Improved Algorithm for Measurement of Blood Pressure based on a Laser Doppler Flowmetry Signal

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1 Master of Science Thesis in Biomedical Engineering Department of Biomedical Engineering, Linköping University, 2016 Improved Algorithm for Measurement of Blood Pressure based on a Laser Doppler Flowmetry Signal Sofie Mårtensson

2 Master of Science Thesis in Biomedical Engineering Improved Algorithm for Measurement of Blood Pressure based on a Laser Doppler Flowmetry Signal Sofie Mårtensson LiTH-IMT/BIT30-A-EX--16/534--SE Supervisor: Examiner: Karin Larsson Perimed AB Ingemar Fredriksson imt, Linköpings universitet Marcus Larsson imt, Linköpings universitet Biomedical Engineering Department of Biomedical Engineering Linköping University SE Linköping, Sweden Copyright 2016 Sofie Mårtensson

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5 Abstract People with diabetes suffer from a high risk of developing foot related diseases. It is therefore important to perform a blood pressure measurement on the toe to be able to diagnose and treat in time. Using laser Doppler flowmetry has been proven to be a useful technique for this purpose during a standard blood pressure measurement procedure using a cuff. The laser Doppler probe detects once the blood flow returns which can then be related to the pressure value. However, the algorithm currently used by the company for detection of return of blood flow is in need of improvements. This thesis aims to develop an improved algorithm, which is more robust against artifacts. Furthermore, a warning system for uncertainties in the detection will be developed and integrated with the new algorithm. To create the algorithm an investigation of the signals appearances was performed to obtain an understanding of what artifacts and characteristics the algorithm should be able to handle. First three different basic approaches were implemented and tested, namely model curve, threshold and pulsations. These algorithms were then combined into two different more complex algorithms. One of them consisted of the model curve and the pulsation algorithm, the second combined algorithm consisted of the threshold algorithm and the pulsation algorithm. From the result it was found that the second combined algorithm performed best. It had a high accuracy and a well-functioning warning system. However, the algorithm had problems to correctly detect the return of flow when it is characterised by a slow increase of the perfusion. The biggest contribution by this thesis is the newly developed warning system. A false detection can lead to a false diagnose to be given if the operator is not attentive. The warning system is therefore an important feature since it can prevent this from occurring. v

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7 Acknowledgments First and foremost I would like to thank my supervisor at Linköping University Ingemar Fredriksson for his support and ideas throughout the project. I would also like to thank the company Perimed AB for the opportunity to carry out the thesis and for a good cooperation. A special thank you goes to my supervisor at the company Karin Larsson for her helpfulness. Furthermore, I wish to thank my examiner at Linköping University, Marcus Larsson. Finally I would like to thank my family and friends for their continuous support during the project. Linköping, June 2016 Sofie Mårtensson vii

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9 Contents Notation xi 1 Introduction Aim Delimitations Theoretical Background Light Interaction with Tissue Refraction Scattering Absorption Laser Doppler Flowmetry Motivation for LDF Clinical Motivation - Toe Pressures Comparison to Other Techniques The Physiology Behind Artifacts and Differences in Signal Appearance Method Database Investigation of Signals Development of Algorithms Algorithm 1 - Model Curve Algorithm 2 - Threshold Algorithm 3 - Pulsation Detection Combined Algorithm 1 - Model Curve and Pulsations Combined Algorithm 2 - Threshold and Pulsations Warning system Comparison between the Algorithms Results Existing Algorithm Algorithm 1 - Model Curve ix

10 x Contents 4.3 Algorithm 2 - Threshold Algorithm 3 - Pulsations Combined Algorithm 1 - Model Curve and Pulsations Combined Algorithm 2 - Threshold and Pulsations Comparison between the Algorithms Accuracy Comparison Discussion The Algorithms Existing Algorithm Algorithm 1 - Model Curve Algorithm 2 - Threshold Algorithm 3 - Pulsations Combined Algorithm 1 - Model Curve and Pulsations Combined Algorithm 2 - Threshold and Pulsations Warning System The Low Rise Problem Investigated Approaches Visual Detection Algorithm Changes for Implementation in the System General Guidelines for the System Conclusion 47 Bibliography 49

11 Notation Variables Notation Abbreviations Description n Refractive index µ a Absorption coefficient µ s Scattering coefficient g Anisotropy Abbreviation ABI CLI LDF PAD PPG PU RBC Description Ankle-Brachial Index Critical Limb Ischemia Laser Doppler Flowmetry Peripheral Arterial Disease Photoplethysmography Perfusion unit Red Blood Cell xi

