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Current Directions in Biomedical Engineering

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Buzug, Thorsten M. / Haueisen, Jens / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Kraft, Marc / Lenarz, Thomas / Leonhardt, Steffen / Malberg, Hagen / Penzel, Thomas / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Urban, Gerald A.


CiteScore 2018: 0.47

Source Normalized Impact per Paper (SNIP) 2018: 0.377

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2364-5504
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Novel computation of pulse transit time from multi-channel PPG signals by wavelet transform

Towards continuous, non-invasive blood pressure estimation

Alexandru-Gabriel Pielmuş
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  • Chair of Electronics and Medical Signal Processing, Technische Universität Berlin, Berlin, Germany, E-mail:
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/ Maik Pflugradt
  • Chair of Electronics and Medical Signal Processing, Technische Universität Berlin, Berlin, Germany, E-mail:
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/ Timo Tigges
  • Chair of Electronics and Medical Signal Processing, Technische Universität Berlin, Berlin, Germany, E-mail:
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/ Michael Klum
  • Chair of Electronics and Medical Signal Processing, Technische Universität Berlin, Berlin, Germany, E-mail:
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/ Aarne Feldheiser
  • Klinik für Anästhesiologie m.S. operative Intensivmedizin, Charité Universitätsmedizin Berlin, Berlin, Germany
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/ Oliver Hunsicker
  • Klinik für Anästhesiologie m.S. operative Intensivmedizin, Charité Universitätsmedizin Berlin, Berlin, Germany
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/ Reinhold Orglmeister
  • Chair of Electronics and Medical Signal Processing, Technische Universität Berlin, Berlin, Germany, E-mail:
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Published Online: 2016-09-30 | DOI: https://doi.org/10.1515/cdbme-2016-0047

Abstract

Being able to accurately monitor blood pressure in a reliable, truly non-invasive manner is a highly sought after goal within the biomedical community. In this paper we propose and assess a system, methodology and algorithm for unobtrusively obtaining true pulse transit time data from readily accessible peripheral locations, such as the hand, using a highly synchronous body-sensor-network encompassing an electrocardiogram- and dual mode photoplethysmogram sensor node. The results suggest the feasibility of acquiring such data, which strongly correlates with the recorded reference blood pressure, and can therefore be further employed to track changes thereof.

Keywords: arm; blood pressure; continuous; fiducial; non-invasive; PPG; pulse transit time; pulse arrival time; wavelet

1 Introduction

Blood pressure issues are a serious health risk, not only for the elderly, but also for the population at large. Its monitoring and regulation is therefore crucial for keeping the human machinery running for as long as possible. Other than routine punctual check-ups in ambulant settings, close surveillance over extended periods of time may become necessary in case of intermittent ailments, and has also long been a part of every major surgical procedure. The need for a continuous and non-invasive method of assessing blood pressure (BP) is as high as ever, with various success in its resolution. The two most common procedures of measuring BP are the cuff sphygmomanometer and the intra-arterial catheter. The first one, while somewhat non-invasive, has the big deficit of being confined to burst sampling rates of a few per minute, and much lower for long-term monitoring (due to its cutting off of blood flow), and can only be reasonably used for punctual data acquisition. On the other side of the spectrum lies the arterial catheter – used almost exclusively during major surgical procedures, it delivers a continuous and accurate reading of BP, at the cost of being prohibitively invasive and risky for routine deployment. A few devices use finger cuffs to continuously, quasi-non-invasively measure BP, akin to the sphygmomanometer, with various accuracies and need for periodic calibration [1].

A truly non-invasive, continuous method of estimating BP is based on the following simple principle: pressure waves travel at different speeds through a medium at different pressures; i.e. the velocity, and therefore the time required between two points therein, is a function of system pressure. Therefore, one should be able, at least in theory, to draw conclusions about the arterial BP from attaining information about the propagation of the pressure (pulse) wave within the arterial system [2], [3]. The pulse wave originates in the heart’s left ventricle during systole, and sets on its way to propagate through the aorta to subsequent arteries, arterioles and finally capillaries, before it is almost completely dissipated in the venous system. The bulk of the vascular resistance the pressure wave is pushing against is implemented by the smallest of the blood vessels, the arterioles and capillaries. Nonetheless, all blood vessels can expand or contract to a certain degree, as a result of sympatho-vagal activity partly informed by baro- and shear receptors in the artery walls [13].

