Detecting cocaine use with wearable electrocardiogram sensors

Authors:
Annamalai Natarajan;Abhinav Parate;Edward Gaiser;Gustavo Angarita;Robert Malison;Benjamin Marlin;Deepak Ganesan
Affiliations:
University of Massachusetts, Amherst, MA, USA;University of Massachusetts, Amherst, MA, USA;Yale School of Medicine, New Haven, CT, USA;Yale School of Medicine, New Haven, CT, USA;Yale School of Medicine, New Haven, CT, USA;University of Massachusetts, Amherst, MA, USA;University of Massachusetts, Amherst, CT, USA
Venue:
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Year:
2013

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Abstract

Ubiquitous physiological sensing has the potential to profoundly improve our understanding of human behavior, leading to more targeted treatments for a variety of disorders. The long term goal of this work is development of novel computational tools to support the study of addiction in the context of cocaine use. The current paper takes the first step in this important direction by posing a simple, but crucial question: Can cocaine use be reliably detected using wearable electrocardiogram (ECG) sensors? The main contributions in this paper include the presentation of a novel clinical study of cocaine use, the development of a computational pipeline for inferring morphological features from noisy ECG waveforms, and the evaluation of feature sets for cocaine use detection. Our results show that 32mg/70kg doses of cocaine can be detected with the area under the receiver operating characteristic curve levels above 0.9 both within and between-subjects.