Compensating for Respiratory Artifacts in Blood Pressure Waveforms

ABSTRACT

Cardiac catheterization has for a long time been a valuable way to evaluate the hemodynamics of a patient. One of the benefits is that the entire blood pressure waveform can be recorded and visualized to the cardiologist. These measurements are however disturbed by different phenomenon, such as respiration and the dynamics of the fluid filled catheter, which introduces artifacts in the blood pressure waveform. If these disturbances could be removed, the measurement would be more accurate. This report focuses on the effects of respiratory artifacts in blood pressure signals during cardiac catheterization.

Four methods, a standard bandpass filter, two adaptive filters and one wavelet based method are considered. The difference between respiratory artifacts in systolic and diastolic pressure is studied and dealt with during compensation. All investigated methods are implemented in Matlab and validated against blood pressure signals from catheterized patients.

The results are algorithms that try to correct for respiratory artifacts. The rate of success is hard to determine since only a few measured blood pressure signals have been available and since the size and appearance of the actual artifacts are unknown.

PROBLEM DEFINITION

Figure 2.1. Blood pressure waveform. Top : Ventricular blood pressure wave form with systolic and diastolic pressure marked out. Bottom : Arterial pressur e waveform with systolic, begin-diastolic and end-diastolic pressure marked out

Figure 2.1. Blood pressure waveform. Top : Ventricular blood pressure wave form with systolic and diastolic pressure marked out. Bottom : Arterial pressur e waveform with systolic, begin-diastolic and end-diastolic pressure marked out

In the ventricular blood pressure waveform, two distinct diastolic pressures can be measured. The begin-diastolic pressure (BDP) is the pressure at the beginning of the diastolic period while the end-diastolic pressure (EDP) is the pressure at its end. In the arterial blood pressure only one pressure point, the diastolic pressure (DP), which is the lowest pressure during one cardiac cycle is determined.

A more detailed description how these pressures are derived from a blood pressure signal is presented in Appendix A. These pressures are important when diagnosing a patient. Below in Figure 2.1 is an example of a ventricular and arterial blood pressure signal measured during cardiac catheterization.

Figure 2.4. Measured ECG-signal. The high peaks is the electrical discharge trigge ring a new contraction of the ventricles

Figure 2.4. Measured ECG-signal. The high peaks is the electrical discharge trigge ring a new contraction of the ventricles

The high peaks, denoted R in Figure 2.4, is the electric discharge stimulating the ventricles to contract. The heart rate is determined from inverse of the length of the interval between those peaks, normally known as R-R interval.

Figure 2.6. Top : Blood pressure (Right ventricle). Bottom : Isolated respirator y artifact. Solid curve : Artifacts considered as variations in diastolic pressure. Dashed : Artifacts considered as variations in systolic pressure

Figure 2.6. Top : Blood pressure (Right ventricle). Bottom : Isolated respirator y artifact. Solid curve : Artifacts considered as variations in diastolic pressure. Dashed : Artifacts considered as variations in systolic pressure

To help visualize these variations, two independent attempts to isolate respiratory artifacts in a ventricular blood pressure signal are calculated. One will consider the respiratory artifacts as the variations in diastolic pressure, while the other as the variations in systolic pressure. The resulting isolated artifacts are displayed below in Figure 2.6. There is a clear difference between the two curves. If for example only the curve formed by diastolic pressure is used to compensate the entire waveform, the variations in diastolic pressure will decrease, but there will still be large variations in systolic pressure.

IMPLEMENTATIONS

Figure 3.1. Bandstop filter characteristic. F pass and F stop define the boundaries for the pass- and stop band in the frequency range, while A pass and A stop defines the dampening in each band. F s is the sample rate

Figure 3.1. Bandstop filter characteristic. Fpass and Fstop define the boundaries for the pass-and stop band in the frequency range, while Apass and Astop defines the dampening in each band. Fs is the sample rate

A normal approach to separate an unwanted disturbance from an interesting signal is to apply some sort of filter. Since respiratory artifacts are a low-frequency disturbance, a natural approach would be to apply a high-pass filter. But if a high-pass filter were applied, the DC-component, the drift and other low-frequency components would be lost. Instead, a band-pass filter only filtering out signal components at the frequency of the respiration is constructed. The general frequency characteristics of a band- stop filter is displayed in Figure 3.1.

