Evaluation of transit time-based models in wearable central aortic blood pressure estimation

Evidence suggests that central aortic blood pressure (CABP) may provide a more accurate prognosis of cardiovascular events than peripheral pressure. The capability of monitoring CABP in a continuous, wearable, unobtrusive way might have a significant impact on hypertension management. The purpose of this study is to experimentally explore whether a wearable device equipped with an electrocardiogram (ECG) and ballistocardiogram (BCG) acquisition system could be used to predict CABP. This is based on state-of-the-art results on the relationship between transit time extracted from these signals and CABP. Ten young, healthy volunteers participated in the study where data-sets were acquired during three hemodynamic interventions, i.e., breath-holding, Valsalva maneuver, and cold pressor. Each data-set included ECG and BCG waveforms acquired by the wearable device and a CABP assessment from a cuff-based device. A total of nine PTT-based models (PBMs) derived from pulse transit time methodology were considered. Each PBM was tested with three alternative feature times extracted from the recorded waveforms PBMs were calibrated with data-sets acquired at baseline state, which were not considered for testing the PBM estimation performance. Four of the nine tested models presented a proper agreement in estimating CABP through the acquired signals, after the calibration procedure with baseline-state data. Results in one of these promising models are the following. Mean estimation error (95% confidence interval), systolic: 0 to 1.7 mmHg, diastolic: 0.4 to 2.3 mmHg, Pearson correlation: 0.82 systolic and 0.78 diastolic (p < 0.001). The proposed methodology may lead to continuous wearable BP monitoring.