Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome

DSpace/Manakin Repository

Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome

Citable link to this page

 

 
Title: Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome
Author: Liu, Yun; Scirica, Benjamin M.; Stultz, Collin M.; Guttag, John V.

Note: Order does not necessarily reflect citation order of authors.

Citation: Liu, Yun, Benjamin M. Scirica, Collin M. Stultz, and John V. Guttag. 2016. “Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome.” Scientific Reports 6 (1): 34540. doi:10.1038/srep34540. http://dx.doi.org/10.1038/srep34540.
Full Text & Related Files:
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.
Published Version: doi:10.1038/srep34540
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5052591/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:29408247
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters