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CardiacProfileR: An R package for extraction and visualisation of heart rate profiles from wearable fitness trackers

Djordje Djordjevic, Beni K. Cawood, Sabrina K. Rispin, Leo H. H. Yim, Christopher S. Hayward, Joshua W. K. Ho
doi: https://doi.org/10.1101/324004
Djordje Djordjevic
1Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia
2The University of New South Wales, Sydney, NSW 2010, Australia
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Beni K. Cawood
1Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia
2The University of New South Wales, Sydney, NSW 2010, Australia
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Sabrina K. Rispin
1Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia
2The University of New South Wales, Sydney, NSW 2010, Australia
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Leo H. H. Yim
1Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia
2The University of New South Wales, Sydney, NSW 2010, Australia
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Christopher S. Hayward
1Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia
2The University of New South Wales, Sydney, NSW 2010, Australia
3St Vincent’s Hospital, Sydney, NSW, Australia
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Joshua W. K. Ho
1Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia
2The University of New South Wales, Sydney, NSW 2010, Australia
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  • For correspondence: j.ho@victorchang.edu.au
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Abstract

A person’s heart rate profile, which consists of resting heart rate, increase of heart rate upon exercise and recovery of heart rate after exercise, is traditionally measured by electrocardiography during a controlled exercise stress test. A heart rate profile is a useful clinical tool to identify individuals at risk of sudden death and other cardiovascular conditions. Nonetheless, conducting such exercise stress tests routinely is often inconvenient and logistically challenging for patients. The widespread availability of affordable wearable fitness trackers, such as Fitbit and Apple Watch, provides an exciting new means to collect longitudinal heart rate and physical activity data. We reason that by combining the heart rate and physical activity data from these devices, we can construct a person’s heart rate profile. Here we present an open source R package CardiacProfileR for extraction, analysis and visualisation of heart rate dynamics during physical activities from data generated from common wearable heart rate monitors. This package represents a step towards quantitative deep phenotyping in humans. CardiacProfileR is available via an MIT license at https://github.com/VCCRI/CardiacProfileR.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 16, 2018.
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CardiacProfileR: An R package for extraction and visualisation of heart rate profiles from wearable fitness trackers
Djordje Djordjevic, Beni K. Cawood, Sabrina K. Rispin, Leo H. H. Yim, Christopher S. Hayward, Joshua W. K. Ho
bioRxiv 324004; doi: https://doi.org/10.1101/324004
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CardiacProfileR: An R package for extraction and visualisation of heart rate profiles from wearable fitness trackers
Djordje Djordjevic, Beni K. Cawood, Sabrina K. Rispin, Leo H. H. Yim, Christopher S. Hayward, Joshua W. K. Ho
bioRxiv 324004; doi: https://doi.org/10.1101/324004

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