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SDNN24 Estimation from Semi-Continuous HR Measures

Huma Therapeutics Limited, London SW1P 4QP, UK
Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX1 2JD, UK
Department of Computer Science, University of Pisa, 56126 Pisa, Italy
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Massimo Sacchetti and Antti Vehkaoja
Sensors 2021, 21(4), 1463;
Received: 7 January 2021 / Revised: 12 February 2021 / Accepted: 15 February 2021 / Published: 20 February 2021
The standard deviation of the interval between QRS complexes recorded over 24 h (SDNN24) is an important metric of cardiovascular health. Wrist-worn fitness wearable devices record heart beats 24/7 having a complete overview of users’ heart status. Due to motion artefacts affecting QRS complexes recording, and the different nature of the heart rate sensor used on wearable devices compared to ECG, traditionally used to compute SDNN24, the estimation of this important Heart Rate Variability (HRV) metric has never been performed from wearable data. We propose an innovative approach to estimate SDNN24 only exploiting the Heart Rate (HR) that is normally available on wearable fitness trackers and less affected by data noise. The standard deviation of inter-beats intervals (SDNN24) and the standard deviation of the Average inter-beats intervals (ANN) derived from the HR (obtained in a time window with defined duration, i.e., 1, 5, 10, 30 and 60 min), i.e., ANN=60HR (SDANNHR24), were calculated over 24 h. Power spectrum analysis using the Lomb-Scargle Peridogram was performed to assess frequency domain HRV parameters (Ultra Low Frequency, Very Low Frequency, Low Frequency, and High Frequency). Due to the fact that SDNN24 reflects the total power of the power of the HRV spectrum, the values estimated from HR measures (SDANNHR24) underestimate the real values because of the high frequencies that are missing. Subjects with low and high cardiovascular risk show different power spectra. In particular, differences are detected in Ultra Low and Very Low frequencies, while similar results are shown in Low and High frequencies. For this reason, we found that HR measures contain enough information to discriminate cardiovascular risk. Semi-continuous measures of HR throughout 24 h, as measured by most wrist-worn fitness wearable devices, should be sufficient to estimate SDNN24 and cardiovascular risk. View Full-Text
Keywords: SDNN; HRV; HR; Logistic Regression; neural network; cardiovascular risk SDNN; HRV; HR; Logistic Regression; neural network; cardiovascular risk
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MDPI and ACS Style

Morelli, D.; Rossi, A.; Bartoloni, L.; Cairo, M.; Clifton, D.A. SDNN24 Estimation from Semi-Continuous HR Measures. Sensors 2021, 21, 1463.

AMA Style

Morelli D, Rossi A, Bartoloni L, Cairo M, Clifton DA. SDNN24 Estimation from Semi-Continuous HR Measures. Sensors. 2021; 21(4):1463.

Chicago/Turabian Style

Morelli, Davide, Alessio Rossi, Leonardo Bartoloni, Massimo Cairo, and David A. Clifton 2021. "SDNN24 Estimation from Semi-Continuous HR Measures" Sensors 21, no. 4: 1463.

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