scholarly journals An Examination of the Feasibility of Detecting Cocaine Use Using Smartwatches

2021 ◽  
Vol 12 ◽  
Author(s):  
Emre Ertin ◽  
Nithin Sugavanam ◽  
August F. Holtyn ◽  
Kenzie L. Preston ◽  
Jeremiah W. Bertz ◽  
...  
Keyword(s):  
Heart Rate ◽  
Clinical Trials ◽  
Heart Rate Variability ◽  
Motion Artifacts ◽  
Physiological Data ◽  
Urine Drug Screen ◽  
Natural Field ◽  
Cocaine Use ◽  
Data Yield ◽  
Field Setting

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.

scholarly journals Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

2020 ◽  
Author(s):  
Sandya Subramanian ◽  
Patrick L. Purdon ◽  
Riccardo Barbieri ◽  
Emery N. Brown
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Prediction Models ◽  
Electrodermal Activity ◽  
Strong Relationship ◽  
Pulse Amplitude ◽  
Physiological Data ◽  
Statistical Tool ◽  
Autonomic Changes ◽  
Before And After

ABSTRACTDuring general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.

scholarly journals Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254053
Author(s):  
Sandya Subramanian ◽  
Patrick L. Purdon ◽  
Riccardo Barbieri ◽  
Emery N. Brown
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Prediction Models ◽  
Electrodermal Activity ◽  
Strong Relationship ◽  
Pulse Amplitude ◽  
Physiological Data ◽  
Statistical Tool ◽  
Autonomic Changes ◽  
Before And After

During general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.

Progress in Mental Workload Measurement

1986 ◽  
Vol 30 (11) ◽  
pp. 1121-1123 ◽  
Author(s):  
Neville Moray ◽  
Burhan Turksen ◽  
Paul Aidie ◽  
David Drascic ◽  
Paul Eisen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mental Workload ◽  
The Other ◽  
Physiological Data ◽  
New Techniques ◽  
Rule Based ◽  
Workload Measurement ◽  
On Line ◽  
Heart Rate Variability Spectrum

Two new techniques are described, one using subjective, the other physiological data for the measurement of workload in complex tasks. The subjective approach uses fuzzy measurement to analyse and predict the difficulty of combinations of skill based and rule based behaviour from the difficulty of skill based behaviour and rule based behaviour measured separately. The physiological technique offers an on-line real-time filter for measuring the Mulder signal at 0.1 Hz in the heart rate variability spectrum.

Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison to Electrocardiography: A Comprehensive Analysis in Time and Frequency Domains (Preprint)

2021 ◽  
Author(s):  
Rui Cao ◽  
Iman Azimi ◽  
Fatemeh Sarhaddi ◽  
Hannakaisa Niela-Vilen ◽  
Anna Axelin ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Correlation Coefficient ◽  
Confidence Intervals ◽  
Pearson Correlation ◽  
Error Rates ◽  
Motion Artifacts ◽  
Linear Regression Method ◽  
Pearson Correlation Coefficient ◽  
Positive Correlations

BACKGROUND Photoplethysmography (PPG) is a non-invasive and low-cost method to remotely and continuously track vital signs. The Oura ring is a compact PPG-based smart ring, which has recently drawn attention to be used in remote health monitoring and wellness applications. The ring is employed to acquire nocturnal heart rate (HR) and heart rate variability (HRV) parameters ubiquitously. However, these parameters are highly susceptible to motion artifacts and environmental noises. Therefore, the validity of the parameters should be assessed separately in everyday settings. OBJECTIVE We evaluate the accuracy of HR and time-domain and frequency-domain HRV parameters collected by the Oura ring against a medical-grade chest electrocardiogram (ECG) monitor. METHODS We conducted overnight home-based monitoring using an Oura ring and an ECG Shimmer device. The nocturnal HR and HRV of 35 healthy individuals were collected and assessed. We evaluated the parameters within two tests: i.e., values collected from five-minute recordings (i.e., short-term HRV analysis) and the average values per night sleep. A linear regression method, the Pearson correlation coefficient, and the Bland–Altman plot were also exploited to compare the measurements of two devices. RESULTS Our findings showed low mean biases of the HR and HRV parameters collected by the Oura ring in both the five-minute and average-per-night tests. In the five-minute test, the error variances of the parameters were nevertheless different. The parameters provided by the Oura ring dashboard (i.e., HR and RMSSD) showed relatively low error variance compared to the HRV parameters extracted from the IBI signals. The Pearson correlation coefficient tests (P < 0.0001) indicated that HR, RMSSD, AVNN, and pNN50 had high positive correlations with the baseline values, SDNN and HF had moderate positive correlations, and LF and LF/HF ratio had low positive correlations. The HR, RMSSD, AVNN, and pNN50 had narrow 95% confidence intervals; however, SDNN, LF, HF, and LF/HF had relatively wider 95% confidence intervals. In contrast, the average-per-night test showed that the HR, RMSSD, SDNN, AVNN, pNN50, LF, and HF had high positive relationships (P < 0.0001), and the LF/HF ratio has a moderate positive relationship (P < 0.0001). The average-per-night test also indicated considerably lower error variances than the 5-minute test for the parameters. CONCLUSIONS The Oura ring could accurately measure nocturnal HR and RMSSD in both the five-minute and average-per-night tests. It provided acceptable nocturnal AVNN, pNN50, HF, and SDNN accuracy in the average-per-night test but not in the five-minute test. On the other hand, the LF and LF/HF of the ring had high error rates in both the tests.

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