No description available.

This thesis work presents the analysis of Oscillometric blood pressure pulse waveform under different pressure points (Systolic, Mean Arterial, and Diastolic Pressures). Pulse waveforms' characteristics were determined from the waveforms at three different pressures and are compared for subjects at three different age groups. Estimation of blood pressure using a morphology based approach was done by using the change of pulse waveform characteristics at different pressure points. Pulse waveforms' characteristics that were obtained from pulse waveforms are utilized to estimate SBP, MAP, and DBP. The estimates obtained with pulse morphology based technique are compared with a BP measurement device and Maximum Amplitude Algorithm. Maximum slope of the pulse was also used for blood pressure estimation. The effect of movement and breathing on proposed method and MAA were compared and it was observed that breathing artifacts affect less the proposed method.

MPH / Department of Public Health

Blood pressure is one of the most important indicators of human health. The symptoms of many cardiovascular diseases like stroke, atrial fibrillation, and acute myocardial infarction are usually indicated by the abnormal variation of blood pressure. Severe symptoms of diseases like coronary syndrome, rheumatic heart disease, arterial aneurysm, and endocarditis also usually appear along with the variation of blood pressure. Most of the current blood pressure measurements rely on the Korotkoff sounds method that focuses on one-time blood pressure measuring but cannot supervise blood pressure continuously, which cannot effectively detect diseases or alert patients. Previous researches indicating the relationship between photoplethysmogram (PPG) signal and blood pressure brought up the new research direction of blood pressure measurement method. Ideally, with the continuous supervision of the PPG signal, the blood pressure of the subject could be measured longitudinally, which matches the current requirements of blood pressure measurement better as an indicator of human health. However, the relationship between blood pressure and PPG signal is very comprehensive that is related to personal and environmental status, which leads to the research challenge for many previous works that tried to find the mapping from PPG signal to blood pressure without considering other factors. In this thesis, we propose two statistical methods modeling the comprehensive relationships among blood pressure, PPG signals, and other factors for blood pressure prediction. We also express the modeling and predicting process for the real data set and provide accurate prediction results that achieve the international blood pressure measurement standard. In the first part, we propose the Independent Variance Components Mixed- model (IVCM) that introduces the variance components to describe the relationship among observations. The relationship indicators are collected as information to divide observations into different groups. The latent impacts from the properties of groups are estimated and used for predicting the multiple responses. The Stochastic Approximation Minorization-maximization (SAM) algorithm is used for IVCM model parameter estimation. As the expansion of Minorization-maximization (MM) algorithm, the SAM algorithm could provide comparable-level estimations as MM algorithm but with faster computing speed and less computational cost. We also provide the subsampling prediction method for IVCM model prediction that could predict multiple responses variables with the conditional expectation of the model random effects. The prediction speed of the subsampling method is as fast as the SAM algorithm for parameter estimation with very small accuracy loss. Because the SAM algorithm and subsampling prediction method requires assigning tuning parameters, a great amount of simulation results are provided for the tuning parameter selection. In the second part, we propose the Groupwise Reweighted Mixed-model (GRM) to describe the variation of random effects as well as the potential components of mixture distributions. In the model, we combine the properties of mixed-model and mixture model for modeling the comprehensive relationship among observations as well as between the predictive variables and the response variables. We bring up the Groupwise Expectation Minorization-maximization (GEM) algorithm for the model parameter estimation. Developed from MM algorithm and Expectation Maximization (EM) algorithm, the algorithm estimates parameters fast and accurate with adopting the properties of the diagonal blocked matrix. The corresponding prediction method for GRM model is provided as well as the simulations for the number of components selection. In the third part, we apply the IVCM model and the GRM model in modeling real data and predicting blood pressure. We establish the database for modeling blood pressure with PPG signals and personal characteristics, extract PPG features from PPG signal waves, and analyze the comprehensive relationship between PPG signal and blood pressure with the IVCM model and the GRM model. The blood pressure prediction results from different models are provided and compared. The best prediction results not only achieve the international blood pressure measurement standard but also show great performance in high blood pressure prediction

