title: Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome.
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.
authors.affiliation: Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology (MIT) and Harvard-MIT Division of Health Sciences and Technology, 77 Massachusetts Ave. Cambridge MA 02139, USA.
authors.forename: Yun
authors.initials: Y
authors.lastname: Liu
authors.affiliation: TIMI Study Group, Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, 75 Francis St., Boston Ma. 02115, USA.
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Scirica
authors.affiliation: Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology (MIT) and Harvard-MIT Division of Health Sciences and Technology, 77 Massachusetts Ave. Cambridge MA 02139, USA.|Department of Electrical Engineering and Computer Science, MIT, 77 Massachusetts Ave. Cambridge MA 02139, USA.
authors.forename: Collin M
authors.initials: CM
authors.lastname: Stultz
authors.affiliation: Department of Electrical Engineering and Computer Science, MIT, 77 Massachusetts Ave. Cambridge MA 02139, USA.|Computer Science and Artificial Intelligence Laboratory, MIT, 77 Massachusetts Ave. Cambridge MA 02139, USA.
authors.forename: John V
authors.initials: JV
authors.lastname: Guttag
mesh_terms: D054058:Acute Coronary Syndrome / Q000150:complications* / Q000503:physiopathology*; D003643:Death; D004569:Electroencephalography; D006801:Humans; D000069550:Machine Learning; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted