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

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 16; p. 5357
• Main Authors: Vila, Gaël, Godin, Christelle, Charbonnier, Sylvie, Campagne, Aurélie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 09-08-2021; MDPI
• Series: Special Issue "Smart Wearables for Cardiac Monitoring"
• Subjects: Accuracy; Algorithms; Bioengineering; Blood; Cardiology and cardiovascular system; Computer Science; Control data (computers); Data integrity; Data loss; Electrocardiography; Errors; Heart rate; heart rate variability; Human health and pathology; Laboratories; Life Sciences; Monitoring; Outliers (statistics); photoplethysmography; Physiology; real-life measurements; Sensors; Signal and Image Processing; signal quality; Support vector machines; Telemedicine; Textiles; wearable cardiac sensors; Wearable technology; signal quality; wearable cardiac sensors; photoplethysmography; heart rate variability; real-life measurements; electrocardiography
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21165357

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.