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Removing the deflation from the recorded NIBP data Fig. 2: a) Filtered pulses (right scale) obtained by band pass (0.3--20 Hz) filtering and b) Oscillometric.

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Presentation on theme: "Removing the deflation from the recorded NIBP data Fig. 2: a) Filtered pulses (right scale) obtained by band pass (0.3--20 Hz) filtering and b) Oscillometric."— Presentation transcript:

1 Removing the deflation from the recorded NIBP data Fig. 2: a) Filtered pulses (right scale) obtained by band pass ( Hz) filtering and b) Oscillometric pulses (right scale) obtained by segmentation of data into heart beats. Measured NIBP data (left scale) are plotted with red colour. Segmentation borders (vertical dashed lines) coincide with time points that determine the negative envelope of filtered pulses. The deflation signal, calculated by the interpolation of data between subsequent segment borders, is subtracted from the measured data to obtain only pulses with positive deflections (oscillometric pulses). Periodic fist closing artefact Fig. 5: Steps in the fist artefact extraction. The person was closing her/his fist every 5 seconds during the NIBP measurement. We performed the following steps during the analysis displayed in Fig. 5: a)heart beat segmentation using filtered data (see also Fig. 2) b)artefact segmentation - 2 s interval around the artefact peak c)calculation of pulses using heart beat segmentation from (a) d)estimation of oscillometric waveform from the normalised reference waveform, obtained from artefact-free recordings (see, Fig. 3) e)subtraction of estimated oscillometric waveform (d) from (c) to obtain only artefacts f)extraction of artefacts from (e) using artefact segmentation in (b), and averaging these artefacts. Averaged shape is shown on the top and plotted with a thicker line. Results of forward and backward transformations When comparing waveforms obtained from different recordings, shifts of ±N (usually 5) beats are allowed to reach the best match between them. We called this procedure waveform optimisation (WO). The average waveform is then calculated by summing optimally shifted waveforms, see Fig. 3c. Criteria for the WO include minimum value of relative difference (RD), maximum value of correlation coefficient (CC) and minimum value of maximum difference (MD) between the compared waveforms. To get the reference signal for a given measured oscillation waveform in a real (measured) time scale, one has to transform the normalised reference oscillation waveform back to the real time scale of the given measured data. However, since oscillometric pulses slightly differ from measurement to measurement, we have used beside the above WO also a single beat optimisation (SBO) during the transformation. In both types, we first transform a given waveform into the heart beat view. Then we compare it with the normalised reference waveform. Finally, we transfer it back to the real time view. In the case of SBO, a single oscillometric pulse at the given cuff pressure level is compared with pulses from the normalised reference. Amplitude corrections and time shifts of these pulses are allowed within some reasonable constraints and the best fitting reference pulse is determined for each pulse. Averaging oscillometric non-invasive blood pressure recordings: Transformation into the normalised view Vojko Jazbinšek*, Janko Lužnik, Zvonko Trontelj Institute of Mathematics, Physics and Mechanics, University of Ljubljana, Ljubljana, Slovenia * Data acquisition. For NIBP measurements, we have used the device (see, Fig.1) that was designed by LODE (Groningen, NL) for the EU-project ”Simulator for NIBP” [3]. We performed measurements on the upper arm of healthy volunteers. Altogether, we have recorded data on 23 persons (11 females and 12 males) between 20 and 66 years old. Most of attention was paid to the external artefacts that could be repeated for every volunteer and arose from known effects: beating the cuff, external sound in the environment, moving of the arm support, tremor, coughing, speaking, walking, muscle contraction in the upper arm induced by moving the arm, hand, finger or fist, etc. Fig. 5 show results for the periodic fist closing artefact. Fig. 1: NIBP device built in a personal computer. Acknowledgment We thank [3] for technical and financial support. References [ 1] NG, K-G. and SMALL, C.F. Survey of automated non-invasive blood pressure monitors. Journal of Clinical Engineering, 19:452–475, [2] JAZBINSEK, V., LUZNIK, J., and TRONTELJ, Z. Non-invasive blood pressure measurements: separation of the arterial pressure oscillometric waveform from the deflation using digital filtering. IFBME proceedings of EMBEC’05, [3] European 5th framework programme. ”Simulator for NIBP”, Grant No. G6DR-CT [4] JACKSON, L. B. Digital Filters and Signal Processing. Kluwer Academic Publishers, Boston, Fig. 3: Oscillometric pulses from the 1 st (a) and the 2 nd recording (b), and the normalised reference (c) obtained by averaging pulses from (a) and (b). Introduction Many oscillometric non-invasive blood pressure (NIBP) measuring devices are based on recording the arterial pressure pulsation in an inflated cuff wrapped around a limb during the cuff pressure deflation [1]. The deflation can be removed from the recorded NIBP data by a digital band pass filtering [2] (see, Fig. 2). The obtained arterial pressure pulses are known as the oscillometric pulses. However, some NIBP measurements are contaminated with the external artefacts arising mainly from person’s movements. In most cases, these artefacts generate pressure changes in the cuff, which have similar frequency response as the oscillometric pulses and it is therefore difficult to separate them. In order to get oscillometric pulses independent on variations of each heart beat duration, we have introduced a transformation of data into the normalised heart beat view. The transformed pulses can be averaged to obtain the normalised reference pulses. We have used such reference to extract artefacts from measured data. Transformation into the normalised view The variable duration ∆t var (sampled with ν m into N var points) of each measured heart beat is re-scaled to a fixed value ∆t fix (ν fix, N var ). The core of the forward transformation (∆t var → ∆t fix ) is to determine a resampling frequency (ν re ) to represent each heart beat (∆t var ) with the N fix points: For the backward transformation (∆t fix → ∆t var ) one has to resample the "normalised" data on the ∆t fix with a frequency ν b to obtain the original points again Fig. 3 demonstrates how two recordings (Figs. 3a,b) can be transformed by Eq. 1 and then averaged to obtain a reference waveform in the normalised view. Fig. 4 shows how such reference can be subtracted from the third recording (Fig. 4a) and then transformed back to the real time view by using Eq. 2. Fig. 4: Oscillometric pulses from the a) 3 rd recording and residuals after reference subtracting obtained by using b) WO and c) SBO.


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