Pulse Wave Signal Research Articles

Machine learning classification of polycystic ovary syndrome based on radial pulse wave analysis.

Patients with Polycystic ovary syndrome (PCOS) experienced endocrine disorders that may present vascular function changes. This study aimed to classify and predict PCOS by radial pulse wave parameters using machine learning (ML) methods and to provide evidence for objectifying pulse diagnosis in traditional Chinese medicine (TCM). A case-control study with 459 subjects divided into a PCOS group and a healthy (non-PCOS) group. The pulse wave parameters were measured and analyzed between the two groups. Seven supervised ML classification models were applied, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Random Forest, Logistic Regression, Voting, and Long Short Term Memory networks (LSTM). Parameters that were significantly different were selected as input features and stratified k-fold cross-validations training was applied to the models. There were 316 subjects in the PCOS group and 143 subjects in the healthy group. Compared to the healthy group, the pulse wave parameters h3/h1 and w/t from both left and right sides were increased while h4, t4, t, As, h4/h1 from both sides and right t1 were decreased in the PCOS group (P < 0.01). Among the ML models evaluated, both the Voting and LSTM with ensemble learning capabilities, demonstrated competitive performance. These models achieved the highest results across all evaluation metrics. Specifically, they both attained a testing accuracy of 72.174% and an F1 score of 0.818, their respective AUC values were 0.715 for the Voting and 0.722 for the LSTM. Radial pulse wave signal could identify most PCOS patients accurately (with a good F1 score) and is valuable for early detection and monitoring of PCOS with acceptable overall accuracy. This technique can stimulate the development of individualized PCOS risk assessment using mobile detection technology, furthermore, gives physicians an intuitive understanding of the objective pulse diagnosis of TCM. Not applicable.

Effect of low-intensity exercise therapy combined with health education on quality of life and blood pressure in hypertensive patients

Abstract In this paper, starting from exercise prescription and health promotion of hypertension, the hyperparameters of the XGBoost model are optimized using the Bayesian optimization algorithm, and then the BO-XGBoost blood pressure detection model is constructed. The model can estimate blood pressure from the ECG pulse wave signal of hypertensive patients and understand the patient’s blood pressure by PPG signal and ECG signal. For the effect of low-intensity exercise therapy combined with health education on the quality of life and blood pressure of hypertensive patients, experiments of the model and comparative test analysis of quality of life scores were conducted in this paper. From the model detection, the systolic and diastolic blood pressure detected by the BO-XGBoost blood pressure detection model was 118.6 mmHg and 65.2 mmHg, respectively, with an error of 3.03% and 3.83%, respectively, compared with the standard value of blood pressure. In terms of quality of life, the quality of life scores of the control and experimental groups improved by about 14% to 20% after the intervention compared with those before the intervention. This indicates that low-intensity exercise therapy combined with health education can help hypertensive patients improve their blood pressure control and quality of life.

Noise-Robust Pulse Wave Estimation from Near-Infrared Face Video Images Using the Wiener Estimation Method.

In this paper, we propose a noise-robust pulse wave estimation method from near-infrared face video images. Pulse wave estimation in a near-infrared environment is expected to be applied to non-contact monitoring in dark areas. The conventional method cannot consider noise when performing estimation. As a result, the accuracy of pulse wave estimation in noisy environments is not very high. This may adversely affect the accuracy of heart rate data and other data obtained from pulse wave signals. Therefore, the objective of this study is to perform pulse wave estimation robust to noise. The Wiener estimation method, which is a simple linear computation that can consider noise, was used in this study. Experimental results showed that the combination of the proposed method and signal processing (detrending and bandpass filtering) increased the SNR (signal to noise ratio) by more than 2.5 dB compared to the conventional method and signal processing. The correlation coefficient between the pulse wave signal measured using a pulse wave meter and the estimated pulse wave signal was 0.30 larger on average for the proposed method. Furthermore, the AER (absolute error rate) between the heart rate measured with the pulse wave meter was 0.82% on average for the proposed method, which was lower than the value of the conventional method (12.53% on average). These results show that the proposed method is more robust to noise than the conventional method for pulse wave estimation.

Noninvasive hemodynamic indices of vascular aging: an in silico assessment.

