Pulse waves contain rich physiological and pathological information of the human vascular system. The pulse wave diagnosis systems are very helpful for the clinical diagnosis and treatment of cardiovascular diseases. Accurate pulse waveform is necessary to evaluate the performances of the pulse wave equipment. However, it is difficult to obtain accurate pulse waveform due to several kinds of physiological and pathological conditions for testing and maintaining the pulse wave acquisition devices. A pulse wave generator was designed and implemented in the present study for this application. The blood flow in the vessel was simulated by modeling the cardiovascular system with windkessel model. Pulse waves can be generated based on the vascular systems with four kinds of resistance. Some functional models such as setting up noise types and signal noise ratio (SNR) values were also added in the designed generator. With the need of portability, high speed dynamic response, scalability and low power consumption for the system, field programmable gate array (FPGA) was chosen as hardware platform, and almost all the works, such as developing an algorithm for pulse waveform and interfacing with memory and liquid crystal display (LCD), were implemented under the flow of system on a programmable chip (SOPC) development. When users input in the key parameters through LCD and touch screen, the corresponding pulse wave will be displayed on the LCD and the desired pulse waveform can be accessed from the analog output channel as well. The structure of the designed pulse wave generator is simple and it can provide accurate solutions for studying and teaching pulse waves and the detection of the equipments for acquisition and diagnosis of pulse wave.
In order to quantitatively analyze the morphology and period of pulse signals, a time-space analytical modeling and quantitative analysis method for pulse signals were proposed. Firstly, according to the production mechanism of the pulse signal, the pulse space-time analytical model was built after integrating the period and baseline of pulse signal into the analytical model, and the model mathematical expression and its 12 parameters were obtained for pulse wave quantification. Then, the model parameters estimation process based on the actual pulse signal was presented, and the optimization method, constraints and boundary conditions in parameter estimation were given. The spatial-temporal analytical modeling method was applied to the pulse waves of healthy subjects from the international standard physiological signal sub-database Fantasia of the PhysioNet in open-source, and we derived some changes in heartbeat rhythm and hemodynamic generated by aging and gender difference from the analytical models. The model parameters were employed as the input of some machine learning methods, e.g. random forest and probabilistic neural network, to classify the pulse waves by age and gender, and the results showed that random forest has the best classification performance with Kappa coefficients over 98%. Therefore, the space-time analytical modeling method proposed in this study can effectively quantify and analyze the pulse signal, which provides a theoretical basis and technical framework for some related applications based on pulse signals.
In this work, we investigated the influence of the bifurcation geometry of the iliac artery on the propagation properties of the pulse wave, and applied software to establish the straight bifurcation and curved bifurcation bi-directional fluid-solid coupling finite element analysis models based on the iliac artery, and compared and analyzed the influence of the bifurcation angle of the blood vessel on the propagation characteristics of the pulse wave. It was found that the bifurcation geometry had a significant effect on the pulse wave propagation in the iliac arteries, and the pressure and velocity pulse wave amplitudes predicted by these two models had a good agreement with that before the vessel bifurcation in a cardiac cycle. The curvilinear bifurcation model predicted the pulse wave amplitude to be lower and the pressure drop to be smaller after the bifurcation, which was more in line with the actual situation of the human body. In addition, the bifurcation point is accompanied by the stress concentration phenomenon in the vessel wall, and there is a transient increase in the velocity pulse waveform amplitude, which was consistent with the fact that the bifurcation site is prone to phenomena such as arterial stenosis and hardening. The preliminary results of this paper will provide some reference for the use of pulse waveforms in the diagnosis of arterial diseases.
