UA cloudflare authentication

 

Computation of Cigarette Smoke Exposure Metrics From Breathing

Simple item page
dc.contributor.authorBelsare, Prajakta
dc.contributor.authorSenyurek, Volkan Yusuf
dc.contributor.authorImtiaz, Masudul H.
dc.contributor.authorTiffany, Stephen
dc.contributor.authorSazonov, Edward
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.contributor.otherState University of New York (SUNY) Buffalo
dc.date.accessioned2023-09-28T19:11:26Z
dc.date.available2023-09-28T19:11:26Z
dc.date.issued2020
dc.description.abstractTraditional metrics of smoke exposure in cigarette smokers are derived either from self-report, biomarkers, or puff topography. Methods involving biomarkers measure concentrations of nicotine, nicotine metabolites, or carbon monoxide. Puff-topography methods employ portable instruments to measure puff count, puff volume, puff duration, and inter-puff interval. In this article, we propose smoke exposure metrics calculated from the breathing signal and describe a novel algorithm for the computation of these metrics. The Personal Automatic Cigarette Tracker v2 (PACT-2) sensors, puff topography devices (CReSS), and video observation were used in a study of 38 moderate to heavy smokers in a controlled environment. Parameters of smoke inhalation including the start and end of each puff, inhale and exhale cycle, and smoke holding were computed from the breathing signal. From these, the traditional metrics of puff duration, inhale-exhale cycle duration, smoke holding duration, inter-puff interval, and novel Respiratory Smoke Exposure Metrics (RSEMs) such as inhale-exhale cycle volume, and inhale-exhale volume over time were calculated. The proposed RSEM algorithm to extract smoke exposure metrics named generated interclass correlations (ICCs) of 0.85 and 0.87 and Pearson's correlations of 0.97 and 0.77 with video observation and CReSS, respectively, for puff duration. Similarly, for the inhale-exhale duration, an ICC of 0.84 and Pearson's correlation of 0.81 was obtained with video observation. The RSEMs provided measures previously unavailable in research that are proportional to the depth and duration of smoke inhalation. The results suggest that the breathing signal may be used to compute smoke exposure metrics.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationBelsare, P., Senyurek, V. Y., Imtiaz, M. H., Tiffany, S., & Sazonov, E. (2020). Computation of Cigarette Smoke Exposure Metrics From Breathing. In IEEE Transactions on Biomedical Engineering (Vol. 67, Issue 8, pp. 2309–2316). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tbme.2019.2958843
dc.identifier.doi10.1109/TBME.2019.2958843
dc.identifier.orcidhttps://orcid.org/0000-0003-4446-4977
dc.identifier.orcidhttps://orcid.org/0000-0002-9132-6633
dc.identifier.orcidhttps://orcid.org/0000-0001-5528-482X
dc.identifier.orcidhttps://orcid.org/0000-0001-7792-4234
dc.identifier.urihttps://ir.ua.edu/handle/123456789/10981
dc.languageEnglish
dc.language.isoen_US
dc.publisherIEEE
dc.subjectCalibration
dc.subjectSurfaces
dc.subjectBiomarkers
dc.subjectWearable sensors
dc.subjectBreathing signal analysis
dc.subjectcigarette smoking
dc.subjectrespiratory inductive plethysmography
dc.subjectsmoke exposure
dc.subjectsmoking topography
dc.subjectRESPIRATORY INDUCTIVE PLETHYSMOGRAPHY
dc.subjectTOBACCO
dc.subjectBIOMARKERS
dc.subjectVALIDITY
dc.subjectBEHAVIOR
dc.subjectEngineering, Biomedical
dc.titleComputation of Cigarette Smoke Exposure Metrics From Breathingen_US
dc.typeArticle
dc.typetext

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
10.1109TBME.2019.2958843.pdf
Size:
1.1 MB
Format:
Adobe Portable Document Format
Download

Collections

Research and Publications - Department of Chemical & Biological Engineering