Validity of the Empatica E4 Wristband to Measure Heart Rate Variability (HRV) Parameters: a Comparison to Electrocardiography (ECG)

Schuurmans, Angela A. T.; Looff, Peter de; Nijhof, Karin S.; Rosada, Catarina; Scholte, Ron H. J.; Popma, Arne; Otten, Roy

doi:10.1007/s10916-020-01648-w

Cited by 131 publications

(110 citation statements)

References 33 publications

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“…The E4 also includes a push-button interface that allows for data annotation. Previous research has assessed the validity of physiological signals recorded by an Empatica E4 device, such as EDA, HRV, and IBI, against the standard clinical ground truth [ 13 , 14 ]. Moreover, previous studies indicate that E4 is among the most commonly used physiological sensor devices in scientific research and validate its usefulness in detecting atrial fibrillation [ 15 ] and emotional arousal and stress [ 16 , 17 ].…”

Section: Methodsmentioning

confidence: 99%

Associations Between Physiological Signals Captured Using Wearable Sensors and Self-reported Outcomes Among Adults in Alcohol Use Disorder Recovery: Development and Usability Study

Alinia¹,

Sah²,

McDonell³

et al. 2021

JMIR Form Res

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Background Previous research has highlighted the role of stress in substance misuse and addiction, particularly for relapse risk. Mobile health interventions that incorporate real-time monitoring of physiological markers of stress offer promise for delivering tailored interventions to individuals during high-risk states of heightened stress to prevent alcohol relapse. Before such interventions can be developed, measurements of these processes in ambulatory, real-world settings are needed. Objective This research is a proof-of-concept study to establish the feasibility of using a wearable sensor device to continuously monitor stress in an ambulatory setting. Toward that end, we first aimed to examine the quality of 2 continuously monitored physiological signals—electrodermal activity (EDA) and heart rate variability (HRV)—and show that the data follow standard quality measures according to the literature. Next, we examined the associations between the statistical features extracted from the EDA and HRV signals and self-reported outcomes. Methods Participants (N=11; female: n=10) were asked to wear an Empatica E4 wearable sensor for continuous unobtrusive physiological signal collection for up to 14 days. During the same time frame, participants responded to a daily diary study using ecological momentary assessment of self-reported stress, emotions, alcohol-related cravings, pain, and discomfort via a web-based survey, which was conducted 4 times daily. Participants also participated in structured interviews throughout the study to assess daily alcohol use and to validate self-reported and physiological stress markers. In the analysis, we first used existing artifact detection methods and physiological signal processing approaches to assess the quality of the physiological data. Next, we examined the descriptive statistics for self-reported outcomes. Finally, we investigated the associations between the features of physiological signals and self-reported outcomes. Results We determined that 87.86% (1,032,265/1,174,898) of the EDA signals were clean. A comparison of the frequency of skin conductance responses per minute with previous research confirmed that the physiological signals collected in the ambulatory setting were successful. The results also indicated that the statistical features of the EDA and HRV measures were significantly correlated with the self-reported outcomes, including the number of stressful events marked on the sensor device, positive and negative emotions, and experienced pain and discomfort. Conclusions The results demonstrated that the physiological data collected via an Empatica E4 wearable sensor device were consistent with previous literature in terms of the quality of the data and that features of these physiological signals were significantly associated with several self-reported outcomes among a sample of adults diagnosed with alcohol use disorder. These results suggest that ambulatory assessment of stress is feasible and can be used to develop tailored mobile health interventions to enhance sustained recovery from alcohol use disorder.

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Section: Methodsmentioning

confidence: 99%

Associations Between Physiological Signals Captured Using Wearable Sensors and Self-reported Outcomes Among Adults in Alcohol Use Disorder Recovery: Development and Usability Study

Alinia¹,

Sah²,

McDonell³

et al. 2021

JMIR Form Res

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“…Arguably, the peak-to-peak interval observed in photoplethysmography can be interpreted as the equivalent of the R-R intervals of electrocardiography [ 112 ]. However, in terms of real-world application, this is only true under non-movement conditions as photoplethysmography recordings are extremely sensitive to motion artefacts, such as wrist movements [ 112 , 113 , 114 , 115 ]. For example, a recent study using a clinical-grade electrocardiogram as a benchmark examined several photoplethysmography-based consumer and research-grade wearables under different conditions [ 113 ].…”

Section: Digital Biomarkers Of Cognitive Fatigue Through Wearables and Machine Learningmentioning

confidence: 99%

Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

Lee

Gan

Christopoulos

2021

Sensors

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Cognitive fatigue is a psychological state characterised by feelings of tiredness and impaired cognitive functioning arising from high cognitive demands. This paper examines the recent research progress on the assessment of cognitive fatigue and provides informed recommendations for future research. Traditionally, cognitive fatigue is introspectively assessed through self-report or objectively inferred from a decline in behavioural performance. However, more recently, researchers have attempted to explore the biological underpinnings of cognitive fatigue to understand and measure this phenomenon. In particular, there is evidence indicating that the imbalance between sympathetic and parasympathetic nervous activity appears to be a physiological correlate of cognitive fatigue. This imbalance has been indexed through various heart rate variability indices that have also been proposed as putative biomarkers of cognitive fatigue. Moreover, in contrast to traditional inferential methods, there is also a growing research interest in using data-driven approaches to assessing cognitive fatigue. The ubiquity of wearables with the capability to collect large amounts of physiological data appears to be a major facilitator in the growth of data-driven research in this area. Preliminary findings indicate that such large datasets can be used to accurately predict cognitive fatigue through various machine learning approaches. Overall, the potential of combining domain-specific knowledge gained from biomarker research with machine learning approaches should be further explored to build more robust predictive models of cognitive fatigue.

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“…In a given time window, however, the proportion of missing samples could reach 57% at rest and 99% during a talk [25], due to the heartbeat selection algorithm embedded in E4. At the feature level, the mean heart rate over a given time window is estimated with high accuracy; but all features reflecting HRV show significant correlations [27] together with significant differences [26] with the ECG-based data.…”

Section: Advanced Cardiac Monitoring With Commercial Sensorsmentioning

confidence: 99%

Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications

Vila¹,

Godin²,

Charbonnier³

et al. 2021

Preprint

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Wearable cardiac sensors pave the way to advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to mo-tion artifacts that can be timely removed from the recordings. This leads to frequent data loss in the HR signal, especially for commercial devices based on photoplethysmography (PPG). The cur-rent study had two main goals: (i) to provide a white-box quality index that estimates the amount of missing samples in any piece of HR signal; and (ii) to quantify the impact of data loss on feature extraction in a PPG-based HR signal. This was done by comparing real-life recordings from com-mercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an out-lier rejection process, our quality index was used to isolate portions of ECG-based HR signal that could be used as benchmark, to validate the output of Empatica E4 at the signal level and at the feature level. Our results showed high accuracy for estimating the mean HR, poor accuracy for short-term HRV features and moderate accuracy for longer-term HRV features. Levels of error could be substantially reduced by using our quality index to identify time windows with few or no missing data.

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Validity of the Empatica E4 Wristband to Measure Heart Rate Variability (HRV) Parameters: a Comparison to Electrocardiography (ECG)

Cited by 131 publications

References 33 publications

Associations Between Physiological Signals Captured Using Wearable Sensors and Self-reported Outcomes Among Adults in Alcohol Use Disorder Recovery: Development and Usability Study

Associations Between Physiological Signals Captured Using Wearable Sensors and Self-reported Outcomes Among Adults in Alcohol Use Disorder Recovery: Development and Usability Study

Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications

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