechanical ventilation.
Aim: To develop an automated system to monitor sedation levels in intensive care unit patients using heart rate variability (HRV). Methods: We developed an automatic sedation level prediction system using HRV as input to a support vector machine learning algorithm. Our data consisted of electrocardiogram recordings from a heterogeneous group of 50 mechanically ventilated adults receiving sedatives in an ICU setting. The target variable was the Richmond agitation-sedation scale score, grouped into four levels: “comatose” (−5), “deeply sedated” (−4 to −3), “lightly sedated” (−2 to 0), and “agitated” (+1 to +4). As input we used 14 features derived from the normalized-RR (NN) interval. We used leave-one-subject-out cross-validated accuracy to measure system performance. Results: A patient-independent version of the proposed system discriminated between the 4 sedation levels with an overall accuracy of 52%. A patient-specific version, where the training data was supplemented with the patient's labeled HRV epochs from the preceding 24 hours, improved classification accuracy to 60%. Conclusions: Our preliminary results suggest that the HRV varies systematically with sedation levels and has potential to supplement current clinical sedation level assessment methods. With additional variables such as disease pathology, and pharmacological data, the proposed system could lead to a fully automated system for depth of sedation monitoring.