PhD Dissertation Defense - Syed Monowar Hossain

Identifying Drug (Cocaine) Intake Events from Acute Physiological Response in the Presence of Free-living Physical Activity

Syed Monowar Hossain, PhD Candidate

Thursday, April 6, 2017, 9:00-10:30 am
Dunn Hall 311

Committee Members:
Prof. Santosh Kumar, Chair
Prof. Deepak Venugopal
Prof. Nirman Kumar
Prof. Emre Ertin (Ohio State University)

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 work, 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 collected 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. As our preliminary work, we developed 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 developed efficient methods to screen and clean the ECG time series data and extract candidate windows to assess for potential drug use. We then applied 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. In this work, in order to further improve the sensitivity and specificity of our model we propose several new data screening methods. Also we propose methods to remove the effects of activities that acts as a confounder. We observed the false positive rates of 0.78 and 0.98 per day when we apply the enhanced model to the lab and field data respectively. Moreover, we observe that the proposed model has high specificity to cocaine. The model does not produce any false positives, when it is run on data from non-cocaine days where the study participants self-reported intake of other drugs than cocaine. We also proposed to develop methods to estimate dosage of drug intake using the collected lab and field data. The proposed model however does not predict the dosage amount reliably for high dosage amount which is observed in the free living conditions. This is due to the lack of data corresponding to high dosage amount in the lab setting.