Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome
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Beatquency domain.pdf
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Author(s)
Scirica, Benjamin M.
Liu, Yun
Stultz, Collin M
Guttag, John V
Date Issued
October 2016
Journal
Scientific Reports
Publisher
Nature Publishing Group
Citation
Liu, Yun, Benjamin M. Scirica, Collin M. Stultz, and John V. Guttag. “Beatquency Domain and Machine Learning Improve Prediction of Cardiovascular Death after Acute Coronary Syndrome.” Scientific Reports 6, no. 1 (October 6, 2016).
Version
Final published version
Abstract
Frequency domain measures of heart rate variability (HRV) are associated with adverse events after a myocardial infarction. However, patterns in the traditional frequency domain (measured in Hz, or cycles per second) may capture different cardiac phenomena at different heart rates. An alternative is to consider frequency with respect to heartbeats, or beatquency. We compared the use of frequency and beatquency domains to predict patient risk after an acute coronary syndrome. We then determined whether machine learning could further improve the predictive performance. We first evaluated the use of pre-defined frequency and beatquency bands in a clinical trial dataset (N = 2302) for the HRV risk measure LF/HF (the ratio of low frequency to high frequency power). Relative to frequency, beatquency improved the ability of LF/HF to predict cardiovascular death within one year (Area Under the Curve, or AUC, of 0.730 vs. 0.704, p < 0.001). Next, we used machine learning to learn frequency and beatquency bands with optimal predictive power, which further improved the AUC for beatquency to 0.753 (p < 0.001), but not for frequency. Results in additional validation datasets (N = 2255 and N = 765) were similar. Our results suggest that beatquency and machine learning provide valuable tools in physiological studies of HRV.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.1038/srep34540