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13 1 Introduction People with diabetes suffer a high risk of developing peripheral arterial disease (PAD), a condition which in severe cases can cause need of amputations and decease. It is therefore important to monitor the blood pressure in the feet and preferably in the toes to be able to diagnose and treat in time. [1] Laser Doppler flowmetry (LDF) has been proven to be a useful technique for detection of toe blood pressure in a standard measurement procedure using a cuff [2]. The working principle consists of a cuff placed on the arm, the ankle or the toe and a laser Doppler probe placed more peripheral, with regard to the vascular tree, than the cuff. The cuff is then inflated causing occlusion of the blood flow. The pressure in the cuff is reduced and at a certain pressure the blood flow returns and the return of the flow is detected by the laser Doppler probe and then related to the current pressure, see figure 1.1. This project was carried out in cooperation with Perimed AB, Järfälla, Stockholm. The algorithm currently used in their laser Doppler flowmetry system for detection of blood pressure has difficulty in accurately determine the return of the flow due to the highly varying appearance of the flow signal. Furthermore, one of the concerns is that a return of blood flow is more or less always detected, even in those cases when no detection is wanted, for example where the signal is too noisy. The project aims to improve blood pressure detection based on a blood flow signal retrieved by laser Doppler flowmetry. The algorithm which will be created aims to be more robust for noise and artifacts as well as signal variability and thereby improve the reproducibility. Moreover, improving the algorithm will make the Laser Doppler flowmetry technique more attractive in the commercial market. 1

14 2 1 Introduction Figure 1.1: Picture of a flow and pressure curve related in time. The black line indicates the time when the blood flow returns and the corresponding blood pressure. 1.1 Aim The aim of the project is to develop an algorithm to detect at which pressure the blood flow returns during a pressure measurement. Furthermore the algorithm should preferably beat the performance of current algorithms in commercial systems. The algorithm should be developed for a blood flow signal measured with laser Doppler flowmetry technique. The algorithm should be designed for real time analysis meaning that the complete signal will not be available, at the time of decision making. The reason for this requirement is that performing a blood pressure measurement can be uncomfortable for the patient. However a time delay of approximately 5 seconds after the actual blood flow has returned will be considered as real time. To ensure a certain quality of the final algorithm different approaches will be investigated. Furthermore, the algorithm should contain a warning system, which will warn the operator if there are uncertainties in the detection. 1.2 Delimitations This project will only include implementation of the algorithm in Matlab, meaning that the testing is limited to post processing of stored measurement, however the algorithm is still written for real time analysis. Since the algorithm is meant to later be implemented in C# for in vivo use the Matlab code to be produced will avoid Matlab specific functions, not easily converted to C#, as much as possible.

15 2 Theoretical Background This chapter will provide the reader with some background knowledge about the subject. First an introduction about light interaction with tissue will be presented to then go into principles about the laser Doppler flowmetry technique. Thereafter follows a section motivating the importance and advantage of the technique from both a clinical and a technical perspective. Finally, some background and explanation to why some of the artifacts appear in the signal will be presented. 2.1 Light Interaction with Tissue When describing light interaction and propagation in tissue the optical properties of the tissue are in focus. These properties include reflection, transmission, refraction, scattering and absorption which will be further explained below Refraction The refractive index, n, is an optical property for a medium and is defined as the ratio between the speed of light in vacuum, c, and the speed of light in the medium, v, see equation 2.1. [3] n = c v (2.1) When incoming light encounters a surface of a media with another refractive index some of the light will be reflected while the remaining light will be transmitted into the second media, see figure 2.1. [4] Refraction occurs when light goes from one media into another with a different refractive index. If this is the case the light changes direction according to Snell s law, see equation 2.2. This phenomena is visualized in figure 2.1 and 3

16 4 2 Theoretical Background will occur at large structures, compared to the wavelength, like at the boundaries between soft tissue, bone and skin. [4] n 1 sin θ 1 = n 2 sin θ 2 (2.2) Figure 2.1: Principle of refraction, in this illustration the media on top has a lower refraction index compared to the media below Scattering Scattering is an event which is crucial for diagnostic and therapeutic applications. When considering the case when light interacts with smaller structures, such as organelles and cell membranes, then scattering takes place instead of refraction. [4] Scattering means that when a light wave interacts with a particle in the tissue, with a different refractive index compared to its environment, it gets redirected into another angle, without losing its energy [3]. This means that the light transmitted into the tissue can go through several scattering events and then return to the surface and be detected [4]. The scattering coefficient, µ s, is a parameter describing the scattering property of a media, that is, how probable it is for the light to scatter when it interacts with the particle. Its inverse, the scattering mean free path 1 µ s, is defined as the mean distance the light will propagate before a scattering event will occur. [4] The scattering anisotropy factor, g, is another parameter used to describe light propagation in scattering medium. It is the mean cosine of the scattering angle, that is, the difference in direction between the incident light and the scattered light. The anisotropy depends on the wavelength of the incoming light and also the size and shape of the particle. [3] The anisotropy describes the relation between scattering in the forward and backward direction and the value variates between +1 to -1. A value of g above 0 means that the light will probably scatter in a forward direction, a value below 0 means that the light will probably scatter in a backward direction. [4]

