The overall analysis shows PAT is an unreliable measure of PTT and a poor surrogate under clinical interventions common in a critical care setting, due to intra- and inter- subject variability in PEP.

Identification of end systole is often necessary when studying events 9 specific to systole or diastole, for example, models that estimate cardiac func-10 tion and systolic time intervals like left ventricular ejection duration. In prox-11 imal arterial pressure waveforms, such as from the aorta, the dicrotic notch 12 marks this transition from systole to diastole. However, distal arterial pres-13 sure measures are more common in a clinical setting, typically containing no 14 dicrotic notch. This study defines a new end systole detection algorithm, for 15 dicrotic notch-less arterial waveforms. The new algorithm utilises the beta 16 distribution probability density function as a weighting function, which is 17 adaptive based on previous heartbeats end systole locations. Its accuracy is 18 compared with an existing end systole estimation method, on dicrotic notch-19 less distal pressure waveforms. Because there are no dicrotic notches defining 20 end systole, validating which method performed better is more difficult. Thus, 21 a validation method is developed using dicrotic notch locations from simul-22 taneously measured aortic pressure, forward projected by pulse transit time 23 (PTT) to the more distal pressure signal. Systolic durations, estimated by 24 each of the end systole estimates, are then compared to the validation systolic 25 duration provided by the PTT based end systole point. Data comes from ten 26 pigs, across two protocols testing the algorithms under different hemodynamic 27 states. The resulting mean difference ± limits of agreement between measured 28 and estimated systolic duration, of −8.7 ± 26.6 ms verses −23.2 ± 37.7 ms, for 29 the new and existing algorithms respectively, indicate the new algorithms su-30 periority. 31Keywords End systole · Start diastole · Dicrotic notch · Cardiovascular 32 system · Pressure contour interpretation. 33 1 Introduction 34 The dicrotic notch is a combination of two turning points with respective 35 local minimum and maximum in arterial pressure signals, found between a 36 beats peak pressure and diastolic relaxation. It is formed by the reflection of 37 a wave off of the aortic valve, following valve closure [1]. Thus, it is clearest in 38 proximal pressure signals and determines transition from systole to diastole [2]. 39 Specifically, aortic systolic duration, associated with left ventricular ejection, 40 lasts from the foot of the aortic pressure wave to the dicrotic notch [3-5]. 41 Diastolic duration, associated with ventricular relaxation, is the remaining 42 time from the dicrotic notch to the next pressure waveform foot. 43 Given the physical significance of the dicrotic notch as a systolic/diastolic 44 time reference, it has been used in numerous applications, including, pulse wave 45 velocity calculations [6], models estimating cardiovascular function [7-12], and 46 left ventricular ejection time. Therefore there are many different algorithms 47 which apply different signal processing methods to dicrotic notch detection [2, 48 13-16]. 49 Despite the...

BackgroundPulse oximeters continuously monitor arterial oxygen saturation. Continuous monitoring of venous oxygen saturation (SvO2) would enable real-time assessment of tissue oxygen extraction (O2E) and perfusion changes leading to improved diagnosis of clinical conditions, such as sepsis.MethodsThis study presents the proof of concept of a novel pulse oximeter method that utilises the compliance difference between arteries and veins to induce artificial respiration-like modulations to the peripheral vasculature. These modulations make the venous blood pulsatile, which are then detected by a pulse oximeter sensor. The resulting photoplethysmograph (PPG) signals from the pulse oximeter are processed and analysed to develop a calibration model to estimate regional venous oxygen saturation (SpvO2), in parallel to arterial oxygen saturation estimation (SpaO2). A clinical study with healthy adult volunteers (n = 8) was conducted to assess peripheral SvO2 using this pulse oximeter method. A range of physiologically realistic SvO2 values were induced using arm lift and vascular occlusion tests. Gold standard, arterial and venous blood gas measurements were used as reference measurements. Modulation ratios related to arterial and venous systems were determined using a frequency domain analysis of the PPG signals.ResultsA strong, linear correlation (r 2 = 0.95) was found between estimated venous modulation ratio (RVen) and measured SvO2, providing a calibration curve relating measured RVen to venous oxygen saturation. There is a significant difference in gradient between the SpvO2 estimation model (SpvO2 = 111 − 40.6*R) and the empirical SpaO2 estimation model (SpaO2 = 110 − 25*R), which yields the expected arterial-venous differences. Median venous and arterial oxygen saturation accuracies of paired measurements between pulse oximeter estimated and gold standard measurements were 0.29 and 0.65%, respectively, showing good accuracy of the pulse oximeter system.ConclusionsThe main outcome of this study is the proof of concept validation of a novel pulse oximeter sensor and calibration model to assess peripheral SvO2, and thus O2E, using the method used in this study. Further validation, improvement, and application of this model can aid in clinical diagnosis of microcirculation failures due to alterations in oxygen extraction.