Objective To observe the effect of combination of antihypertensive and lipid lowering therapy on arterial stiffness in elderly patients with mild to moderate essential hypertension. Methods A total of 216 elderly patients with mild to moderate essential hypertension were enrolled and treated by hydrochlorothiazide as the basic therapy for two weeks. Then the patients were randomly divided into four groups. Namely, the intensified antihypertensive and lipid lowering therapy group (hydrochlorothiazide 25 mg/d, Candesartan 8 mg/d, Rosuvastatin 10 mg/d, n=54), the intensified antihypertensive treatment group (hydrochlorothiazide 25 mg/d, Candesartan 8 mg/d, n=54), the antihypertensive and lipid lowering therapy group (hydrochlorothiazide 25 mg/d, Rosuvastatin 10 mg/d, n=54), and the control group (hydrochlorothiazide 25 mg/d, n=54). After 12-month treatment, the blood pressure, blood lipid and carotid-radial pulse wave velocity (crPWV) of each group were recorded. Results Twelve months later, the SBP, DBP, PP and crPWV of each group were significantly lower than before (Plt;0.05). There was interactive effect of antihypertensive and lipid lowering therapy in lowering SBP, DBP, PP and crPWV (F=40.765, 4.869, 24.829, and 53.149, respectively, all Рlt;0.05). Conclusion The combination of antihypertensive and lipid lowering therapy can significantly lower the crPWV of elderly patients with hypertension and improve the arterial stiffness; it is superior to single treatment of either antihypertensive or lipid lowering.
In order to improve the accuracy of blood pressure measurement in wearable devices, this paper presents a method for detecting blood pressure based on multiple parameters of pulse wave. Based on regression analysis between blood pressure and the characteristic parameters of pulse wave, such as the pulse wave transit time (PWTT), cardiac output, coefficient of pulse wave, the average slope of the ascending branch, heart rate, etc. we established a model to calculate blood pressure. For overcoming the application deficiencies caused by measuring ECG in wearable device, such as replacing electrodes and ECG lead sets which are not convenient, we calculated the PWTT with heart sound as reference (PWTTPCG). We experimentally verified the detection of blood pressure based on PWTTPCG and based on multiple parameters of pulse wave. The experiment results showed that it was feasible to calculate the PWTT from PWTTPCG. The mean measurement error of the systolic and diastolic blood pressure calculated by the model based on multiple parameters of pulse wave is 1.62 mm Hg and 1.12 mm Hg, increased by 57% and 53% compared to those of the model based on simple parameter. This method has more measurement accuracy.
Artery stiffness is a main factor causing the various cardiovascular diseases in physiology and pathology. Therefore, the development of the non-invasive detection of arteriosclerosis is significant in preventing cardiovascular problems. In this study, the characterized parameters indicating the vascular stiffness were obtained by analyzing the electrocardiogram (ECG) and pulse wave signals, which can reflect the early change of vascular condition, and can predict the risk of cardiovascular diseases. Considering the coupling of ECG and pulse wave signals, and the association with atherosclerosis, we used the ECG signal characteristic parameters, including RR interval, QRS wave width and T wave amplitude, as well as the pulse wave signal characteristic parameters (the number of peaks, 20% main wave width, the main wave slope, pulse rate and the relative height of the three peaks), to evaluate the samples. We then built an assessment model of arteriosclerosis based on Adaptive Network-based Fuzzy Interference System (ANFIS) using the obtained forty sets samples data of ECG and pulse wave signals. The results showed that the model could noninvasively assess the arteriosclerosis by self-learning diagnosis based on expert experience, and the detection method could be further developed to a potential technique for evaluating the risk of cardiovascular diseases. The technique will facilitate the reduction of the morbidity and mortality of the cardiovascular diseases with the effective and prompt medical intervention.
Characteristics in pulse wave signals (PWSs) include the information of physiology and pathology of human cardiovascular system. Therefore, identification of characteristic points in PWSs plays a significant role in analyzing human cardiovascular system. Particularly, the characteristic points show personal dependent features and are easy to be affected. Acquiring a signal with high signal-to-noise ratio (SNR) and integrity is fundamentally important to precisely identify the characteristic points. Based on the mathematical morphology theory, we design a combined filter, which can effectively suppress the baseline drift and remove the high-frequency noise simultaneously, to preprocess the PWSs. Furthermore, the characteristic points of the preprocessed signal are extracted according to its position relations with the zero-crossing points of wavelet coefficients of the signal. In addition, the differential method is adopted to calibrate the position offset of characteristic points caused by the wavelet transform. We investigated four typical PWSs reconstructed by three Gaussian functions with tunable parameters. The numerical results suggested that the proposed method could identify the characteristic points of PWSs accurately.