17 2.2 Laser Doppler Flowmetry 5 The red blood cells (RBCs) have an anisotropy factor close to 1 which means that the light most probably will scatter in a forward direction. This means that the light has to go through many scattering events to return to where it was emitted. [5] However, there are several other structures in the tissue that can contribute to the backscatter effect since the human tissue is a hetrogeneous structure [4] Absorption Another optical property is absorption which means that when a photon interacts with a particle it loses energy either to conversion into heat or into a photon with less energy. These particles are called chromophores and are present in the tissue, as for example melanin, hemoglobin and water. [3] The absorption coefficient, µ a, is a measurement stating the probability of a chromophore to absorb a photon of a certain wavelength. Taking the inverse, 1 µa, the absorption mean free path is obtained, which is a measurement of how far the photon is likely to propagate before absorption occur. It can be seen that a high absorption coefficient means that the photons will only propagate a short distance before being absorbed. [4] Beer-Lambert law is used to see how the intensity, I, decreases depending on the thickness, z, and absorption coefficient, µ a, compared to the initial intensity, I 0. [4] I = I 0 exp( µ a z) (2.3) Depending on the wavelength the photon is more or less likely to be absorbed. To detect a sufficient signal it is necessary for enough photons to return to the surface, meaning that absorption is not allowed to occur too often, that is, the absorption coefficient has to be small. There is an interval of the wavelengths, nm, called the diagnostic window where the absorption is low for many of the chromophores in the tissue, see figure 2.2. It is therefore suited to use wavelengths within this interval to detect a good signal. [4] 2.2 Laser Doppler Flowmetry Laser Doppler flowmetry (LDF) is a non-invasive technique used to measure blood flow. It takes advantage of the Doppler effect, that is, light which interacts with a moving object obtains a shift in its frequency. The Doppler effect is classically described as a moving sound source in relation to an observer. When the source moves toward the observer the frequency increases and as the source moves away from the observer the frequency decreases, see Figure 2.3. [6] For simplicity, consider the one dimensional case when using ultrasound to detect the blood flow. When the soundwave hits a RBC which flows with a velocity the frequency of the sound changes. By measuring the frequency shift it is possible to calculate the velocity. However, when light is transmitted into tissue

18 6 2 Theoretical Background Figure 2.2: A graph showing the absorption coefficient for different chromophores. It can be seen that for a certain interval of wavelengths the absorption is particularly low. This interval is referred to as the diagnostic window. Figure 2.3: Sketch of the Doppler effect. the Doppler shift is not only determined by the velocity, instead it is much more complex due to the highly scattering property of tissue [6]. The LDF technique consists of two parts, first there is the light source which transmits laser light into the tissue and then there is the detection part which consists of a photodetector, see Figure 2.4. To be able to detect the frequency broadening due to the Doppler effect it is crucial that the incident light is monochromatic, which is the case when using laser. [7] The transmitted light propagates inside the tissue and interacts with particles as explained in section 2.1. The light scatters several times inside the tissue, which means that after a while the direction in which the light is propagating

19 2.2 Laser Doppler Flowmetry 7 Figure 2.4: Sketch of the LDF principle; light is transmitted into the tissue where it interacts with both static tissue and RBCs, then some of the light backscatters and impinges on the detector. can be seen as random. Consequently, this makes the laser Doppler flowmetry technique unable to detect the direction of the blood flow. [7] Additionally, if the light wave interacts with a moving object, in this case mainly RBCs, a shift in frequency will be introduced. However, when interaction occur with a stationary particle no shift in frequency will occur. The Doppler shift, described in equation 2.4, depends on the velocity of the RBCs, v, and the scattering vector, q, which is defined as the difference between the vector of the incoming light, k i, and the vector of the scattered light, k s, see Figure 2.5. Moreover, one photon can interact with moving RBCs several times causing an additional Doppler shift. [4] ω D = v q = v ( k i k s ) (2.4) Figure 2.5: Sketch of the scattering vector, q, where k i is the light incident on a RBC and k s is the light scattered by the RBC.

20 8 2 Theoretical Background Eventually some of the light returns to the surface of the skin, where the light intensity gets collected by the photodetector and converted into a photocurrent. The detected intensity consists of both light that has undergone a Doppler shift and light that only has interacted with stationary objects. [4] Extensive signal processing is then performed to estimate the tissue perfusion and the concentration of moving RBCs, this is further explained in [7]. 2.3 Motivation for LDF This section aims to motivate the importance and advantages of LDF, both from a clinical and a technical perspective Clinical Motivation - Toe Pressures Diabetes is a world wide disease. In 2011 the estimated number of people with diabetes was 366 million. Moreover, the prognosis is that the diabetic population will increase by about 50 % until year [8] People with diabetes suffer a higher risk of developing peripheral arterial disease (PAD), claudication and foot ulcers. Moreover, people with diabetes tend to have a more severe PAD causing a higher risk of needing amputations. To avoid these situations it is important to monitor the condition of the foot so that early diagnosis and treatment is possible. [1] PAD is caused by narrowing of the arteries supplying blood to the lower extremities. Consequently the extremities do not get sufficient amount of blood and oxygen, which further can cause pain and complications. However, there are also patients with PAD which do not have any symptoms. The standard in diagnosing PAD is to perform an ankle-brachial blood pressure index (ABI) measurement, which means that a pressure measurement is performed both at the brachial artery in the arm and at the ankle. The relation between these two yields the ABI. [1] People with diabetes can develop vascular calcification. Patients who have developed vascular calcification can further suffer from incompressible tibial arteries which in turn causes complications when trying to perform ankle pressure measurements on these patients. When the cuff is placed on the ankle and inflated the vessels will not occlude completely, which can lead to falsely elevated or falsely normal ABI. In these cases, a more accurate pressure measurement can be achieved by placing the cuff onto the toe and perform a toe pressure measurement instead. The reason why a more accurate measure can be obtained is due to the fact that the vessels inside the toe are not as likely to be incompressible. Furthermore, since it is a peripheral disease the cuff should be placed at a peripheral location to enable detection as soon as possible. [1] Critical limb ischemia (CLI) is a progression of PAD and can in severe cases lead to death. It has been reported that one year after diagnosis the mortality rate is 20 % and keeps increasing over the years. To diagnose CLI the toe blood pressure should be below 30 mmhg, which means that the technique has to be able to perform reliably in this region concerning very low pressures. [1]