Vascular aging (VA) involves structural and functional changes in blood vessels that contribute to cardiovascular disease. Several noninvasive pulse wave (PW) indices have been proposed to assess the arterial stiffness component of VA in the clinic and daily life. This study investigated 19 of these indices, identified in recent review articles on VA, by using a database comprising 3,837 virtual healthy subjects aged 25-75 yr, each with unique PW signals simulated under various levels of artificial noise to mimic real measurement errors. For each subject, VA indices were calculated from filtered PW signals and compared with the precise theoretical value of aortic Young's modulus (EAo). In silico PW indices showed age-related changes that align with in vivo population studies. The cardio-ankle vascular index (CAVI) and all pulse wave velocity (PWV) indices showed strong linear correlations with EAo (Pearson's rp > 0.95). Carotid distensibility showed a strong negative nonlinear correlation (Spearman's rs < -0.99). CAVI and distensibility exhibited greater resilience to noise compared with PWV indices. Blood pressure-related indices and photoplethysmography (PPG)-based indices showed weaker correlations with EAo (rp and rs < 0.89, |rp| and |rs| < 0.84, respectively). Overall, blood pressure-related indices were confounded by more cardiovascular properties (heart rate, stroke volume, duration of systole, large artery diameter, and/or peripheral vascular resistance) compared with other studied indices, and PPG-based indices were most affected by noise. In conclusion, carotid-femoral PWV, CAVI and carotid distensibility emerged as the superior clinical VA indicators, with a strong EAo correlation and noise resilience. PPG-based indices showed potential for daily VA monitoring under minimized noise disturbances.NEW & NOTEWORTHY For the first time, 19 noninvasive pulse wave indices for assessing vascular aging were examined together in a single database of nearly 4,000 subjects aged 25-75 yr. The dataset contained precise values of the aortic Young's modulus and other hemodynamic measures for each subject, which enabled us to test each index's ability to measure changes in aortic stiffness while accounting for confounding factors and measurement errors. The study provides freely available tools for analyzing these and additional indices.

Non-invasive continuous blood pressure prediction based on ECG and PPG fusion map.

To achieve real-time blood pressure monitoring, a novel non-invasive method is proposed in this article. Electrocardiographic (ECG) and pulse wave signals (PPG) are fused from a multi-omics signal-level perspective. A physiological signal fusion matrix and fusion map, which can estimate the blood pressure of blood loss(BPBL), are constructed. The results demonstrate the efficacy of the fusion map model, with correlation values of 0.988 and 0.991 between the estimated BPBL and the true systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively. The root mean square errors for SBP and DBP were 3.21mmHg and 3.00mmHg, respectively. The model validation showed that the fusion map method is capable of simultaneous highlighting of the respective characteristics of ECG and PPG and their correlation, improving the utilization of the information and the accuracy of BPBL. This article validates that physiological signal fusion map can effectively improve the accuracy of BPBL estimation and provides a new perspective for the field of physiological information fusion.

Vascular stiffness in cold pressor test hyper-reactors

Abstract Background: The cold pressor test (CPT) is a sympathoexcitatory manoeuvre to measure cardiovascular hyperactivity which may be a sign of future hypertension development. Aims and Objectives: To evaluate the vascular stiffness background among healthy CPT hyper-reactor (H-R) males and age and gender-matched normo-reactors (N-R). Methods: The study was carried out in 34 healthy young males aged 19 years old. Five minutes of recordings of left middle fingertip digital pulse wave (DPW) signals (through a piezoelectric finger pulse transducer) were registered. This was followed by the application of a cold pressor. Blood pressure (BP) and heart rate (HR) during CPT were recorded, and the individuals were divided into N-Rs and H-Rs. Vascular stiffness indices like pulse wave velocity (PWV), B/A wave ratio, pulse rising time (PRT, or crest), large artery stiffness index (SI), and pulse width (PW) were compared between H-Rs and N-Rs. Results: No significant differences were observed between the two groups across all studied parameters. Conclusion: The results suggest there was no difference in the vascular stiffness background between CPT H-R and N-R individuals.

Automatic and continuous blood pressure monitoring via an optical-fiber-sensor-assisted smartwatch

Automatic and continuous blood pressure monitoring is important for preventing cardiovascular diseases such as hypertension. The evaluation of medication effects and the diagnosis of clinical hypertension can both benefit from continuous monitoring. The current generation of wearable blood pressure monitors frequently encounters limitations with inadequate portability, electrical safety, limited accuracy, and precise position alignment. Here, we present an optical fiber sensor-assisted smartwatch for precise continuous blood pressure monitoring. A fiber adapter and a liquid capsule were used in the building of the blood pressure smartwatch based on an optical fiber sensor. The fiber adapter was used to detect the pulse wave signals, and the liquid capsule was used to expand the sensing area as well as the conformability to the body. The sensor holds a sensitivity of -213µw/kPa, a response time of 5 ms, and high reproducibility with 70,000 cycles. With the assistance of pulse wave signal feature extraction and a machine learning algorithm, the smartwatch can continuously and precisely monitor blood pressure. A wearable smartwatch featuring a signal processing chip, a Bluetooth transmission module, and a specially designed cellphone APP was also created for active health management. The performance in comparison with commercial sphygmomanometer reference measurements shows that the systolic pressure and diastolic pressure errors are -0.35 ± 4.68 mmHg and -2.54 ± 4.07 mmHg, respectively. These values are within the acceptable ranges for Grade A according to the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). The smartwatch assisted with an optical fiber is expected to offer a practical paradigm in digital health.

