skip to main content
10.1145/2493432.2493496acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
research-article

Detecting cocaine use with wearable electrocardiogram sensors

Published: 08 September 2013 Publication History

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.

References

[1]
Alamudun, F., Choi, J., Gutierrez-Osuna, R., Khan, H., and Ahmed, B. Removal of subject-dependent and activity-dependent variation in physiological measures of stress. In Pervasive Computing Technologies for Healthcare, Proceedings of the 6th International Conference on (2012), 115--122.
[2]
Bouhenguel, R., and Mahgoub, I. A risk and incidence based atrial fibrillation detection scheme for wearable healthcare computing devices. In Pervasive Computing Technologies for Healthcare, Proceedings of the 6th International Conference on (2012), 97--104.
[3]
Bouhenguel, R., Mahgoub, I., and llyas, M. An energy efficient model for monitoring and detecting atrial fibrillation in wearable computing. In Body Area Networks, Proceedings of the 7th International Conference on (2012), 59--65.
[4]
Clifford, G. D. Ecgtools. http://www.robots.ox.ac.uk/~gari/code.html.
[5]
Elman, I., Krause, S., Breiter, H., Gollub, R. L., Heintges, J., Baumgartner, W. A., Rosen, B. R., and Gastfriend, D. R. The validity of self-reported drug use in non-treatment seeking individuals with cocaine dependence: correlation with biochemical assays. The American Journal on Addictions 9, 3 (2000), 216--221.
[6]
Friedman, J., Hastie, T., and Tibshirani, R. The elements of statistical learning, vol. 1. Springer Series in Statistics, 2001.
[7]
Gaggioli, A., Pioggia, G., Tartarisco, G., Baldus, G., Corda, D., Cipresso, P., and Riva, G. A mobile data collection platform for mental health research. Personal Ubiquitous Computing 17, 2 (2013), 241--251.
[8]
Haapalainen, E., Kim, S., Forlizzi, J. F., and Dey, A. K. Psycho-physiological measures for assessing cognitive load. In Ubiquitous computing, Proceedings of the 12th ACM international conference on (2010), 301--310.
[9]
Haigney, M. C., Alam, S., Tebo, S., Marhefka, G., Elkashef, A., Kahn, R., Chiang, C., Vocci, F., and Cantilena, L. Intravenous cocaine and qt variability. Journal of cardiovascular electrophysiology 17,6 (2006), 610--616.
[10]
Hale, S. L., Lehmann, M. H., and Kloner, R. A. Electrocardiographic abnormalities after acute administration of cocaine in the rat. The American journal of cardiology 63, 20 (1989), 1529--1530.
[11]
Hong, J.-H., Ramos, J., and Dey, A. K. Understanding physiological responses to stressors during physical activity. In Ubiquitous Computing, Proceedings of the 2012 ACM Conference on (2012), 270--279.
[12]
Hu, S., Shao, Z., and Tan, J. A real-time cardiac arrhythmia classification system with wearable electrocardiogram. In Body Sensor Networks, Proceedings of the 2011 International Conference on (2011), 119--124.
[13]
Kolbrich, E., Barnes, A., Gorelick, D., Boyd, S., Cone, E., and Huestis, M. Major and minor metabolites of cocaine in human plasma following controlled subcutaneous cocaine administration. Journal of analytical toxicology 30, 8 (2006), 501--510.
[14]
Levin, K., Copersino, M., Epstein, D., Boyd, S., and Gorelick, D. Longitudinal ECG changes in cocaine users during extended abstinence. Drug Alcohol Depend 95, 1-2 (2008), 160--163.
[15]
Magnano, A., Talathoti, N., Hallur, R., Jurus, D., Dizon, J., Holleran, S., M., B. D., Collins, E., and Garan, H. Effect of acute cocaine administration on the QTc interval of habitual users. The American journal of cardiology 97, 8 (2006), 1244--1246.
[16]
Nocedal, J., and Wright, S. J. Numerical optimization. Springer verlag, 1999.
[17]
O'Brien, C. P. Evidence-based treatments of addiction. Focus 9, 1 (2011), 107.
[18]
Schwartz, A., Janzen, D., Jones, R., and Boyle, W. Electrocardiographic and hemodynamic effects of intravenous cocaine in awake and anesthetized dogs. Journal of electrocardiology 22, 2 (1989), 159--166.
[19]
Schwartz, B. G., Rezkalla, S., and Kloner, R. A. Cardiovascular effects of cocaine. Circulation 122, 24 (2010), 2558--2569.
[20]
Sughondhabirom, A., Jain, D., Gueorguieva, R., Coric, V., Berman, R., Lynch, W. J., Self, D., Jatlow, P., and Malison, R. T. A paradigm to investigate the self-regulation of cocaine administration in humans. Psychopharmacology 180, 3 (2005), 436--446.
[21]
Vongpatanasin, W., Taylor, A. J., and Victor, R. G. Effects of cocaine on heart rate variability in healthy subjects. The American journal of cardiology 93,3 (2004), 385--388.
[22]
Wikipedia. QT cinterval. http://en.wikipedia.org/wiki/QT_interval.
[23]
Zephyr. Bioharness 3. http://www.zephyr-technology.com/products/bioharness-3

