Pulse Decomposition Analysis of the digital arterial pulse during hemorrhage simulation

Abstract

BackgroundMarkers of temporal changes in central blood volume are required to non-invasively detect hemorrhage and the onset of hemorrhagic shock. Recent work suggests that pulse pressure may be such a marker. A new approach to tracking blood pressure, and pulse pressure specifically is presented that is based on a new form of pulse pressure wave analysis called Pulse Decomposition Analysis (PDA). The premise of the PDA model is that the peripheral arterial pressure pulse is a superposition of five individual component pressure pulses, the first of which is due to the left ventricular ejection from the heart while the remaining component pressure pulses are reflections and re-reflections that originate from only two reflection sites within the central arteries. The hypothesis examined here is that the PDA parameter T13, the timing delay between the first and third component pulses, correlates with pulse pressure.T13 was monitored along with blood pressure, as determined by an automatic cuff and another continuous blood pressure monitor, during the course of lower body negative pressure (LBNP) sessions involving four stages, -15 mmHg, -30 mmHg, -45 mmHg, and -60 mmHg, in fifteen subjects (average age: 24.4 years, SD: 3.0 years; average height: 168.6 cm, SD: 8.0 cm; average weight: 64.0 kg, SD: 9.1 kg).ResultsStatistically significant correlations between T13 and pulse pressure as well as the ability of T13 to resolve the effects of different LBNP stages were established. Experimental T13 values were compared with predictions of the PDA model. These interventions resulted in pulse pressure changes of up to 7.8 mmHg (SE = 3.49 mmHg) as determined by the automatic cuff. Corresponding changes in T13 were a shortening by -72 milliseconds (SE = 4.17 milliseconds). In contrast to the other two methodologies, T13 was able to resolve the effects of the two least negative pressure stages with significance set at p < 0.01.ConclusionsThe agreement of observations and measurements provides a preliminary validation of the PDA model regarding the origin of the arterial pressure pulse reflections. The proposed physical picture of the PDA model is attractive because it identifies the contributions of distinct reflecting arterial tree components to the peripheral pressure pulse envelope. Since the importance of arterial pressure reflections to cardiovascular health is well known, the PDA pulse analysis could provide, beyond the tracking of blood pressure, an assessment tool of those reflections as well as the health of the sites that give rise to them.

In-ear photoplethysmography for central pulse waveform analysis in non-invasive hemodynamic monitoring

“…The second pulse is thought of being a reflection at the junction between the thoracic and abdominal aorta, as there is a significant decrease in the artery diameter. Similarly, the change in diameter at the juncture between the abdominal aorta and the iliac arteries is regarded to be responsible for the third pulse wave [1] In order to find the unknown model parameters we needed to solve the following optimization problem:…”

“…For the extraction of physiologically relevant information from single volume pulse cycles, the pulse decomposition analysis (PDA) has proven to be a powerful tool [1]. In the PDA, a single volume cycle is modelled as a linear superposition of basis functions.…”

Assessment of Cardiovascular Parameters with the Caretaker Device during Meditation

“…First, several reports have shown the utility of HRV is the assessment of short and long term outcomes that reflect physiologic, pharmacologic, and pathologic conditions [8][9][10]. We and others have identified that meditation increases LF and that this has been interpreted as reflecting an increase in the balance of the autonomic nervous system [5,11].…”

“…Using a smoother to eliminate false peaks, heart beats can be detected as peaks above a certain threshold. Subsequently, the series of inter beat intervals for a subject are taken to be the differences between times of these peaks and the instantaneous heart rates at each beat are calculated as the inverse of these interbeat intervals [9,10]. For each heart beat, the systolic blood pressure and diastolic blood pressure are taken to be the differences in the pressure components of the raw expression data.…”

FPGA-based design and implementation of arterial pulse wave generator using piecewise Gaussian–cosine fitting

“…Actually, the parameters of the decomposed Gaussian functions can be deduced from the feature points of the pulse wave such as the crests, troughs, and periods of the pulse wave [5,[20][21][22]. Therefore, it could be better if a user can simply and freely set several feature points through an independent embedded system and let this portable system generate the simulated pulse wave that can meet the user's requirement and approximate the realistic pulse wave with high accuracy [23].…”