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.
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.
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.
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.
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.
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.
This paper studied the rule for the change of vigilance based on pulse wave. 10 participants were recruited in a 95-minute Mackworth clock test (MCT) experiment. During the experiment, the vigilance of all participants were evaluated by Karolinska sleepiness scale (KSS) and Stanford sleepiness scale (SSS), and behavior data (the reaction time and the accuracy of target) and pulse wave signal of the participants were recorded simultaneously. The result indicated that vigilance of the participants can be divided into 3 classes: the first 30 minutes for high vigilance level, the middle 30 minutes for general vigilance level, and the last 30 minutes for low vigilance level. Besides, time domain features such as amplitude of secondary peak, amplitude of peak and the latency of secondary peak decreased with the decrease of vigilance, while the amplitude of troughs increased. In terms of frequency domain features, the energy of 4 frequency band including 8.600 ~ 9.375 Hz, 11.720 ~ 12.500 Hz, 38.280 ~ 39.060 Hz and 39.060 ~ 39.840 Hz decreased with the decrease of vigilance. Finally, under the recognition model established by the 8 characteristics mentioned above, the average accuracy of three-classification results over the 10 participants was as high as 88.7%. The results of this study confirmed the feasibility of pulse wave in the evaluation of vigilance, and provided a new way for the real-time monitoring of vigilance.
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 investigate the relationship between pulmonary ventilation function (obstructive and restrictive ventilation dysfunction) and atherosclerosis, and explore the correlation between brachial-ankle pulse wave velocity (ba-PWV, an effective index for evaluating atherosclerosis) and pulmonary ventilation function.MethodsFrom January to August 2018, a total of 6403 healthy subjects who reported no major chronic diseases such as stroke, myocardial infarction, cor pulmonale or malignant tumor were selected. Past history such as smoking history, hypertension, diabetes, blood biochemistry, and blood hypersensitive C reactive protein (hs-CRP), hemodynamic indexes such as systolic pressure, diastolic pressure and ba-PWV, body measurement indexes such as height, weight, waist circumference and pulmonary ventilation function were collected. The relationship between ba-PWV and pulmonary ventilation function were evaluated.ResultsA total of 2433 subjects were included, including 916 males and 1517 females. Ba-PWV showed significant positive correlations with age, smoking index, waist circumference, systolic blood pressure, diastolic blood pressure, triglyceride, cholesterol, low density lipoprotein, hs-CRP, glycosylated hemoglobin, and significant negative correlations with height, percentage of forced vital capacity (FVC) in the predicted value (FVC%pred), forced expiratory volume in one second (FEV1), percentage of FEV1 in the predicted value (FEV1%pred), FEV1/FVC ratio and percentage of maximun midexpiratory flow (MMEF) in the predicted value (MMEF%pred). The ba-PWV was not correlated with weight, body mass index, FVC, MMEF, γ-glutamyl transpeptidase, high density lipoprotein, creatinine or uric acid. In multiple regression analysis using factors other than ba-PWV and respiratory function as adjustment variables, both FVC%pred and FEV1%pred showed significant negative relationships with ba-PWV (P<0.05).ConclusionsThe results indicate that FEV1/FVC, an indicator of airflow limitation, is not a predictor of ba-PWV. However, since ba-PWV showed significant negative relationship with FVC%pred and FEV1%pred, clinically assessment of arterial stiffness might be considered in individuals with impaired pulmonary ventilation.
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.