BackgroundPressure contour analysis is commonly used to estimate cardiac performance for patients suffering from cardiovascular dysfunction in the intensive care unit. However, the existing techniques for continuous estimation of stroke volume (SV) from pressure measurement can be unreliable during hemodynamic instability, which is inevitable for patients requiring significant treatment. For this reason, pressure contour methods must be improved to capture changes in vascular properties and thus provide accurate conversion from pressure to flow.MethodsThis paper presents a novel pressure contour method utilizing pulse wave velocity (PWV) measurement to capture vascular properties. A three-element Windkessel model combined with the reservoir–wave concept are used to decompose the pressure contour into components related to storage and flow. The model parameters are identified beat-to-beat from the water-hammer equation using measured PWV, wave component of the pressure, and an estimate of subject-specific aortic dimension. SV is then calculated by converting pressure to flow using identified model parameters. The accuracy of this novel method is investigated using data from porcine experiments (N = 4 Pietrain pigs, 20–24.5 kg), where hemodynamic properties were significantly altered using dobutamine, fluid administration, and mechanical ventilation. In the experiment, left ventricular volume was measured using admittance catheter, and aortic pressure waveforms were measured at two locations, the aortic arch and abdominal aorta.ResultsBland–Altman analysis comparing gold-standard SV measured by the admittance catheter and estimated SV from the novel method showed average limits of agreement of ±26% across significant hemodynamic alterations. This result shows the method is capable of estimating clinically acceptable absolute SV values according to Critchely and Critchely.ConclusionThe novel pressure contour method presented can accurately estimate and track SV even when hemodynamic properties are significantly altered. Integrating PWV measurements into pressure contour analysis improves identification of beat-to-beat changes in Windkessel model parameters, and thus, provides accurate estimate of blood flow from measured pressure contour. The method has great potential for overcoming weaknesses associated with current pressure contour methods for estimating SV.

BackgroundThe aim of this paper was to establish a minimally invasive method for deriving the left ventricular time varying elastance (TVE) curve beat-by-beat, the monitoring of which’s inter-beat evolution could add significant new data and insight to improve diagnosis and treatment. The method developed uses the clinically available inputs of aortic pressure, heart rate and baseline end-systolic volume (via echocardiography) to determine the outputs of left ventricular pressure, volume and dead space volume, and thus the TVE curve. This approach avoids directly assuming the shape of the TVE curve, allowing more effective capture of intra- and inter-patient variability.ResultsThe resulting TVE curve was experimentally validated against the TVE curve as derived from experimentally measured left ventricular pressure and volume in animal models, a data set encompassing 46,318 heartbeats across 5 Piétrain pigs. This simulated TVE curve was able to effectively approximate the measured TVE curve, with an overall median absolute error of 11.4% and overall median signed error of −2.5%.ConclusionsThe use of clinically available inputs means there is potential for real-time implementation of the method at the patient bedside. Thus the method could be used to provide additional, patient specific information on intra- and inter-beat variation in heart function.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-017-0338-7) contains supplementary material, which is available to authorized users.