21 2.4 The Physiology Behind Artifacts and Differences in Signal Appearance 9 LDF is therefore suited for this task since it has proved to be a useful technique for detection of toe blood pressure, especially when low pressures need to be detected. [2] Comparison to Other Techniques The main techniques used to evaluate toe perfusion are photoplethysmography (PPG) a technique which detects the return of the pulsations, and LDF [9]. Research has been made aiming to compare LDF and PPG regarding toe blood pressure measurements. It shows that LDF has a better ability to measure the return of blood flow at low blood pressures compared to PPG. This is most evident when the return of blood flow does not contain any pulsations. In some cases it was found impossible to measure the return of blood flow using PPG while using LDF it was possible. Moreover, it has been found that LDF is less sensitive to movement artifacts than PPG. [2] Another related technique which could be considered for the flow measurement is the ultrasound Doppler, a technique typically used to examine larger vessels. However, in the microcirculation the blood flow velocity is much lower which thereby only give rise to a small frequency shift, which is too small to detect with ultrasound. [6] In addition, even though the main area of LDF is to measure toe blood pressure, it has been shown that it also has advantages in ankle blood pressure measurement compared to ultrasound Doppler. LDF has a lower interoperator variability and the pressure measurements are easier to perform due to that no pulse has to be located which is the case when using ultrasound Doppler. [10] 2.4 The Physiology Behind Artifacts and Differences in Signal Appearance When performing a blood pressure measurement using laser Doppler flowmetry many artifacts and differences in the appearance of the signal can occur making it harder to automatically detect the return of the blood flow. This section will present some of the documented differences and the reason why they occur. One of the characteristics which can be seen in a blood flow curve is the biphasic curve pattern, as depicted in figure 2.6. This pattern occurs when the cuff is deflated and the blood flow returns and flow back into the connected vessel tree, hence the first rise. However, the blood in the veins has not yet started to flow and thereby temporarily slows down the re-flow into the capillaries, though once the venous blood starts to flow the capillary re-flow can continue freely giving rise to the second increase. The biphasic curve is not always present, especially when the arterial pressure is low and the venous pressure is of the same order. This causes the two phases to merge together so that the pattern cannot be distinguished. [11] Another characteristics which can be seen in the blood flow curve is that the flow signal continues to pulsate even after a high pressure has been added, which

22 10 2 Theoretical Background Figure 2.6: A pressure and blood flow curve showing a biphasic curve pattern can be seen in Figure 2.7. This occurs when the vessels are not occluded completely and this is a condition present amongst patients suffering from incompressible vessels. [11] Figure 2.7: A pressure and blood flow curve showing no occlusion. Furthermore, motion artifacts can appear in the signal, which can be both

23 2.4 The Physiology Behind Artifacts and Differences in Signal Appearance 11 due to motion of the tissue at the measuring site and due to movement of the optical fiber if this is used in the instrument. The tissue motion can be due to both muscle contractions as well as the heart s pumping. Moreover, the blood pumped by the heart creates pressure waves in the arteries which can affect the surrounding tissue to obtain a pulsatial motion. If the probe is placed close to the cuff, pulsations can occur at the measuring site without the presence of blood flow. [6] It is possible to locally heat the site where the blood pressure measurement is performed using a heated laser Doppler probe. This is done to see a clearer increase of the perfusion value once the blood flow returns after occlusion. Adding heat causes vasodilation which in turn increases the skin s blood flow and can be seen by an increasing perfusion value. [12] The appearance of the heating mechanism consists of a rapid increase followed by a decrease to then slowly increase again to reach a stable plateau [13].