Measurement system.
Figure 1

Measurement system.

The pulse arrival time (PAT) is composed of the pre-ejection period (PEP; isovolumetric ventricular contraction) and the pulse transit time (PTT) to the pick-up location [4]. PAT is usually measured from the preceding R-peak of the electrocardiogram (ECG) signal to a fiducial point (onset, peak, etc.) of the corresponding photoplethysmogram (PPG) pulse wave (see Figure 2). Since the PEP is difficult to predict or measure, and not a linear function of pressure, the PAT is a less accurate indicator overall. To rectify this, the aortic valve opening (AO) could be used, but this is an issue still under investigation [5]. Though more complicated to assess, the PTT exhibits much better tracking of the arterial BP [6], [7]. As can be seen in Figure 3, the PTT only describes the temporal offset between the arrival times of the pulse wave at two different locations along a blood vessel.

Arm positions.
Figure 2

Arm positions.

PAT and PTT wavelet method illustration.
Figure 3

PAT and PTT wavelet method illustration.

2 Methods

2.1 Hardware

To gain access to the PTT, two pick-up points for the pulse wave are needed at different distances from the heart along the same artery. We implement this using a dual photo plethysmography (PPG) node, encompassing a conceptually traditional, transmissive finger clip (though with adaptive gain and filtering) and a more intricate reflective sensor. These two are guaranteed by design to be absolutely synchronous. The 12 bit samples are acquired with a 500 Hz rate. The reflective sensor is placed either a top the radial or ulnar artery in close proximity to the wrist, dependent on the emerging signal quality. The finger clip can be positioned on whichever finger, as long as the arterial length in-between is roughly known (typ. 15–25 cm). This setup would, in theory, suffice for calculating the PTT. Nonetheless, for the sake of robustness, a wirelessly (Bluetooth) synchronized minimal ECG is recorded to be able to assess the different PAT at the two sites. These subsystems are part of a bigger robust body-sensor-network (rBSN) [8], [9].

The standard used for validation is the data retrieved from a Finapres Medical Systems Portapres system, which has been synchronously recorded via the Portapres analog output module and an rBSN universal data recorder. The finger cuff is attached to a different finger than the transmissive PPG clip, so as to avoid interference, and the hydrostatic pressure compensation of the Portapres has been deactivated. The Portapres is by no way a gold standard – unfortunately arterial catheter or cuff sphygmomanometer measurements were not viable because of their invasivity and low sample rate respectively.

2.2 Methodology

The experiment involves raising and holding the measured limb at various angles to the ground while standing: –90, –45, 0, 45 and 90 degrees respectively [10]. This effects different relative height to the heart and emulates a change in blood pressure. The limb can either be passively held in place by appropriate means, or actively held up, yielding two measurement scenarios.

2.3 Processing

The resulting signals (one ECG – preferably Einthoven II, and two PPGs) are processed in Matlab, at first using quasi-standardized algorithms: the R-peak detection is done via a Pan-Thompkins-like algorithm [11], albeit slightly adapted for noisy environments. The DC component of the PPG signals is discarded, as it bears no significance for the further proceedings, and different fiducial points thereof are computed; for example pulse wave onset and peak are extracted with a modified Zong algorithm [12].

From here on, two methods will be pursued: based on the aforementioned fiducial points, or employing the continuous wavelet transform. Since the expected fluctuation in PAT is very small, the task of reliably accessing this information is jeopardized by signal disturbances. Therefore the chosen reference point for the pulse wave arrival is halfway between onset and first maximum – much more immune to noise and artifacts due to its location on the sharply ascending upslope.

The second method relies on the continuous wavelet transform to identify the pulse wave location within the signal.

