Coronary artery disease diagnosis by means of heart rate variability analysis using respiratory information
Heart rate variability (HRV) analysis during exercise has been used to evaluate cardiovascular response to the stress of exercise, which may offer additional value than in rest condition. To properly analyze HRV during exercise, several challenges need to be addressed, such as including respiratory information and removing the dependance with the mean heart rate (HR) level. The objective of this work is to extract parameters from HRV analysis and respiratory information during exercise to evaluate their capability of diagnose coronary artery disease (CAD). Significant differences in mean HR were found due to medication effect in patients with CAD. By correcting the HRV parameters by mean HR, this effect is minimized. Power related to high frequency, when guided by respiration, results to have the best diagnosis capability (AUC > 0.7).
KeywordsExercise test respiratory rate CAD diagnosis
Unable to display preview. Download preview PDF.
- 1.Working group of ESC. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J. 1996;17:354–381.Google Scholar
- 2.Wennerblom B., Lurje L., Tygesen H., Vahisalo R., Hjalmarson A. Patients with uncomplicated coronary artery disease have reduced heart rate variability mainly affecting vagal tone. Heart. 2000;83(3):290–294.Google Scholar
- 3.Bailón R., Serrano P., Laguna P. Influence of time-varying mean heart rate in coronary artery disease diagnostic performance of heart rate variability indices from exercise stress testing. J Electrocardiol. 2011;44:445–452.Google Scholar
- 4.Virtanen M., Kähönen M., Nieminen T., et al. Heart rate variability derived from exercise ECG in the detection of coronary artery disease. Physiol Meas. 2007;28(10):1189-1-200.Google Scholar
- 5.Lázaro J., Alcaine A., Romero D., et al. Electrocardiogram Derived Respiratory Rate from QRS Slopes and R-wave Angle. Ann Biomed Eng. 2014;40(10):2072–2083.Google Scholar
- 6.Nieminen T., Lehtinen R., Viik J., et al. The Finnish Cardiovascular Study (FINCAVAS): characterising patients with high risk of cardiovascular morbidity and mortality. BMC Cardiovasc Disord. 2006;6:9.Google Scholar
- 7.Hernando D., Bailón R., Almeida R., Hernández A. QRS Detection Optimization in Stress Test Recordings using Evolutionary Algorithms. XLI International Conference on Computing in Cardiology. 2014:737–740.Google Scholar
- 8.Bailón R., Laouini G., Grao C., Orini M., Laguna P., Meste O. The integral pulse frequency modulation with time-varying threshold: Application to heart rate variability analysis during exercise stress testing. IEEE Trans Biomed Eng. 2011;58(3):642–652.Google Scholar
- 9.Mateo J., Laguna P. Analysis of Heart Rate Variability in the Presence of Ectopic Beats Using the Heart Timing Signal. IEEE Trans Biomed Eng. 2003;50(3):334–343.Google Scholar
- 10.Bailón R., Garatachea N., I. de la Iglesia, JA. Casajús, Laguna P. Influence of running stride frequency in heart rate variability analysis during treadmill exercise testing. IEEE Trans Biomed Eng. 2013;60(7):1796–1805.Google Scholar
- 11.Bigger Jr JT., Fleiss JL., Steinman RC., Rolnitzky LM., Schneider WJ., Stein PK. RR variability in healthy, middle-aged persons compared with patients with chronic coronary heart disease or recent acute myocardial infarction. Circulation. 1995;91(7):1936–1943.Google Scholar
- 12.Kardelen F., Akçurin G., Ertuǧ H., Akcurin S., Bircan I. Heart rate variability and circadian variations in type 1 diabetes mellitus. Pediatr Diabetes. 2006;7(1):45–50.Google Scholar