Blood pressure is one of the most important indicators of human health. The symptoms of many cardiovascular diseases like stroke, atrial fibrillation, and acute myocardial infarction are usually indicated by the abnormal variation of blood pressure. Severe symptoms of diseases like coronary syndrome, rheumatic heart disease, arterial aneurysm, and endocarditis also usually appear along with the variation of blood pressure. Most of the current blood pressure measurements rely on the Korotkoff sounds method that focuses on one-time blood pressure measuring but cannot supervise blood pressure continuously, which cannot effectively detect diseases or alert patients. Previous researches indicating the relationship between photoplethysmogram (PPG) signal and blood pressure brought up the new research direction of blood pressure measurement method. Ideally, with the continuous supervision of the PPG signal, the blood pressure of the subject could be measured longitudinally, which matches the current requirements of blood pressure measurement better as an indicator of human health. However, the relationship between blood pressure and PPG signal is very comprehensive that is related to personal and environmental status, which leads to the research challenge for many previous works that tried to find the mapping from PPG signal to blood pressure without considering other factors. In this thesis, we propose two statistical methods modeling the comprehensive relationships among blood pressure, PPG signals, and other factors for blood pressure prediction. We also express the modeling and predicting process for the real data set and provide accurate prediction results that achieve the international blood pressure measurement standard. In the first part, we propose the Independent Variance Components Mixed- model (IVCM) that introduces the variance components to describe the relationship among observations. The relationship indicators are collected as information to divide observations into different groups. The latent impacts from the properties of groups are estimated and used for predicting the multiple responses. The Stochastic Approximation Minorization-maximization (SAM) algorithm is used for IVCM model parameter estimation. As the expansion of Minorization-maximization (MM) algorithm, the SAM algorithm could provide comparable-level estimations as MM algorithm but with faster computing speed and less computational cost. We also provide the subsampling prediction method for IVCM model prediction that could predict multiple responses variables with the conditional expectation of the model random effects. The prediction speed of the subsampling method is as fast as the SAM algorithm for parameter estimation with very small accuracy loss. Because the SAM algorithm and subsampling prediction method requires assigning tuning parameters, a great amount of simulation results are provided for the tuning parameter selection. In the second part, we propose the Groupwise Reweighted Mixed-model (GRM) to describe the variation of random effects as well as the potential components of mixture distributions. In the model, we combine the properties of mixed-model and mixture model for modeling the comprehensive relationship among observations as well as between the predictive variables and the response variables. We bring up the Groupwise Expectation Minorization-maximization (GEM) algorithm for the model parameter estimation. Developed from MM algorithm and Expectation Maximization (EM) algorithm, the algorithm estimates parameters fast and accurate with adopting the properties of the diagonal blocked matrix. The corresponding prediction method for GRM model is provided as well as the simulations for the number of components selection. In the third part, we apply the IVCM model and the GRM model in modeling real data and predicting blood pressure. We establish the database for modeling blood pressure with PPG signals and personal characteristics, extract PPG features from PPG signal waves, and analyze the comprehensive relationship between PPG signal and blood pressure with the IVCM model and the GRM model. The blood pressure prediction results from different models are provided and compared. The best prediction results not only achieve the international blood pressure measurement standard but also show great performance in high blood pressure prediction

No description available.

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.

