Assessment of changes in blood volume during lower body negative pressure-induced hypovolemia using bioelectrical impedance analysis.

BACKGROUND: Lower body negative Pressure (LBNP)-induced hypovolemia is simulating acute hemorrhage by sequestrating blood into lower extremities. Bioelectrical Impedance Analysis (BIA) is based on the electrical properties of biological tissues, as electrical current flows along highly conductive body tissues (such as blood). Changes in blood volume will lead to changes in bioimpedance. This study aims to study changes in upper (UL) and lower (LL) extremities bioimpedance during LBNP-induced hypovolemia. METHODS: This was a prospective observational study of healthy volunteers who underwent gradual LBNP protocol which consisted of 3-minute intervals: at baseline, -15, -30, -45, -60 mmHg, then recovery phases at -30 mmHg and baseline. The UL&LL extremities bioimpedance were measured and recorded at each phase of LBNP and the percentage changes of bioimpedance from baseline were calculated and compared using student's t-test. A P-value of < 0.05 was considered significant. Correlation between relative changes in UL&LL bioimpedance and estimated blood loss (EBL) from LBNP was calculated using Pearson correlation. RESULTS: 26 healthy volunteers were enrolled. As LBNP-induced hypovolemia progressed, there were a significant increase in UL bioimpedance and a significant decrease in LL bioimpedance. During recovery phases (where blood was shifted from the legs to the body), there were a significant increase in LL bioimpedance and a reduction in UL bioimpedance. There were significant correlations between estimated blood loss from LBNP model with UL (R = 0.97) and LL bioimpedance (R = - 0.97). CONCLUSION: During LBNP-induced hypovolemia, there were reciprocal changes in UL&LL bioimpedance. These changes reflected hemodynamic compensatory mechanisms to hypovolemia.

Signal quality assessment of peripheral venous pressure.

Develop a signal quality index (SQI) for the widely available peripheral venous pressure waveform (PVP). We focus on the quality of the cardiac component in PVP. We model PVP by the adaptive non-harmonic model. When the cardiac component in PVP is stronger, the PVP is defined to have a higher quality. This signal quality is quantified by applying the synchrosqueezing transform to decompose the cardiac component out of PVP, and the SQI is defined as a value between 0 and 1. A database collected during the lower body negative pressure experiment is utilized to validate the developed SQI. All signals are labeled into categories of low and high qualities by experts. A support vector machine (SVM) learning model is trained for practical purpose. The developed signal quality index coincide with human experts' labels with the area under the curve 0.95. In a leave-one-subject-out cross validation (LOSOCV), the SQI achieves accuracy 0.89 and F1 0.88, which is consistently higher than other commonly used signal qualities, including entropy, power and mean venous pressure. The trained SVM model trained with SQI, entropy, power and mean venous pressure could achieve an accuracy 0.92 and F1 0.91 under LOSOCV. An exterior validation of SQI achieves accuracy 0.87 and F1 0.92; an exterior validation of the SVM model achieves accuracy 0.95 and F1 0.96. The developed SQI has a convincing potential to help identify high quality PVP segments for further hemodynamic study. This is the first work aiming to quantify the signal quality of the widely applied PVP waveform.