Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications
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Abstract
Wearable cardiac sensors pave the way for advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to motion artifacts that may lead to frequent data loss (missing samples in the HR signal), especially for commercial devices based on photoplethysmography (PPG). The current 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 commercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an outlier rejection process, our quality index was used to isolate portions of ECG-based HR signals 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 in estimating the mean HR (median error: 3.2%), poor accuracy for short-term HRV features (e.g., median error: 64% for high-frequency power), and mild accuracy for longer-term HRV features (e.g., median error: 25% for low-frequency power). These levels of errors could be reduced by using our quality index to identify time windows with few or no data loss (median errors: 0.0%, 27%, and 6.4% respectively, when no sample was missing). This quality index should be useful in future work to extract reliable cardiac features in real-life measurements, or to conduct a field validation study on wearable cardiac sensors.
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The Empatica E4 may be unsuited for monitoring everyday activities.
“The device has also been evaluated in general purpose real-world settings, with results indicating that the Empatica E4 may be unsuited for monitoring everyday activities70,71.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [70]
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Self-citationOlszewski, B., Uryga, A., Mataczyński, C., & Kasprowicz, M. (2026) · Physiological Measurement · doi:10.1088/1361-6579/ae95ae
- Authors
- Vila, G., Godin, C., Charbonnier, S., & Campagne, A.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Corresponding author
- Vila, Gaël
- Godin, Christelle
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-08-09
- Volume
- 21
- Issue
- 16
- Pages
- 5357
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8400129
- OpenAlex
- W3175050776
- Cited by
- 8
- References
- 41
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2022: 2 · 2021: 1
- FWCI
- 0.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- heart rate variability, photoplethysmography, motion artifacts, data loss, quality index, Empatica E4, outlier removal
- MeSH terms
- Electrocardiography, Heart Rate, Monitoring, Physiologic, Signal Processing, Computer-Assisted, Artifacts, Photoplethysmography
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