Cited By

View all
  • (2024)Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive SensingAI10.3390/ai50401315:4(2725-2738)Online publication date: 3-Dec-2024
  • (2024)Analysis of behavioural curves to classify iris images under the influence of alcohol, drugs, and sleepiness conditionsExpert Systems with Applications10.1016/j.eswa.2023.122808242(122808)Online publication date: May-2024
  • (2023)Watch your watchProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620249(193-210)Online publication date: 9-Aug-2023
  • Show More Cited By

Index Terms

  1. Detecting cocaine use with wearable electrocardiogram sensors

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    UbiComp '13: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
    September 2013
    846 pages
    ISBN:9781450317702
    DOI:10.1145/2493432
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    In-Cooperation

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 08 September 2013

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tag

    1. medicine and science

    Qualifiers

    • Research-article

    Conference

    UbiComp '13
    Sponsor:

    Acceptance Rates

    UbiComp '13 Paper Acceptance Rate 92 of 394 submissions, 23%;
    Overall Acceptance Rate 764 of 2,912 submissions, 26%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)19
    • Downloads (Last 6 weeks)2
    Reflects downloads up to 20 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive SensingAI10.3390/ai50401315:4(2725-2738)Online publication date: 3-Dec-2024
    • (2024)Analysis of behavioural curves to classify iris images under the influence of alcohol, drugs, and sleepiness conditionsExpert Systems with Applications10.1016/j.eswa.2023.122808242(122808)Online publication date: May-2024
    • (2023)Watch your watchProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620249(193-210)Online publication date: 9-Aug-2023
    • (2023)Personalized Deep Learning using Passive Sensing and Ecological Momentary Assessments for Substance Users in Hawaii: A Research Protocol (Preprint)JMIR Research Protocols10.2196/46493Online publication date: 25-Aug-2023
    • (2023)Digital biomarker applications across the spectrum of opioid use disorderCogent Mental Health10.1080/28324765.2023.22403752:1Online publication date: 1-Aug-2023
    • (2023)Discriminating cocaine use from other sympathomimetics using wearable electrocardiographic (ECG) sensorsDrug and Alcohol Dependence10.1016/j.drugalcdep.2023.110898250(110898)Online publication date: Sep-2023
    • (2022)7th International Workshop on Mental Health and Well-being: Sensing and InterventionAdjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers10.1145/3544793.3560374(468-471)Online publication date: 11-Sep-2022
    • (2022)Systematic review: Wearable remote monitoring to detect nonalcohol/nonnicotine‐related substance use disorder symptomsThe American Journal on Addictions10.1111/ajad.1334131:6(535-545)Online publication date: 5-Sep-2022
    • (2022)An Overview of Wearable Biosensor Systems for Real-Time Substance Use DetectionIEEE Internet of Things Journal10.1109/JIOT.2022.32070909:23(23405-23415)Online publication date: 1-Dec-2022
    • (2021)An Examination of the Feasibility of Detecting Cocaine Use Using SmartwatchesFrontiers in Psychiatry10.3389/fpsyt.2021.67469112Online publication date: 24-Jun-2021
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media