ObjectiveTo evaluate the level of arteriosclerosis in patients with hypertension defined by the American Heart Association (AHA) and classical diagnostic criteria. MethodsA total of 3 815 residents were enrolled in 10 communities in north Shanghai. According to the classic diagnostic criteria of hypertension (systolic blood pressure≥140 mmHg and/or diastolic blood pressure≥90 mmHg) and AHA diagnostic criteria (systolic blood pressure≥130 mmHg and/or diastolic blood pressure≥80 mmHg), the population was divided into normal blood pressure group, AHA diagnosis standard hypertension group, and classic methods of diagnosis of hypertension group. The differences in cervical-femoral pulse wave velocity (cf-PWV) and brachial-ankle pulse wave velocity (ba-PWV) among the three groups were compared. SPSS 13.0 software was then used for data analysis.ResultsCompared with the patients who met the standard criteria, patients who met AHA criteria had lower mean ages (70.2±7.4 vs. 71.4±7.9 year, P<0.001), more history of hypertension (48.8% vs. 72.7%, P<0.001) and lower body mass index (24.1±3.5 vs. 24.7±3.9 kg/m2, P<0.001), low-density lipoprotein (3.07±0.92 vs. 3.15±0.97 mmol/L, P=0.033), cf-PWV (8.7±2.7 vs. 9.8±3.0 m/s, P<0.001) and ba-PWV (1 647.7±610.1 vs. 1 797.2±729.7 cm/s, P<0.001). ConclusionsThe degree of arteriosclerosis of patients who meet AHA standards is between that who meet the standard criteria and the normal population. For these patients, blood pressure should be actively controlled to delay the progression of arteriosclerosis.
The pulse amplitude of fingertip volume could be improved by selecting the vascular dense area and applying appropriate pressure above it. In view of this phenomenon, this paper used Comsol Multiphysics 5.6 (Comsol, Sweden), the finite element analysis software of multi-physical field coupling simulation, to establish the vascular tissue model of a single small artery in fingertips for simulation. Three dimensional Navier-Stokes equations were solved by finite element method, the velocity field and pressure distribution of blood were calculated, and the deformation of blood vessels and surrounding tissues was analyzed. Based on Lambert Beer's Law, the influence of the longitudinal compression displacement of the lateral light surface region and the tissue model on the light intensity signal is investigated. The results show that the light intensity signal amplitude could be increased and its peak value could be reduced by selecting the area with dense blood vessels. Applying deep pressure to the tissue increased the amplitude and peak of the signal. It is expected that the simulation results combined with the previous experimental experience could provide a feasible scheme for improving the quality of finger volume pulse signal.
Early detection of vascular function plays an important role in the prevention and treatment of cardiovascular diseases (CVDs). This paper reports the main studies of the effectiveness of fingertip temperature curve in digital thermal monitoring (DTM) for predicting CVDs, as well as the relationship between parameters from DTM and pulse wave velocity (PWV) detection. A total of 112 subjects [age (42.18±12.28) years, 50% male, 37 with known CVDs] underwent DTM and PWV detection. Results showed that most of parameters related to CVDs were from the declining stage of the digital thermal signal. Binary Logistic regression models were built, and the best one was chosen by ten-fold validation to predict CVDs. Consistency was great between the detection result of PWV and that of the Logistic model of DTM parameters. Parameters from DTM also contained information for early detecting of vascular stiffness. This study indicates that the fingertip temperature curve in DTM has a potential application for predication of CVDs, and it would be used to access vascular function in the initial stage of CVDs.