The advantage of this approach, though more computationally complex, is its relative imperviousness to noise and uncorrelated artifacts. To increase the temporal resolution of the results, a ten-fold up sampling is performed, followed by the CWT with an appropriate analysing wavelet. In the performed analysis, Mexican Hat employing rather large scaling factors has empirically been proven most appropriate so far. As pulse wave morphology can vary very strongly between individuals and even at different pick-up locations (Figure 3) of the same one, the effect of wavelet shape is difficult to evaluate. After each R-peak, the maximum of the resulting coefficients is an indicator for the most probable wave location. The resulting indexes from the two methods can now be subtracted to gain the PTT between wrist and finger. As obvious from Figure 3, the morphology of the pulse wave, or rather the change thereof, plays a role in the detected peak locations, and therefore the calculated PAT and PTT. This is, however, not problematic, but should much rather could have the added benefit of assessing two features at the same time – PTT and morphology variation related to BP changes. Nonetheless, further research needs to be undertaken to investigate the link.

3 Results

Over 800 heartbeats have been analysed so far, pertaining to five postures in each of the two experimental setups (passive vs. active arm sustain). The results fall in line with the expected behavior, as can be seen in Table 1: when the arm is lowered, and the net pressure high, the PTT remains low around 5 ms. With progressive heightening of the arm, the BP drops, and the PTT rises to in excess of 120 ms. Also, while the transitions between arm states in the passive scenario tended to be more gradual, the active raising and sustaining caused sharp and well-defined jumps in PTT. Figures 4 and 5 exemplarily show the PTT and PAT of a single measurement (male, healthy, 26, assisted arm raise) – the 90 degree segment data should be taken with a grain of salt, as the PPG quality degrades to a point where both methods fail to reliably detect most pulse waves.

Table 1:

Mean PAT and PTT and BP change vs. arm angle (90° data unreliable).

PTT results with wavelet method.
Figure 4

PTT results with wavelet method.

PAT results at wrist and finger.
Figure 5

PAT results at wrist and finger.

3.1 Remarks

As stated, during the 90 and sometimes 45 degree raising of the arm, the PPG quality spontaneously degenerates, yielding an unsatisfactorily accurate signal for determining the pulse wave locations. This is due to multiple factors, amongst which is the experimental setup itself. By producing such a big height difference and, therefore, static pressure differential between heart and hand, the pulse strength is strongly diminished. Furthermore, due to the uncomfortable and somewhat unnatural pose, tremors and contractions are provoked in the subject, juddering the measurement apparatus and introducing disturbances.

The PTT does not, however, tell the whole truth. One would likely expect to find a simultaneous fluctuation of the PAT at both locations (i.e. wrist and finger) – interestingly, this is not the case. While the PAT at the fingertip does visibly scale with the height of the hand, the PAT at the wrist seems less influenced, remaining flat almost throughout all phases, as depicted in Figure 5. This would suggest that the big arteries (aorta, brachial and radial/ulnar) do not experience much pressure change at all, even when the BP as a whole does vary. This is consistent with the actuality that most of the vascular resistance occurs in the small blood vessels [13], making the changes in BP more visible therein.

Finally, it has been observed that the PAT in the fingertips (and accordingly the PTT) does not return to base level as soon as the limb is lowered in its incipient position, but does linger in a heightened state for some time. This is probably due to the latency of the capillary system’s reaction to abrupt state changes. Also, PAT at the wrist seems to rise only for the 90 degree test, but returns to normal instantaneously after release, hinting at a possible mechanical clamping of the arteries at the shoulder.

4 Conclusion

It has been proven possible to extract meaningful PTT information from non-invasive, continuous forms of pulse wave measurements via PPG. A compact system with sufficient synchronicity and sampling rate can be employed to record these variations even over relatively short pick-up distances. The evoked changes in blood pressure by posture of the measured limb are made well visible in the PTT with the use of the described processing methods and can be therefore used to predict BP.

Acknowledgement

The authors would like to thank all the people involved in creating and refining the rBSN over the years, and the volunteers partaking in the testing thereof.

Author’s Statement

Research funding: The author state no funding involved. Conflict of interest: Authors state no conflict of interest. Material and Methods: Informed consent: Informed consent has been obtained from all individuals included in this study. Ethical approval: The research related to human use complied with all the relevant national regulations, institutional policies and was performed in accordance with the tenets of the Helsinki Declaration, and has been approved by the authors’ institutional review board or equivalent committee.

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About the article

Published Online: 2016-09-30

Published in Print: 2016-09-01


Citation Information: Current Directions in Biomedical Engineering, Volume 2, Issue 1, Pages 209–213, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2016-0047.

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©2016 Alexandru-Gabriel Pielmuş et al., licensee De Gruyter.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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