@inproceedings{40a87a24bc1f46159d236927cac75726,
title = "Online Tool for Dynamical Heart Rate Variability Analysis",
abstract = "We present a straightforward yet versatile online tool for rapid heart rate variability (HRV) analysis utilizing state-of-the-art methods. The unique feature of the tool is the dynamical examination of accurate RR interval (RRI) correlations as a function of time. This is accomplished by the recently developed dynamical detrended fluctuation analysis, which determines local scaling exponents continuous in both time and scale. Therefore, the tool is particularly well-suited for studying HRV during highly non-stationary situations, such as physical exercise. To this end, the tool integrates with common sport watches. The tool reveals complex correlations in the RRIs during physical exercise. Different sports at various intensities leave distinct fingerprints in the dynamic correlations. Another key advantage of the temporal fidelity is the possibility to study transient alterations in the HRV that could be indicative of various cardiac diseases. We have implemented an accessible online tool for HRV analysis that is attractive to both researchers and enthusiasts alike. The tool could accelerate cardiac research by providing a quick visual overview of dynamic HRV measures, and permitting further custom analysis by downloadable raw data.",
author = "Matti Molkkari and Janne Solanp{\"a}{\"a} and Esa R{\"a}s{\"a}nen",
note = "JUFOID=72942; Computing in Cardiology, CinC ; Conference date: 13-09-2020 Through 16-09-2020",
year = "2020",
doi = "10.22489/CinC.2020.280",
language = "English",
isbn = "978-1-7281-1105-6",
series = "Computing in Cardiology",
publisher = "IEEE",
booktitle = "Computing in Cardiology, CinC 2020",
address = "United States",
}