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Open AccessCommunication

Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts

1
Department of Computer Science, University of Pisa, 56126 Pisa, Italy
2
Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX1 2JD, UK
3
Huma Therapeutics Limited, London SW1P 4QP, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(24), 7122; https://doi.org/10.3390/s20247122
Received: 19 November 2020 / Revised: 9 December 2020 / Accepted: 10 December 2020 / Published: 11 December 2020
Application of ultra–short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people’s daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge problem of extracting these HRV parameters from wrist-worn devices is that their data are affected by the motion artifacts. For this reason, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable HRV parameters from these devices. To this aim, we simulate missing values induced by motion artifacts (from 0 to 70%) in an ultra-short time window (i.e., from 4 min to 30 s) by the random walk Gilbert burst model in 22 young healthy subjects. In addition, 30 s and 2 min ultra-short time windows are required to estimate rMSSD and SDNN, respectively. Moreover, due to the fact that ultra-short time window does not permit assessing very low frequencies, and the SDNN is highly affected by these frequencies, the bias for estimating SDNN continues to increase as the time window length decreases. On the contrary, a small error is detected in rMSSD up to 30 s due to the fact that it is highly affected by high frequencies which are possible to be evaluated even if the time window length decreases. Finally, the missing values have a small effect on rMSSD and SDNN estimation. As a matter of fact, the HRV parameter errors increase slightly as the percentage of missing values increase. View Full-Text
Keywords: SDNN; rMSSD; HRV; autonomic nervous system SDNN; rMSSD; HRV; autonomic nervous system
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MDPI and ACS Style

Rossi, A.; Pedreschi, D.; Clifton, D.A.; Morelli, D. Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts. Sensors 2020, 20, 7122. https://doi.org/10.3390/s20247122

AMA Style

Rossi A, Pedreschi D, Clifton DA, Morelli D. Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts. Sensors. 2020; 20(24):7122. https://doi.org/10.3390/s20247122

Chicago/Turabian Style

Rossi, Alessio; Pedreschi, Dino; Clifton, David A.; Morelli, Davide. 2020. "Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts" Sensors 20, no. 24: 7122. https://doi.org/10.3390/s20247122

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