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25 3 Method This chapter describes the procedure of the project. First a database was set up, then the signals were investigated and the different problems were listed. Thereafter programming and testing were performed iteratively until a satisfactory algorithm had been developed. Throughout the report, it is occasionally referred to the perfusion baseline which is the baseline before occlusion and it is also referred to the occlusion baseline which is the baseline during occlusion. Another concept is the pressure plateau which is the time period when the pressure is kept constant to ensure that the occlusion baseline has time to stabilize. All these concepts are visualized in Figure Database To develop an algorithm several signals were needed to perform both developing and testing of the algorithm. These signals had to represent preferably all different kinds of appearances and artifacts to make the algorithm robust. Therefore, many signals were used, totally 438 from 29 patients, from both arm, ankle and toe. Approximately half of the signals were used for development and all signals were used for testing. The signals used had previously been obtained by the company. All the signals were converted into a format readable by Matlab and the corresponding blood pressures detected by the existing algorithm were stored to simplify the comparison at the end. 13

26 14 3 Method Figure 3.1: A pressure and blood flow curve showing the perfusion baseline, occlusion baseline, pressure plateau and the return of flow. 3.2 Investigation of Signals When the database had been established an investigation of the signals appearances started. The signals were visually inspected and a list was produced consisting of artifacts and other special cases that the algorithm should handle. The list is presented below together with why it may cause problems for the signal processing. Biphasic curve - Even though the blood flow has returned, the perfusion value does not have a continuous increase, as depicted in Figure 2.6 and explained in Section 2.4. Biphasic artifacts - A temporary rise in the occlusion baseline, before the actual blood flow returns. This causes a problem since the analysis have to be performed in real time making it difficult to determine if the rise in perfusion value is due to return of blood flow or due to a biphasic artifact, see Figure 3.2. Varying perfusion baseline - The perfusion value varies a lot before occlusion, which can be seen in Figure 3.3. This causes a problem if the algorithm uses the perfusion value before inflation as a parameter when detecting the return of blood flow. Spikes - Artifacts can also appear as spikes in the signal. These spikes often include several samples making it more difficult for the algorithm to know when to disregard them.

27 3.2 Investigation of Signals 15 Figure 3.2: A pressure and blood flow curve showing a biphasic artifact. Figure 3.3: A pressure and blood flow curve showing a varying perfusion baseline. Absolute perfusion value - There are large differences between patients perfusion values. Sometimes the absolute value can be as high as 500 PU and sometimes it can be as low as 5 PU. Therefore it is difficult to decide a static threshold value for the return of the blood flow. Low perfusion baseline - Some patients have very low perfusion value be-

28 16 3 Method fore pressure is added, below 15 PU. This makes the detection of the return of flow more uncertain since it can be hard to see a clear increase in perfusion value. No occlusion - Continuous pulsations even though pressure is added, as seen in Figure 2.7. In these cases there is no occlusion, hence it is not possible to detect the return of the blood flow. Therefore the algorithm should not output a value, just warn the operator that detection was not possible due to no occlusion. Moreover, it has been noticed that this mostly occur in ankle blood pressure measurement. This was expected since the vessels in the ankle have a tendency to be incompressible for people with diabetes as described in Section Varying slope - How fast the perfusion returns to its normal level after occlusion varies highly between patients. The angle of the slope when the blood flow returns can be anything between a few degrees up to 90 degrees, for this reason no threshold could be used concerning the speed of elevation after occlusion. Such a threshold would have been useful to disregard spikes or to disregard floating changes in the baseline. See Figure 3.4 and 3.5 for examples of a rapid respectively slow increase. Figure 3.4: A pressure and blood flow curve showing rapid return of blood flow. Low perfusion after occlusion - Even though the blood flow has returned, indicated by the return of pulsations, the perfusion value does not increase significantly within the time period of detection. This is the case for the signal shown in Figure 3.5.

29 3.2 Investigation of Signals 17 Figure 3.5: A pressure and blood flow curve showing an initially low perfusion after the occlusion which thereby also gives an initially small slope. In this case the blood flow returns at around 50 seconds, however looking closely reveals some pulsations even before that. No pulsations - During perfusion there is often a pulsating blood flow. However, occasionally the situation in Figure 3.6 occurs where the blood flow is not pulsating. The detection can therefore not solely rely on an algorithm based on pulsations. Missing pulsations - The flow is pulsating during the perfusion baseline, however at first when the flow returns there are no pulsations in the signal but they tend to appear later, as depicted in Figure 3.7. Therefore, by using pulsations in the algorithm, in cases like this, the algorithm will detect the return of flow a bit later than when it actually returns. Small pulsations without increasing mean value - Figure 3.5 demonstrates small pulsations occurring early in the occlusion baseline without an increase in the mean value. These pulsations are probably due to arterial pressure waves in the surrounding tissue, as explained in Section 2.4. These pulsations have the same frequency as the pulsating blood making it hard to distinguish whether the blood flow has returned or not. Bad signal - The signal can be so noisy that the return of blood flow cannot be detected. No detection is wanted in this kind of signal, only a warning should be produced. See Figure 3.8 for an example of how bad a signal can be. Heating - When heating, as described in Section 2.4, is used the perfusion

30 18 3 Method Figure 3.6: A pressure and blood flow curve where no pulsations are present. Figure 3.7: A pressure and blood flow curve where the blood flow returns (time 60 s) but the pulsations are initially missing. baseline can be increasing, as depicted in Figure 3.9. Algorithms using the perfusion baseline for detection hence need to take this into consideration. Noise - The signals always contain some level of noise. However, sometimes the noise is so significant that it can be difficult to interpret where the actual return of flow occurs.

