Department of ECE, Georgia Institute of Technology, Atlanta, Georgia.
Department of ECE, Georgia Institute of Technology, Atlanta, Georgia.
J Card Fail. 2020 Nov;26(11):948-958. doi: 10.1016/j.cardfail.2020.05.014. Epub 2020 May 27.
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) owing 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 patients with HF with reduced ejection fraction (31% women, mean age 55 ± 13 years, ejection fraction 0.27 ± 0.11, 79% stage C). The patients were fitted with a wearable sensing patch and underwent treadmill CPX. We divided the dataset into a training-testing set (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 of 2.51 ± 1.12 mL · kg · min on the training-testing set, whereas R and root mean square error 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 VO and thereby classify clinical status for patients with HF. These methods may provide value in the 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.
使用小型可穿戴贴片同时记录地震心动图（SCG）和心电图（ECG）信号，从心肺运动测试（CPX）中估算摄氧量（VO）。CPX 是心力衰竭（HF）患者进行危险分层的重要工具，因为从气体交换变量（如 VO）中得出的特征具有预后价值。然而，CPX 需要专门的设备和经过培训的专业人员来进行研究。
我们对 59 名射血分数降低的心力衰竭（HF）患者（31%为女性，平均年龄 55±13 岁，射血分数 0.27±0.11，79%为 C 期）进行了总共 68 次 CPX 测试。患者佩戴可穿戴感测贴片并进行跑步机 CPX。我们将数据集分为训练-测试集（n=44）和单独的验证集（n=24）。我们开发了全局化（人群）回归模型，从贴片连续测量的 SCG 和 ECG 信号中估算 VO。我们进一步使用 SCG 和 ECG 特征将患者分类为 D 期或 C 期，以评估仅从可穿戴贴片测量来检测临床状态的能力。我们在训练-测试集上使用交叉验证开发了回归和分类模型，并在验证集上验证了模型。用于从可穿戴设备特征中估算 VO 的回归模型在训练-测试集上的相关性适中（R 值为 0.64），均方根误差为 2.51±1.12 mL·kg·min，而在验证集上的 R 值和均方根误差分别为 0.76 和 2.28±0.93 mL·kg·min。此外，对于训练-测试集，临床状态的分类分别产生了 0.84、0.91、0.64 和 0.74 的准确性、灵敏度、特异性和受试者工作特征曲线下面积值，对于验证集，分别产生了 0.83、0.86、0.67 和 0.92 的准确性、灵敏度、特异性和受试者工作特征曲线下面积值。
可穿戴式 SCG 和 ECG 可评估 CPX 的 VO，并据此对 HF 患者进行临床状态分类。这些方法通过在专门设置之外跟踪心肺参数和临床状态，可能在 HF 患者的风险分层中具有价值，从而可以在纵向监测和治疗期间进行更频繁的评估。
