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SDNN24 Estimation from Semi-Continuous HR Measures

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 = 60/HR (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.

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Additional Info
Field Value
Accessibility Both
AccessibilityMode Download
Area Health Science
Availability On-Line
Basic rights Download
CreationDate 2021-02-20
Creator Alessio, Rossi, alessio.rossi2@gmail.com, orcid.org/0000-0002-6400-5914
External Identifier https://www.mdpi.com/1424-8220/21/4/1463
Field/Scope of use Any use
Group Others
Group Health Studies
Owner Alessio, Rossi, alessio.rossi2@gmail.com, orcid.org/0000-0002-6400-5914
ProgrammingLanguage Python
Sublicense rights No
Territory of use World Wide
Thematic Cluster Other
system:type Method
Management Info
Field Value
Author Rossi Alessio
Maintainer Rossi Alessio
Version 1
Last Updated 12 September 2023, 09:17 (CEST)
Created 8 April 2021, 10:05 (CEST)