Hossain Syed Monowar, Ali Amin Ahsan, Rahman Mahbubur, Ertin Emre, Epstein David, Kennedy Ashley, Preston Kenzie, Umbricht Annie, Chen Yixin, Kumar Santosh
Dept. of Computer Science, University of Memphis.
Dept of Electrical & Computer Engineering, The Ohio State University.
IPSN. 2014;2014:71-82.
A variety of health and behavioral states can potentially be inferred from physiological measurements that can now be collected in the natural free-living environment. The major challenge, however, is to develop computational models for automated detection of health events that can work reliably in the natural field environment. In this paper, we develop a physiologically-informed model to automatically detect drug (cocaine) use events in the free-living environment of participants from their electrocardiogram (ECG) measurements. The key to reliably detecting drug use events in the field is to incorporate the knowledge of autonomic nervous system (ANS) behavior in the model development so as to decompose the activation effect of cocaine from the natural recovery behavior of the parasympathetic nervous system (after an episode of physical activity). We collect 89 days of data from 9 active drug users in two residential lab environments and 922 days of data from 42 active drug users in the field environment, for a total of 11,283 hours. We develop a model that tracks the natural recovery by the parasympathetic nervous system and then estimates the dampening caused to the recovery by the activation of the sympathetic nervous system due to cocaine. We develop efficient methods to screen and clean the ECG time series data and extract candidate windows to assess for potential drug use. We then apply our model on the recovery segments from these windows. Our model achieves 100% true positive rate while keeping the false positive rate to 0.87/day over (9+ hours/day of) lab data and to 1.13/day over (11+ hours/day of) field data.
从现在可以在自然自由生活环境中收集的生理测量数据中，有可能推断出各种健康和行为状态。然而，主要的挑战是开发能够在自然野外环境中可靠运行的用于自动检测健康事件的计算模型。在本文中，我们开发了一种基于生理信息的模型，用于根据参与者的心电图（ECG）测量数据，在其自由生活环境中自动检测药物（可卡因）使用事件。在野外可靠检测药物使用事件的关键在于，在模型开发中纳入自主神经系统（ANS）行为的知识，以便将可卡因的激活效应与副交感神经系统的自然恢复行为（在一次身体活动之后）区分开来。我们在两个住宅实验室环境中收集了9名活跃吸毒者89天的数据，以及在野外环境中42名活跃吸毒者922天的数据，总计11283小时。我们开发了一个模型，该模型跟踪副交感神经系统的自然恢复过程，然后估计由于可卡因激活交感神经系统而对恢复造成的抑制作用。我们开发了有效的方法来筛选和清理ECG时间序列数据，并提取候选窗口以评估潜在的药物使用情况。然后，我们将模型应用于这些窗口的恢复段。我们的模型在实验室数据（每天9小时以上）上实现了100%的真阳性率，同时将假阳性率保持在每天0.87；在野外数据（每天11小时以上）上实现了100%的真阳性率，并将假阳性率保持在每天1.13。