31 3.2 Investigation of Signals 19 Figure 3.8: A pressure and blood flow curve showing an example of a signal where detection is not possible. Figure 3.9: A pressure and blood flow curve showing the effect of heating. Absolute pressure - There are large differences in blood pressure between patients. Sometimes the absolute value can be as high as 200 mmhg and sometimes it can be as low as 20 mmhg. Otherwise, this could have been an approach for disregarding the biphasic artifacts which often appear early in the signal when the pressure is still very high.

32 20 3 Method Tilting occlusion baseline - When the flow has not had time to stabilize during occlusion, what should have been a flat occlusion baseline is instead sloping down as seen in Figure Moreover, there are also a few cases where the occlusion baseline is sloping upwards as seen in Figure As expected, these artifacts make it harder to set the occlusion baseline correctly. Figure 3.10: A pressure and blood flow curve showing a down sloping occlusion baseline. Each of these artifacts and difference in appearance can often be quite easy to handle one by one. However, when all of these are combined the degree of difficulty increases tremendously, especially since the type of artifact is not known beforehand due to the requirement of real time analysis. For example how can you differentiate between a fast increasing blood flow and a spike. Another example is the combined problem with differences in perfusion value when the blood flow returns and biphasic artifacts, since a biphasic artifact can in some cases consist of an increase in perfusion value by 100 PU and sometimes an actual return of blood flow consists only of a 20 PU increase. 3.3 Development of Algorithms Before finding the final approach, several other things were tried to get a better understanding of what was required by the algorithm. This included things such as the use of derivative for detecting change in appearance, cumulative sum for detecting change in mean, static model curve and modifications of the existing algorithm which used threshold values only based on the occlusion baseline.

33 3.3 Development of Algorithms 21 Figure 3.11: A pressure and blood flow curve showing an up sloping occlusion baseline. The initial aim was trying to create an algorithm which could be used for almost all of the curves. Therefore the first approach with promising results was to create an algorithm which took advantage of the elevated perfusion value when the blood flow returns. Moreover, the algorithm was made to not rely on pulsations, since they are not present in all curves. This first algorithm turned out to use a model curve for detection. However, the model curve had difficulties with spikes, biphasic artifacts and low elevation of the perfusion value. Therefore two more algorithms were developed which could handle these artifacts. The second algorithm also used the elevation characteristic, but was instead based on threshold values for increasing mean. The third algorithm used pulsations, since it was found that a sufficient elevation was not always present. All three algorithms had different advantages and drawbacks, and none of them could handle all types of curves. Therefore two combinations of them were created with the hope to reduce the amount of misclassifications. Below are more detailed descriptions of all three individual algorithms as well as how they were combined. However, due to company confidentiality all details cannot be revealed. Additional to the detection of flow return, a warning system has been implemented. It aims to warn the operator if there are large uncertainties concerning the detection, for example a bad curve fit, no clear baseline and so on. Depending on the approach, all the algorithms have different levels of robustness in their warning systems. The warning system is further described below.

34 22 3 Method Algorithm 1 - Model Curve The first algorithm consists of a model curve which is fitted to the flow signal to detect when the blood flow returns, see Figure The model curve consists of two lines which are joined together. One of the lines is horizontal which aims to match the occlusion baseline, while the other line has a slope and aims to fit the blood flow once it has returned. The point where the two lines intersect aims to match the point in time when the blood flow returns. Figure 3.12: A pressure and blood flow curve showing a fit of the model curve. The basic concept is an iterative fitting where both the slope of the model curve as well as the location in time is changed to see when the best fit occurs. The best fit is decided by looking at when in time the square error of the fitting is minimized for the different slopes. When the perfusion value has reached 40 % of the perfusion baseline level, the least squares error is decided. Meaning that the time delay for detection varies between the signals and depends on the slope of the flow signal. The first step in this algorithm is to detect a perfusion baseline by taking the mean of an interval, corresponding to five seconds, right before the inflation. Then an occlusion baseline is decided, this is simply done by taking the mean of an interval, corresponding to five seconds, after the inflation. Even though an occlusion baseline has been set, a potential new one is calculated and if the new one has a lower value than the previous, the previous gets replaced by the new. This makes it possible to handle down sloping occlusion baselines. Then the model curve is created, where the horizontal line is given the same height as the occlusion baseline. The slope of the second line is based on the difference between the perfusion and occlusion baseline. The height is consistent and

