Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability

Abstract

Objective: To develop a personalizable algorithm to discriminate between sedation levels in ICU patients based on heart rate variability.

Design: Multicenter, pilot study.

Setting: Several ICUs at Massachusetts General Hospital, Boston, MA.

Patients: We gathered 21,912 hours of routine electrocardiogram recordings from a heterogenous group of 70 adult ICU patients. All patients included in the study were mechanically ventilated and were receiving sedatives.

Measurements and main results: As "ground truth" for developing our method, we used Richmond Agitation Sedation Scale scores grouped into four levels denoted "comatose" (-5), "deeply sedated" (-4 to -3), "lightly sedated" (-2 to 0), and "agitated" (+1 to +4). We trained a support vector machine learning algorithm to calculate the probability of each sedation level from heart rate variability measures derived from the electrocardiogram. To estimate algorithm performance, we calculated leave-one-subject out cross-validated accuracy. The patient-independent version of the proposed system discriminated between the four sedation levels with an overall accuracy of 59%. Upon personalizing the system supplementing the training data with patient-specific calibration data, consisting of an individual's labeled heart rate variability epochs from the preceding 24 hours, accuracy improved to 67%. The personalized system discriminated between light- and deep-sedation states with an average accuracy of 75%.

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 under sedation.

Figure 1

Illustration of (a) the proposed automatic sedation classification system, (b) the calibration window (initial 24 hours of ECG ) and prediction window using 15 minutes of ECG (15 minutes was found as optimal window length in the classification) for patient-specific system, (c) identifying optimal RRI epoch length for sedation level assessment (d) a sample RRI, (e) its corresponding spectrogram, and (f) sample probability output from the patient specific classification where day 1 recording is used for calibrating the system and the predicted on the remaining days.

Figure 2

Illustration of the proposed patient-specific AHSISt with LOSO performance assessment and 10-fold optimal parameter selection routines. Note that day 1 recordings are used during training process in case of patient-specific system. Data from the test patient was not used in case of patient-independent system.

Figure 3

The distribution of the epochs classified by the proposed automatic sedation system for different sedation levels, and (b) accuracy of the proposed patient-specific AHSISt for individual patients.