title: Identifying Drug (Cocaine) Intake Events from Acute Physiological Response in the Presence of Free-living Physical Activity.
abstract: 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.
authors.affiliation: Dept. of Computer Science, University of Memphis.
authors.forename: Syed Monowar
authors.initials: SM
authors.lastname: Hossain
authors.affiliation: Dept of Electrical & Computer Engineering, The Ohio State University.
authors.forename: Amin Ahsan
authors.initials: AA
authors.lastname: Ali
authors.affiliation: NIDA Intramural Research Program.
authors.forename: Mahbubur
authors.initials: M
authors.lastname: Rahman
authors.affiliation: Dept. of Psychiatry and Behavioral Sciences, Johns Hopkins University.
authors.forename: Emre
authors.initials: E
authors.lastname: Ertin
authors.affiliation: Dept of Computer Science and Engg., Washington University in St. Louise.
authors.forename: David
authors.initials: D
authors.lastname: Epstein
authors.forename: Ashley
authors.initials: A
authors.lastname: Kennedy
authors.forename: Kenzie
authors.initials: K
authors.lastname: Preston
authors.forename: Annie
authors.initials: A
authors.lastname: Umbricht
authors.forename: Yixin
authors.initials: Y
authors.lastname: Chen
authors.forename: Santosh
authors.initials: S
authors.lastname: Kumar
keywords: Drug Event Detection; Electrocardiogram; Wearable Sensors