35 3.3 Development of Algorithms 23 instead the length along the horizontal axis is altered resulting in five different slopes. The warning system of the model curve consists of warnings regarding unstable occlusion baseline, low perfusion, low perfusion difference, no occlusion and bad fit, all of which are described below Algorithm 2 - Threshold This algorithm is based on the existing algorithm, however some changes have been made. The main purpose with this approach was to see whether small changes can yield improved results, thereby having the advantage of being cost efficient from the company s perspective. The algorithm is based on the mean value of four consecutive intervals. Thereafter several requirements are made concerning a continuous elevation of the mean perfusion which are based on the level of the occlusion and the perfusion baseline. The time point when all requirements are fulfilled corresponds to the time when the blood flow returns. The reliability of the algorithm has also been improved by adding the warning system concerning unstable occlusion baseline, low perfusion, low perfusion difference and warning if the occlusion is not sufficient Algorithm 3 - Pulsation Detection The pulsation algorithm starts by filtering the signal using a bandpass filter with cut-off frequencies 0.8 to 4 Hz, see Figure Thereafter peak detection is performed to calculate the time period between the peaks and hence the frequency during the perfusion baseline. To make the algorithm more robust against noise both minimum and maximum peaks are used to calculate the frequency. Furthermore, there are also requirements on the peaks absolute amplitudes, which aim to disregard noise in the occlusion baseline. The peak detection continues and once it is found that three consecutive periods, based on minimum as well as maximum peaks, have approximately the same frequency as the peaks during the perfusion baseline, the return of blood flow is set to when the first of these peaks occurs. This algorithm includes warnings regarding unstable occlusion baseline and insufficient occlusion Combined Algorithm 1 - Model Curve and Pulsations The first combined algorithm performs model curve fitting and pulsation detection in parallel. The aim is to better handle signals where the returning blood flow is manifested by clear pulsations but with slowly increasing perfusion values since those signals cannot be handled by the model curve alone. Moreover, there is a requirement combined with the pulsation algorithm stating that the mean value of the perfusion has to increase for detection to occur. This requirement is based on the same principle as the threshold algorithm and aims to disregard noise in the signal. Since the model curve algorithm and the pulsation algorithm

36 24 3 Method Figure 3.13: A blood pressure measurement and the corresponding filtered perfusion signal. might detect the return of blood flow at different times, a schema has been created to choose whether to trust the pulsation detection or the model curve, see Figure A system consisting of the same warnings as for the model curve exists for this algorithm as well, except for the case when there is a bad fit using the model curve. Instead of producing a warning, this algorithm first checks if the pulsation detection has located a return of blood flow and if that is the case that result is used. However if no pulsations are present then a warning about bad fit will be produced Combined Algorithm 2 - Threshold and Pulsations The second combined algorithm makes use of the threshold algorithm and pulsation algorithm independently. The aim of this combined algorithm is the same as for the first combined algorithm but using the benefits of the threshold algorithm especially concerning spikes and noise in the signal. If only one of the included algorithms detects a return of flow, that one is used. However, if both algorithms detect the returning blood flow, the one with the earliest detection is chosen. Note that the pulsation part must also include a small rise in the perfusion to be classified as the return of flow in the same way as for the first combined algorithm. Furthermore, this algorithm includes the same warning system as the threshold algorithm.

37 3.3 Development of Algorithms 25 Figure 3.14: A schema visualizing the decision making weather to trust the pulsations or the model curve Warning system The main purpose of the warning system is to reduce the risk of false detection and thereby reduce the risk of wrongly made diagnosis if the operator is not attentive. Below is a list presenting the different warnings and what triggers them. Bad fit - A warning occurs when the model curve is badly fitted to the flow curve. This is decided when the square error of the first line of the model curve exceeds a certain value. The reason why the second line of the model

38 26 3 Method curve is not included is due to the fact that it should be possible to get a good fit even if there are pulsations present in the signal when the blood flow returns. Unstable occlusion baseline - This warning occurs if the baseline is very unstable or noisy, the trigger is based on a threshold value for the variance of the occlusion baseline. Low perfusion - If the perfusion baseline is very low from the beginning it introduces an extra uncertainty since the blood flow is almost invisible. Hence there will be a warning if the perfusion baseline is lower than 15 PU. Low perfusion difference - If the difference between the perfusion baseline and occlusion baseline is less than 15 PU there will be a warning because such a small difference introduces a large uncertainty in the detection. No occlusion - If there is not a sufficient occlusion no return of flow can be detected. This warning is simply based on the difference between the maximum inflation pressure and the detected blood pressure. 3.4 Comparison between the Algorithms When the combined algorithms were considered complete, for the extent of this project, they were compared to the existing algorithm to be able to draw any conclusions about if the new algorithms were better and in that case how much. Moreover, the three basic algorithms were compared as well to give an independent answer to which algorithm that can handle which type of artifacts and signals. The comparison was made in the following way. First, the return of blood flow was decided manually by visual inspection to have a true value to compare against. In cases where the flow signal was difficult to interpret, two experts at Perimed AB were consulted. Then the stored signals were evaluated using all algorithms and the resulting blood pressure value was compared to the visually decided value. Moreover, the blood pressure retrieved when the measurements were obtained using the existing algorithm in the system was also compared to the visually decided value. In some cases during the visual inspection it was decided that no blood pressure should be detected but instead the algorithm should produce a warning. Therefore the testing also included if the algorithm produced a warning when it should.

