To estimate oxygen uptake (VO) from cardiopulmonary exercise testing (CPX) using simultaneously recorded seismocardiogram (SCG) and electrocardiogram (ECG) signals captured with a small wearable patch.
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 VO. However, CPX requires specialized equipment, as well as trained professionals to conduct the study.
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 VO 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.
The regression model to estimate VO from the wearable features yielded a moderate correlation (R of 0.64) with a root-mean-square-error (RMSE) of 2.51±1.12 ml.kg.min on the training-testing set, whereas R and RMSE on the validation set were 0.76 and 2.28±0.93 ml.kg.min 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.
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.
Copyright © 2020. Published by Elsevier Inc.