Automated Detection of Benzodiazepine Dosage in ICU Patients Through a Computational Analysis of Electrocardiographic Data

To enable automated maintenance of patient sedation in an intensive care unit (ICU) setting, more robust, quantitative metrics of sedation depth must be developed. In this study, we demonstrated the feasibility of a fully computational system that leverages low-quality electrocardiography (ECG) from a single lead to detect the presence of benzodiazepine sedatives in a subject's system. Starting with features commonly examined manually by cardiologists searching for evidence of poisonings, we generalized the extraction of these features to a fully automated process. We tested the predictive power of these features using nine subjects from an intensive care clinical database. Features were found to be significantly indicative of a binary relationship between dose and ECG morphology, but we were unable to find evidence of a predictable continuous relationship. Fitting this binary relationship to a classifier, we achieved a sensitivity of 89% and a specificity of 95%.