Wearable Patch Based Estimation of Oxygen Uptake and Assessment of Clinical Status during Cardiopulmonary Exercise Testing in Patients with Heart Failure.

OBJECTIVE To estimate oxygen uptake (VO2) from cardiopulmonary exercise testing (CPX) using simultaneously recorded seismocardiogram (SCG) and electrocardiogram (ECG) signals captured with a small wearable patch. BACKGROUND CPX is an important risk stratification tool for patients with heart failure (HF) due to the prognostic value of the features derived from the gas exchange variables such as VO2. However, CPX requires specialized equipment, as well as trained professionals to conduct the study. METHODS We have conducted a total of 68 CPX tests on 59 subjects with HF with reduced ejection fraction (31% women, mean age 55±13 years, ejection fraction 0.27±0.11, 79% stage C). The subjects were fitted with a wearable sensing patch and underwent treadmill CPX. We divided the dataset into a training-testing (N=44) and a separate validation set (N=24). We developed globalized (population) regression models to estimate VO2 from the SCG and ECG signals measured continuously with the patch. We further classified the patients as stage D or C using the SCG and ECG features to assess the ability to detect clinical state from the wearable patch measurements alone. We developed the regression and classification model with cross-validation on the training-testing set and validated the models on the validation set. RESULTS The regression model to estimate VO2 from the wearable features yielded a moderate correlation (R2 of 0.64) with a root-mean-square-error (RMSE) of 2.51±1.12 ml.kg-1.min-1 on the training-testing set, whereas R2 and RMSE on the validation set were 0.76 and 2.28±0.93 ml.kg-1.min-1 respectively. Furthermore, the classification of clinical state yielded accuracy, sensitivity, specificity, and an area under the receiver operating characteristic curve values of 0.84, 0.91, 0.64, and 0.74 respectively for the training-testing set, and 0.83, 0.86, 0.67, and 0.92 respectively for the validation set. CONCLUSION Wearable SCG and ECG can assess CPX oxygen uptake and thereby classify clinical status for patients with HF. These methods may provide value in risk stratification of patients with HF by tracking cardiopulmonary parameters and clinical status outside of specialized settings, potentially allowing for more frequent assessments to be performed during longitudinal monitoring and treatment.

[1]  B. Blanchfield,et al.  Hospital-Level Care at Home for Acutely Ill Adults: a Pilot Randomized Controlled Trial , 2018, Journal of General Internal Medicine.

[2]  S. Steinhubl,et al.  Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial , 2018, JAMA.

[3]  R. Arena,et al.  2016 Focused Update: Clinical Recommendations for Cardiopulmonary Exercise Testing Data Assessment in Specific Patient Populations , 2016, Circulation.

[4]  M. Stone Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[5]  D. Kitzman,et al.  Determinants of exercise intolerance in patients with heart failure and reduced or preserved ejection fraction. , 2015, Journal of applied physiology.

[6]  Shuvo Roy,et al.  Using Ballistocardiography to Monitor Left Ventricular Function in Heart Failure Patients , 2016 .

[7]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[8]  P. Thompson,et al.  ACSM's Guidelines for Exercise Testing and Prescription , 1995 .

[9]  R. Arena,et al.  Cardiopulmonary Exercise Testing in Heart Failure. , 2015, Current problems in cardiology.

[10]  A. O. Bicen,et al.  Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients , 2018, Circulation. Heart failure.

[11]  M. Hannan,et al.  The 2016 International Society for Heart Lung Transplantation listing criteria for heart transplantation: A 10-year update. , 2016, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[12]  Kouhyar Tavakolian,et al.  Ballistocardiography and Seismocardiography: A Review of Recent Advances , 2015, IEEE Journal of Biomedical and Health Informatics.

[13]  Shuvo Roy,et al.  A Wearable Patch to Enable Long-Term Monitoring of Environmental, Activity and Hemodynamics Variables , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[14]  Pablo Laguna,et al.  Bioelectrical Signal Processing in Cardiac and Neurological Applications , 2005 .

[15]  Luc Vanhees,et al.  Clinical Recommendations for Cardiopulmonary Exercise Testing Data Assessment in Specific Patient Populations , 2012, Circulation.

[16]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[17]  P. Mahalanobis On the generalized distance in statistics , 1936 .

[18]  Ulrik Wisløff,et al.  Aerobic Capacity Reference Data in 3816 Healthy Men and Women 20–90 Years , 2013, PloS one.

[19]  R. Arena,et al.  focused update : clinical recommendations for cardiopulmonary exercise testing data assessment in specific patient populations , 2016 .

[20]  Rajeev Malhotra,et al.  Cardiopulmonary Exercise Testing in Heart Failure. , 2016, JACC. Heart failure.

[21]  Ross Arena,et al.  Clinician's Guide to cardiopulmonary exercise testing in adults: a scientific statement from the American Heart Association. , 2010, Circulation.

[22]  S. Bot,et al.  The relationship between heart rate and oxygen uptake during non-steady state exercise , 2000, Ergonomics.