Chan Ka Wing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references. / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Objectives --- p.1 / Chapter 1.2 --- Definitions --- p.2 / Chapter 1.2.1 --- Definition of blood pressure --- p.2 / Chapter 1.2.2 --- Definition of hypertension --- p.3 / Chapter 1.3 --- Problems related to hypertension --- p.4 / Chapter 1.4 --- The importance of measuring blood pressure --- p.4 / Chapter 1.4.1 --- Self-measurement of blood pressure --- p.5 / Chapter 1.4.2 --- Ambulatory blood pressure measurement --- p.5 / Chapter 1.5 --- Review of blood pressure measurement techniques --- p.7 / Chapter 1.5.1 --- The invasive method --- p.7 / Chapter 1.5.2 --- Noninvasive methods --- p.8 / Chapter 1.6 --- Review of currently available blood pressure meters --- p.15 / Chapter 1.7 --- Prevalence of hypertension --- p.19 / Chapter 1.7.1 --- Hong Kong --- p.19 / Chapter 1.7.2 --- Worldwide --- p.20 / Chapter 1.8 --- The market for blood pressure meters --- p.21 / Chapter 1.9 --- Organization of the thesis --- p.22 / References --- p.24 / Chapter Chapter 2 --- Measurement of the ECG-PPG interval --- p.30 / Chapter 2.1 --- Introduction --- p.30 / Chapter 2.1.1 --- Pulse transit time (PTT) --- p.30 / Chapter 2.1.2 --- Electrocardiogram (ECG) --- p.36 / Chapter 2.1.2.1 --- Measurement of the ECG signal --- p.37 / Chapter 2.1.3 --- Photoplethysmography (PPG) --- p.38 / Chapter 2.1.3.1 --- Measurement of the PPG signal --- p.41 / Chapter 2.1.4 --- Measurement of blood pressure by ECG-PPG interval --- p.43 / Chapter 2.2 --- Source of errors for measurement of the ECG-PPG interval --- p.44 / Chapter 2.2.1 --- Effects of variability of ECG-PPG intervals --- p.44 / Chapter 2.2.2 --- Effects of bending the arm --- p.49 / Chapter 2.2.3 --- Effects of an external force --- p.54 / Chapter 2.3 --- Conclusion --- p.60 / References --- p.62 / Chapter Chapter 3 --- Cuffless and Noninvasive Measurement of Blood Pressure --- p.68 / Chapter 3.1 --- Introduction --- p.68 / Chapter 3.2 --- Effects of subject-dependent calibration --- p.74 / Chapter 3.3 --- Effects of different time intervals --- p.81 / Chapter 3.4 --- The impact of using different Q-P intervals --- p.96 / Chapter 3.5 --- Real-time measurement of blood pressure --- p.104 / Chapter 3.6 --- Conclusion --- p.108 / References --- p.110 / Chapter Chapter 4 --- Motion Artifact Reduction from PPG Recordings in Ambulatory Blood Pressure Measurement --- p.114 / Chapter 4.1 --- Introduction --- p.114 / Chapter 4.2 --- Previous works --- p.115 / Chapter 4.3 --- Theory --- p.116 / Chapter 4.3.1 --- The adaptive filter --- p.117 / Chapter 4.3.2 --- Variation of step-size parameters --- p.119 / Chapter 4.3.3 --- Effects of filter length --- p.120 / Chapter 4.4 --- Experiment --- p.121 / Chapter 4.5 --- Results --- p.123 / Chapter 4.6 --- Discussion --- p.131 / Chapter 4.7 --- Conclusion --- p.133 / References --- p.135 / Chapter Chapter 5 --- Measurement of Blood Pressure using the PPG signal --- p.138 / Chapter 5.1 --- Introduction --- p.138 / Chapter 5.2 --- Theory --- p.138 / Chapter 5.3 --- Experiment --- p.142 / Chapter 5.3.1 --- Multiple linear regression (MLR) --- p.142 / Chapter 5.3.2 --- Artificial neural networks (ANNs) --- p.146 / Chapter 5.3.3 --- Results --- p.149 / Chapter 5.3.4 --- Discussion --- p.152 / Chapter 5.4 --- The implementation of the Q-P interval --- p.153 / Chapter 5.4.1 --- Results --- p.154 / Chapter 5.4.2 --- Discussion --- p.156 / Chapter 5.5 --- Conclusion --- p.157 / References --- p.158 / Chapter Chapter 6 --- Conclusion and Future Studies --- p.160 / Chapter 6.1 --- Major contributions --- p.160 / Chapter 6.2 --- Future studies --- p.162 / References --- p.165 / Appendix I --- p.166

Liu, Qilin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 97-110). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese; appendixes includes Chinese.

