A Probabilistic Approach for Heart Rate Variability Analysis Using Explicit Duration Hidden Markov Models

Gao, Ju; Teng, Diyan; Ertin, Emre

doi:10.1109/ssp.2018.8450781

Cited by 1 publication

(1 citation statement)

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“…In addition, the cocaine-use detection algorithm was modified for use on data collected by the wrist-worn devices by preprocessing PPG signal to generate likely hypotheses for peak locations. Specifically, a probabilistic labeling algorithm developed previously by authors was used ( 17 , 18 ) to generate likely sequences of heartbeats consistent with sensor data. These labels in turn were used to generate heart rate and heart rate variability measure features used in the cocaine detection algorithm ( 5 ).…”

Section: Methodsmentioning

confidence: 99%

An Examination of the Feasibility of Detecting Cocaine Use Using Smartwatches

et al. 2021

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As digital technology increasingly informs clinical trials, novel ways to collect study data in the natural field setting have the potential to enhance the richness of research data. Cocaine use in clinical trials is usually collected via self-report and/or urine drug screen results, both of which have limitations. This article examines the feasibility of developing a wrist-worn device that can detect sufficient physiological data (i.e., heart rate and heart rate variability) to detect cocaine use. This study aimed to develop a wrist-worn device that can be used in the natural field setting among people who use cocaine to collect reliable data (determined by data yield, device wearability, and data quality) that is less obtrusive than chest-based devices used in prior research. The study also aimed to further develop a cocaine use detection algorithm used in previous research with an electrocardiogram on a chestband by adapting it to a photoplethysmography sensor on the wrist-worn device which is more prone to motion artifacts. Results indicate that wrist-based heart rate data collection is feasible and can provide higher data yield than chest-based sensors, as wrist-based devices were also more comfortable and affected participants' daily lives less often than chest-based sensors. When properly worn, wrist-based sensors produced similar quality of heart rate and heart rate variability features to chest-based sensors and matched their performance in automated detection of cocaine use events.Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT02915341.

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Section: Methodsmentioning

confidence: 99%