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Discriminating cocaine use from other sympathomimetics using wearable electrocardiographic (ECG) sensors.
Angarita, Gustavo A; Pittman, Brian; Nararajan, Annamalai; Mayerson, Talia F; Parate, Abhinav; Marlin, Benjamin; Gueorguieva, Ralitza R; Potenza, Marc N; Ganesan, Deepak; Malison, Robert T.
Afiliación
  • Angarita GA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Clinical Neuroscience Research Unit, Connecticut Mental Health Center, 34 Park Street, New Haven, CT06519, USA; Connecticut Mental Health Center, New Haven, CT06519, USA; Department of Radiology and Biomedical Ima
  • Pittman B; Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA.
  • Nararajan A; Philips Research North America, Cambridge, MA02141, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA.
  • Mayerson TF; Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Clinical Neuroscience Research Unit, Connecticut Mental Health Center, 34 Park Street, New Haven, CT06519, USA; Connecticut Mental Health Center, New Haven, CT06519, USA; Department of Radiology and Biomedical Ima
  • Parate A; Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA01003, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA; Lumme Health Inc, Boston, MA02210, USA.
  • Marlin B; Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA01003, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA.
  • Gueorguieva RR; Department of Biostatistics, Yale University School of Public Health, New Haven, CT06510, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA.
  • Potenza MN; Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Connecticut Mental Health Center, New Haven, CT06519, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA; Child Study Center, Yale University School of
  • Ganesan D; Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA01003, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA.
  • Malison RT; Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Clinical Neuroscience Research Unit, Connecticut Mental Health Center, 34 Park Street, New Haven, CT06519, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT0651
Drug Alcohol Depend ; 250: 110898, 2023 09 01.
Article en En | MEDLINE | ID: mdl-37523916
ABSTRACT

BACKGROUND:

Our group has established the feasibility of using on-body electrocardiographic (ECG) sensors to detect cocaine use in the human laboratory. The purpose of the current study was to test whether ECG sensors and features are capable of discriminating cocaine use from other non-cocaine sympathomimetics.

METHODS:

Eleven subjects with cocaine use disorder wore the Zephyr BioHarness™ 3 chest band under six experimental (drug and non-drug) conditions, including 1) laboratory, intravenous cocaine self-administration, 2) after a single oral dose of methylphenidate, 3) during aerobic exercise, 4) during tobacco use (N=7 who smoked tobacco), and 5) during routine activities of daily inpatient living (unit activity). Three ECG-derived feature sets served as primary outcome measures, including 1) the RR interval (i.e., heart rate), 2) a group of ECG interval proxies (i.e., PR, QS, QT and QTc intervals), and 3) the full ECG waveform. Discriminatory power between cocaine and non-cocaine conditions for each of the three outcomes measures was expressed as the area under the receiver operating characteristics (AUROC) curve.

RESULTS:

All three outcomes successfully discriminated cocaine use from unit activity, exercise, tobacco, and methylphenidate conditions with a mean AUROC values ranging from 0.66 to 0.99 and with least squares means values all statistically different/higher than 0.5 among all subjects [F(3, 99) = 3.38, p =0.02] and among those with tobacco use [F(4, 84) = 5.39, p = 0.0007].

CONCLUSIONS:

These preliminary results support discriminatory power of wearable ECG sensors for detecting cocaine use.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Cocaína / Trastornos Relacionados con Cocaína / Dispositivos Electrónicos Vestibles / Metilfenidato Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Drug Alcohol Depend Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Cocaína / Trastornos Relacionados con Cocaína / Dispositivos Electrónicos Vestibles / Metilfenidato Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Drug Alcohol Depend Año: 2023 Tipo del documento: Article