Lee Chi Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 112-121). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Objectives --- p.1 / Chapter 1.2 --- Blood Pressure --- p.2 / Chapter 1.3 --- Hypertension --- p.3 / Chapter 1.3.1 --- Definition of Hypertension --- p.3 / Chapter 1.3.2 --- Causes and Symptoms of Hypertension --- p.3 / Chapter 1.3.3 --- Complication of Hypertension --- p.4 / Chapter 1.3.4 --- Prevalence of Hypertension --- p.4 / Chapter 1.4 --- Blood Pressure Measurement --- p.5 / Chapter 1.4.1 --- History --- p.5 / Chapter 1.4.2 --- Techniques and Methods --- p.7 / Chapter 1.4.3 --- Current Devices --- p.13 / Chapter 1.5 --- Organization of the Thesis --- p.16 / Chapter Chapter 2 --- Theory --- p.18 / Chapter 2.1 --- Introduction --- p.18 / Chapter 2.2 --- Blood Rheology --- p.18 / Chapter 2.2.1 --- Blood Composition --- p.18 / Chapter 2.2.2 --- Flow Properties of Blood --- p.19 / Chapter 2.2.3 --- Blood Vessels --- p.21 / Chapter 2.3 --- Principle of the PTT-Based Blood Pressure Measurement --- p.22 / Chapter 2.3.1 --- Wave Propagation in Blood Vessels --- p.22 / Chapter 2.3.2 --- Pulse Transit Time (PTT) --- p.27 / Chapter 2.3.3 --- Blood Pressure Measurement Based on PTT --- p.31 / Chapter 2.4 --- Effects of Temperature on Blood Pressure --- p.34 / Chapter 2.4.1 --- Human Body Temperature Regulation --- p.34 / Chapter 2.4.2 --- Physiological Responses to Decreased Temperature --- p.36 / Chapter 2.4.3 --- Effects of Temperature on Blood Pressure --- p.38 / Chapter 2.5 --- Possible Effects of Temperature on PTT-Based Blood Pressure Measurement --- p.47 / Chapter 2.5.1 --- Windkessel Model --- p.47 / Chapter 2.5.2 --- Phase Velocity --- p.49 / Chapter 2.5.3 --- Effects of temperature on PTT --- p.52 / Chapter 2.5.4 --- Possible Effects of temperature on PTT-based Blood Pressure Measurement --- p.53 / Chapter 2.6 --- Conclusion --- p.54 / Chapter Chapter 3 --- Algorithms in Calculating Pulse Transit Time: Wavelet-Based and Derivative-Based --- p.55 / Chapter 3.1 --- Introduction --- p.55 / Chapter 3.1.1 --- Wavelet Transform (WT) --- p.56 / Chapter 3.1.2 --- Wavelet Transform Modulus Maxima (WTMM) --- p.58 / Chapter 3.2 --- Experiment --- p.60 / Chapter 3.2.1 --- Subjects --- p.60 / Chapter 3.2.2 --- Equipment and Sensors --- p.61 / Chapter 3.2.3 --- Protocol --- p.61 / Chapter 3.3 --- Methods --- p.62 / Chapter 3.3.1 --- Wavelet-Based Algorithm of PTT Calculation --- p.62 / Chapter 3.3.2 --- Derivative-Based Algorithm of PTT Calculation --- p.65 / Chapter 3.3.3 --- PTT-Based Blood Pressure Estimation --- p.67 / Chapter 3.4 --- Results --- p.68 / Chapter 3.5 --- Discussion --- p.70 / Chapter 3.6 --- Conclusion --- p.72 / Chapter Chapter 4 --- Effects of Ambient Temperature on PTT-Based Blood Pressure Estimation --- p.74 / Chapter 4.1 --- Introduction --- p.74 / Chapter 4.2 --- Experiment --- p.74 / Chapter 4.2.1 --- Subjects --- p.74 / Chapter 4.2.2 --- Equipment --- p.75 / Chapter 4.2.3 --- Protocol --- p.76 / Chapter 4.3 --- Methods --- p.77 / Chapter 4.3.1 --- Features of Photoplethysmographic Signals --- p.78 / Chapter 4.3.2 --- Calculation of Pulse Transit Time (PTT) --- p.78 / Chapter 4.4 --- Results --- p.79 / Chapter 4.4.1 --- "Effects of Ambient Temperature on Blood Pressure, Heart Rate and Finger Skin Temperature" --- p.79 / Chapter 4.4.2 --- Effects of Ambient Temperature on the Features of Photoplethysmographic Signals --- p.82 / Chapter 4.4.3 --- Effects of Ambient Temperature on Pulse Transit Time --- p.84 / Chapter 4.4.4 --- PTT-Based Blood Pressure Estimation --- p.85 / Chapter 4.4.6 --- Evaluation of the Modified Equations of the PTT-Based Blood Pressure Measurement Approach --- p.89 / Chapter 4.5 --- Discussion --- p.94 / Chapter 4.6 --- Conclusion --- p.98 / Chapter Chapter 5 --- Effects of Local Temperature on PTT-Based Blood Pressure Estimation --- p.99 / Chapter 5.1 --- Introduction --- p.99 / Chapter 5.2 --- Methods --- p.99 / Chapter 5.3 --- Results --- p.100 / Chapter 5.3.1 --- "Effects of Local Temperature on Blood Pressure, Heart Rate and Finger Skin Temperature" --- p.100 / Chapter 5.3.2 --- Effects of Local Temperature on Pulse Transit Time --- p.102 / Chapter 5.3.3 --- Effects of Local Temperature on the Features of Photoplethysmographic Signal --- p.103 / Chapter 5.3.4 --- Effects of Local Temperature on PTT-Based Blood Pressure Estimation --- p.104 / Chapter 5.4 --- Discussion --- p.105 / Chapter 5.5 --- Conclusion --- p.107 / Chapter Chapter 6 --- Conclusion and Future Study --- p.108 / Chapter 6.1 --- Major Contributions --- p.108 / Chapter 6.2 --- Future Study --- p.110 / References --- p.112 / Chapter Appendix A --- Motion Artifact Reduction from PPG signal Based on a Wavelet Approach --- p.122 / Chapter A.l --- Introduction --- p.122 / Chapter A.1.1 --- Motion Artifact --- p.122 / Chapter A.1.2 --- Stationary Wavelet Transform (SWT) --- p.123 / Chapter A.2 --- Experiment --- p.124 / Chapter A.2.1 --- Subjects --- p.124 / Chapter A.2.2 --- Equipment --- p.124 / Chapter A.2.3 --- Protocol --- p.125 / Chapter A.3 --- Methods --- p.126 / Chapter A.3.1 --- Algorithm --- p.126 / Chapter A.3.2 --- Data Analysis --- p.128 / Chapter A.4 --- Results --- p.129 / Chapter A.5 --- Discussion --- p.131 / Chapter A.6 --- Conclusion --- p.133 / Reference --- p.133 / Appendix B Derivation of the Moens-Korteweg Equation --- p.134 / Reference --- p.136