An open source benchmarked toolbox for cardiovascular waveform and interval analysis

• Type of Material: Article
• Authors: Adriana N. Vest, Emory University; Giulia Da Poian, Emory University; Qiao Li, Emory University; Chengyu Liu, Emory University; Shamim Nemati, Emory University; Amit Shah, Emory University; Gari Clifford, Emory University
• Language: English
• Date: 2018-10-01
• Publisher: IOP PUBLISHING LTD
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2018 Institute of Physics and Engineering in Medicine.
• Final Published Version (URL): http://dx.doi.org/10.1088/1361-6579/aae021
• Title of Journal or Parent Work: PHYSIOLOGICAL MEASUREMENT
• Volume: 39
• Issue: 10
• Start Page: 105004
• End Page: 105004
• Grant/Funding Information: National Institutes of Health (Grant # NIH K23 HL127251, R01 HL136205) the National Science Foundation Award 1636933, the Fogarty International Center and the Eunice Kennedy Shriver National Institute of Child Health and Human Development, grant number 1R21HD084114–01, the Rett Syndrome Research Trust, and the One Mind Foundation.
• Keywords: Technology; RATE TURBULENCE; SYMPATHOVAGAL BALANCE; heart rate variability; SIGNAL QUALITY INDEXES; RATE MULTISCALE ENTROPY; PHYSIOLOGICAL INTERPRETATION; Engineering; TRAUMA PATIENTS; toolbox validation; Biophysics; ACUTE MYOCARDIAL-INFARCTION; Engineering, Biomedical; TIME-SERIES ANALYSIS; SPECTRAL-ANALYSIS; Life Sciences & Biomedicine; Science & Technology; peak detection; physiological signal processing; Physiology; HEART-RATE-VARIABILITY
• Research Categories: Psychology, Psychobiology; Biophysics, Medical

Abstract

Objective: This work aims to validate a set of data processing methods for variability metrics, which hold promise as potential indicators for autonomic function, prediction of adverse cardiovascular outcomes, psychophysiological status, and general wellness. Although the investigation of heart rate variability (HRV) has been prevalent for several decades, the methods used for preprocessing, windowing, and choosing appropriate parameters lacks consensus among academic and clinical investigators. Moreover, many of the important steps are omitted from publications, preventing reproducibility. Approach: To address this, we have compiled a comprehensive and open-source modular toolbox for calculating HRV metrics and other related variability indices, on both raw cardiovascular time series and RR intervals. The software, known as the PhysioNet Cardiovascular Signal Toolbox, is implemented in the MATLAB programming language, with standard (open) input and output formats, and requires no external libraries. The functioning of our software is compared with other widely used and referenced HRV toolboxes to identify important differences. Main results: Our findings demonstrate how modest differences in the approach to HRV analysis can lead to divergent results, a factor that might have contributed to the lack of repeatability of studies and clinical applicability of HRV metrics. Significance: Existing HRV toolboxes do not include standardized preprocessing, signal quality indices (for noisy segment removal), and abnormal rhythm detection and are therefore likely to lead to significant errors in the presence of moderate to high noise or arrhythmias. We therefore describe the inclusion of validated tools to address these issues. We also make recommendations for default values and testing/reporting.