Smartwatch-derived heart rate variability: a head-to-head comparison with the gold standard in cardiovascular disease

Abstract Aims We aimed to investigate the concordance between heart rate variability (HRV) derived from the photoplethysmographic (PPG) signal of a commercially available smartwatch compared with the gold-standard high-resolution electrocardiogram (ECG)-derived HRV in patients with cardiovascular disease. Methods and results We prospectively enrolled 104 survivors of acute ST-elevation myocardial infarction, 129 patients after an ischaemic stroke, and 30 controls. All subjects underwent simultaneous recording of a smartwatch (Garmin vivoactive 4; Garmin Ltd, Olathe, KS, USA)-derived PPG signal and a high-resolution (1000 Hz) ECG for 30 min under standardized conditions. HRV measures in time and frequency domain, non-linear measures, as well as deceleration capacity (DC) were calculated according to previously published technologies from both signals. Lin’s concordance correlation coefficient (ρc) between smartwatch-derived and ECG-based HRV markers was used as a measure of diagnostic accuracy. A very high concordance within the whole study cohort was observed for the mean heart rate (ρc = 0.9998), standard deviation of the averages of normal-to-normal (NN) intervals in all 5min segments (SDANN; ρc = 0.9617), and very low frequency power (VLF power; ρc = 0.9613). In contrast, detrended fluctuation analysis (DF-α1; ρc = 0.5919) and the square mean root of the sum of squares of adjacent NN-interval differences (rMSSD; ρc = 0.6617) showed only moderate concordance. Conclusion Smartwatch-derived HRV provides a practical alternative with excellent accuracy compared with ECG-based HRV for global markers and those characterizing lower frequency components. However, caution is warranted with HRV markers that predominantly assess short-term variability.

[1]  C. Sargent,et al.  A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults , 2022, Sensors.

[2]  Álvaro Alesanco Iglesias,et al.  Validation of the Apple Watch for Heart Rate Variability Measurements during Relax and Mental Stress in Healthy Subjects , 2018, Sensors.

[3]  Aldo Quattrone,et al.  Comparison between Electrocardiographic and Earlobe Pulse Photoplethysmographic Detection for Evaluating Heart Rate Variability in Healthy Subjects in Short- and Long-Term Recordings , 2018, Sensors.

[4]  Terry K Koo,et al.  A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. , 2016, Journal Chiropractic Medicine.

[5]  Guy Nagels,et al.  Heart Rate Variability and Baroreceptor Sensitivity in Acute Stroke: A Systematic Review , 2015, International journal of stroke : official journal of the International Stroke Society.

[6]  S. Cerutti,et al.  Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society. , 2015, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[7]  Wolfgang Hamm,et al.  Autonomic Nervous System Activity as Risk Predictor in the Medical Emergency Department: A Prospective Cohort Study , 2015, Critical care medicine.

[8]  Michael L. Jacobson,et al.  Time-frequency analysis of heart rate variability , 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

[9]  M. Malik,et al.  Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study , 2006, The Lancet.

[10]  H. Huikuri,et al.  Prediction of sudden cardiac death after acute myocardial infarction: role of Holter monitoring in the modern treatment era. , 2005, European heart journal.

[11]  W. Abraham,et al.  Continuous Autonomic Assessment in Patients With Symptomatic Heart Failure: Prognostic Value of Heart Rate Variability Measured by an Implanted Cardiac Resynchronization Device , 2004, Circulation.

[12]  H. Huikuri,et al.  Fractal analysis of heart rate variability and mortality after an acute myocardial infarction. , 2002, The American journal of cardiology.

[13]  R M Heethaar,et al.  Impaired autonomic function is associated with increased mortality, especially in subjects with diabetes, hypertension, or a history of cardiovascular disease: the Hoorn Study. , 2001, Diabetes care.

[14]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[15]  A. Camm,et al.  Heart-rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction , 1999, The Lancet.

[16]  T Seppänen,et al.  Abnormalities in beat-to-beat dynamics of heart rate before the spontaneous onset of life-threatening ventricular tachyarrhythmias in patients with prior myocardial infarction. , 1996, Circulation.

[17]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[18]  J. Miller,et al.  Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. , 1987, The American journal of cardiology.

[19]  F. Witkowski,et al.  Mechanisms controlling cardiac autonomic function and their relation to arrhythmogenesis , 1986 .

[20]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[21]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[22]  B Lown,et al.  Neural activity and ventricular fibrillation. , 1976, The New England journal of medicine.

[23]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.