Figure 3.5. Left : Measured respiratory signal. Right: The corresponding actual artifacts, approximated from the blood pressure signal

Figure 3.5. Left : Measured respiratory signal. Right: The corresponding actual artifacts, approximated from the blood pressure signal

Since the measured respiration signal is a measurement of the level of CO2 in the patients expiration air, not a direct measurement of respiratory pressure changes inside the chest, the signal is not suited to be used directly as a reference signal to the adaptive filter. The appearance of the signal is different from the artifact s seen in blood pressure. An example of this is displayed below in Figure 3.5.

Figure 3.11. An arterial blood pressure signal with respiratory artifacts. To make the respiratory artifacts more visible, the tops of the signals are shown. T op : Original signal. Middle : Only approximations removed. Bottom : Approximations and th resholded details removed

Figure 3.11. An arterial blood pressure signal with respiratory artifacts. To make the respiratory artifacts more visible, the tops of the signals are shown. Top : Original signal. Middle : Only approximations removed. Bottom : Approximations and thresholded details removed

The thresholding is performed with soft thresholding, where the kept details above the threshold is subtracted by the threshold value. We now have details containing the variations in amplitude and approximations containing the low-frequency respiratory disturbance. These are then used to recompose a new signal, containing only respiration. This signal can then be subtracted from the original signal to remove respiration. If this thresholding is not completed and just the approximation are removed, this will be more of a normal filtering approach, not removing all variations as efficiently. An example of the difference in a compensated blood pressure signal is displayed in Figure 3.11

RESULT

Figure 4.1. Arterial pressure signal compensated with adaptive filter and RSA compensation

Figure 4.1. Arterial pressure signal compensated with adaptive filter and RSA compensation

There is problems of getting the algorithm to converge in the short time period of the signal, especially if respiration is varying. The power spectrum from the compensation of arterial signal 3 is displayed below in Figure 4.1 together with the corresponding compensated signal.

Figure 4.1. Arterial pressure signal compensated with adaptive filter and RSA compensation. Top : Original signal. Middle : Adaptive filter applied to diastolic pressure. Bottom : RSA compensation applied. After about 5-6 seconds the algorithm has found the correlation between the respiratory heart rate variations and the systolic pressure variations which are then canceled out.

CONCLUDING REMARKS

In this report, four different methods have been implemented and tested for the task of removing respiratory artifacts. Different respiratory variations in systole (the contraction phase of the heart) and diastole (the relaxation phase) is a problem that had to be dealt with. Variations in diastole are considered to be the effects of intra-thoracic pressure variations and should be removed, while the different variations in systolic pressure may result from other respiratory driven phenomenon.

The simple filter solution removes respiratory artifacts of a specific frequency band. There is no discrimination between variations in systolic and diastolic pressure which make a normal filtering solution a poor choice.

Adaptive algorithms can be adjusted to only update according to diastolic values, then only filtering out the actual intra-thoracic respiratory artifacts. They can also be extended to remove respiratory induced systolic pressure variations if that is required. If the respiratory frequency varies, the algorithms may need a few seconds to adapt to this new respiratory frequency before displaying a correct result.

The wavelet compensation dampens all artifacts in the frequency range of the respiration and can suppress deviations in systolic pressure, which produces a good, smooth result from all tested signals. However, it gives no real physical explanation for the removed artifacts and is not implemented in real-time.

It is not easy to fully determine the cause of the respiratory artifacts seen in a blood pressure waveform. Are the variations just a direct reflection of pressure changes in the thoracic region or variations caused by the heart working differently at different phases of the respiration? And if so, should these variations be compensated for? This work offers different ways to deal with the artifacts. They can be used to eliminate both variations considered consist of intra-thoracic pressure and effects considered originating from RSA and other phenomena in the systolic pressure. If these are used, one should first consider what kind of effects that should be filtered out.

Source: Linköping University
Author: Martin Wikström

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