39 4 Results In this chapter the result from the testing is presented. At first the results for the different algorithms are given individually, to be followed by a comparison based on tables and histograms. The results in this thesis are partly presented in terms of false detection which is defined as when the error is larger than 10 mmhg. The limit of 10 mmhg was chosen since it is considered to have an important impact in clinical practice [9]. Note that detection is only performed if no warning is produced for the signal. False detection is of interest since it can lead to a false diagnosis of the patient. The signals where visual detection were not possible, and the desired outcome is a warning, are not included when calculating the percentage of falsely detected signals, since no comparison can be made. For the newly introduced warning system, two main metrics are whether the algorithm produces a warning when desired and how many extra warnings it gives. In total 438 signals were evaluated, out of those 45 should clearly result in a warning and not a detected blood pressure. For the remaining 393 signals, the return of flow could be visually detected but in some cases the system was not handled correctly causing artifacts such as down sloping occlusion baselines, heating and insufficient occlusion. The result from these cases of improper handling were considered valid either if a detection was performed or if an extra warning was produced. Furthermore, if the signal was too short before inflation then it was impossible to determine a frequency used for comparison in the pulsation detection. This lead to a warning, but was considered valid as for the cases above. 27

40 28 4 Results 4.1 Existing Algorithm For 13.5 % of the signals the detection was more than 10 mmhg off. In most cases that were due to problems with the occlusion baseline. This was mainly caused by small fluctuations in the occlusion baseline. Other reasons for false detections due to problems with the occlusion baseline included spikes, biphasic artifacts, noise and upslopes. If the return of flow was characterised by small pulsations then the algorithm detected the return too late since the mean value of the perfusion did not increase until later. However, overall it performed well with signals where the perfusion level only increased slowly when the flow returned. The existing algorithm performed detection on all signals even those where it was not wanted. The reason for this is that the algorithm does not contain a warning system. 4.2 Algorithm 1 - Model Curve The model curve falsely detected the return of flow in 16.7 % of the cases. When a slow rise, either with or without pulsations, characterised the return of flow, were the situations the algorithm had problems with, see Figure 4.1. It also had difficulties when there were spikes in the occlusion baseline and additional problems related to the occlusion baseline were cases with an upslope, as depicted in Figure 4.2 and 4.3. The model curve performed well in cases of small fluctuations and noise in the occlusion baseline. Figure 4.1: A blood measurement showing a bad fit of the model curve due to a slow rise with pulsations. The extra warnings mainly appeared as a result of a bad fit of the model curve.

41 4.2 Algorithm 1 - Model Curve 29 Figure 4.2: A blood measurement showing a false detection by the model curve due to spikes. Figure 4.3: A blood measurement showing a false detection by the model curve due to an up sloping occlusion baseline. This was in turn caused by two cases, either related to the return of flow or to the occlusion baseline. Regarding the return of flow, problems arose when there was a slow rise of the perfusion with or without pulsation. Problems related to the occlusion baseline consisted of biphasic artifacts, spikes, and upslopes.

42 30 4 Results There were two cases where the warning failed to occur. These were signals which were noisy but still could be segmented into a perfusion and occlusion baseline followed by a rise of the perfusion. Still, it was visually difficult to determine exactly where the blood flow returned. 4.3 Algorithm 2 - Threshold There was a false detection made by the threshold algorithm for 12.0 % of the curves. Almost all of these were due to a low rise of the perfusion once the blood flow returned, this includes a low rise both with and without pulsations. The threshold algorithm was good at handling both spikes, small fluctuations, biphasic artifacts and noise. The extra warnings were mainly cases when the algorithm could not detect a return of flow due to a too low rise of the perfusion, both with and without pulsations. There were eight cases where no warning was produced even though it was desired since the signal was too bad for even a visual detection to be performed. 4.4 Algorithm 3 - Pulsations In 14.1 % of the curves the detection was performed falsely, which was mainly due to two reasons. The first one being that the return of flow started with a rise without pulsations and then a while later the pulsations appeared, causing the detection to occur too late. The other reason was that noise in the signal was falsely detected as pulsations. This algorithm was good at handling signals where the return of flow was characterized by pulsations, which also was the purpose of the algorithm. Furthermore, it performed well in the presence of small fluctuations and spikes. There were as many as 112 extra warnings produced by the pulsation algorithm. 73 of these were because no return of flow could be found by the algorithm, which were due to several reasons. The main reason being that there were no pulsations present in the signal. Other causes were that the frequency difference before occlusion and after were too big and simply that the signal itself was too bad to detect pulsations during the perfusion baseline. In three case the algorithm failed to produce a warning. These were all cases that should have warned about a too low perfusion value to start with. This is simply explained by the fact that this algorithm never calculates the level of the perfusion baseline. 4.5 Combined Algorithm 1 - Model Curve and Pulsations This algorithm falsely detected the return of flow in 7.6 % of the cases. The main cause for this was the presence of spikes. There were also some cases with upslopes as well as low rises without pulsations. Moreover, there are some cases