Spadafore Maxwell T, Syed Zeeshan, Rubinfeld Ilan S
University of Michigan, Ann Arbor, MI.
Henry Ford Hospital, Detroit, MI.
AMIA Annu Symp Proc. 2015 Nov 5;2015:2083-91. eCollection 2015.
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%.
为了在重症监护病房（ICU）环境中实现患者镇静的自动维持，必须开发更强大、定量的镇静深度指标。在本研究中，我们展示了一个完全计算系统的可行性，该系统利用单导联的低质量心电图（ECG）来检测受试者系统中苯二氮䓬类镇静剂的存在。从心脏病专家通常手动检查以寻找中毒证据的特征开始，我们将这些特征的提取推广到一个完全自动化的过程。我们使用重症监护临床数据库中的九名受试者测试了这些特征的预测能力。发现这些特征显著表明剂量与心电图形态之间存在二元关系，但我们未能找到可预测的连续关系的证据。将这种二元关系拟合到一个分类器上，我们实现了89%的灵敏度和95%的特异性。