Pulse wave signal modelling and feature extraction based on Lognormal function from photoplethysmography in wireless body area networks

Among the various physiological signals of the human body, the pulse wave signal from photoplethysmography is widely used for commercial products in Wireless Body Area Networks. The comfortable wearing of a product and the richness of physiological information are two advantages of Wireless Body Area Networks. To address the shortcomings of the existing modelling pulse wave signal in lacking characteristics that are consistent with a human physiological mechanism in medicine, we proposed a Lognormal function model of the pulse wave signal and a novel model to extract physiological parameters. The proposed Lognormal function model consists of four successively spaced single-peaked pulses with long trailing features. To define the parameters in the proposed Lognormal function model, we proposed a method that took advantage of the positivity and negativity of the first-order derivatives and the trans-zero point of the second-order derivatives. Based on the determined parameters, we introduced a four-shot staged curve fitting approach that can displace the sum of the four fits at iterative and different time scales. Finally, a parameter vector with 12 elements, which is known as a physiological feature to determine the health status of the human body in Wireless Body Area Networks. Experimental results show that the proposed Lognormal function model is superior to the conventional Gaussian function model in terms of physiological importance and waveform fitting accuracy.

Deep Learning-Based Non-Contact IPPG Signal Blood Pressure Measurement Research

In this paper, a multi-stage deep learning blood pressure prediction model based on imaging photoplethysmography (IPPG) signals is proposed to achieve accurate and convenient monitoring of human blood pressure. A camera-based non-contact human IPPG signal acquisition system is designed. The system can perform experimental acquisition under ambient light, effectively reducing the cost of non-contact pulse wave signal acquisition while simplifying the operation process. The first open-source dataset IPPG-BP for IPPG signal and blood pressure data is constructed by this system, and a multi-stage blood pressure estimation model combining a convolutional neural network and bidirectional gated recurrent neural network is designed. The results of the model conform to both BHS and AAMI international standards. Compared with other blood pressure estimation methods, the multi-stage model automatically extracts features through a deep learning network and combines different morphological features of diastolic and systolic waveforms, which reduces the workload while improving accuracy.

Uncertainty quantification of microcirculatory characteristic parameters for recognition of cardiovascular diseases

BackgroundCardiovascular disease is one of the leading causes of death worldwide. However, according to studies, 90% of cardiovascular diseases can be prevented. Cardiovascular function parameters are an important basis for the diagnosis of cardiovascular diseases. The pulse wave also contains a wealth of physiological and pathological information, which can reflect the trend of cardiac function parameters at an early stage, so the measurement and analysis of the pulse wave signal becomes more and more important. The wearable pulse signal acquisition device has gradually become a new trend. In the mobile health scenario, convenient use is the prerequisite for long-term and rapid health monitoring. The data containing diverse pulse wave signals is the basis for obtaining more comprehensive and accurate human physiopathological information. Accurate data analysis and processing is the key to realizing the important goal of cardiovascular health monitoring. ObjectiveBased on the concept of mobile health care, wearable devices are developed to obtain physiological signals. The zero-dimensional model and the optimization algorithm are combined to complete the uncertainty quantification of the microcirculation parameters. Then, a feature set containing the cardiovasvular parameters can be constructed. The machine learning algorithm can be used to build a model that can accurately realize cardiovascular disease identification. MethodsThis paper adopts laboratory-developed equipment to acquire the wrist pulse wave and fingertip volume pulse wave. A total of 323 samples were collected from healthy populations, hypertensive patients and patients with coronary heart disease (CHD). The pulse blood flow model in fingertip microcirculation is established, and the uncertainty quantification of model parameters is completed based on slime mold algorithm (SMA). After comparing and analyzing the performance of four algorithms on pulse wave classification, the identification model of cardiovascular diseases is established based on the microcirculatory characteristic parameter set and random forest algorithm (RF). ResultsRF showed good classification performance among the four classification algorithms. The identification accuracy of the model built on the microcirculatory characteristic parameter set and RF algorithm all reached more than 88%. The highest recognition accuracy was 95.51% for coronary heart disease samples, 92.11% for healthy samples, and 88.55% for hypertensive samples. It can be seen that the model based on RF algorithm has a good ability to distinguish the characteristic parameters in different cardiovascular health states. ConclusionsThe wearable device designed in this paper can facilitate the daily health monitoring of cardiovascular disease. By using the combination of the physical model and machine learning model, the uncertainty quantification of microcirculation parameters and the identification of cardiovascular disease was finally completed. The recognition model based on machine learning provides a new idea and method for the research of cardiovascular health monitoring through pulse waves.

A Hemodynamic Pulse Wave Simulator Designed for Calibration of Local Pulse Wave Velocities Measurement for Cuffless Techniques.

Devices for cuffless blood pressure (BP) measurement have become increasingly widespread in recent years. Non-invasive continuous BP monitor (BPM) devices can diagnose potential hypertensive patients at an early stage; however, these cuffless BPMs require more reliable pulse wave simulation equipment and verification methods. Therefore, we propose a device to simulate human pulse wave signals that can test the accuracy of cuffless BPM devices using pulse wave velocity (PWV). We design and develop a simulator capable of simulating human pulse waves comprising an electromechanical system to simulate the circulatory system and an arm model-embedded arterial phantom. These parts form a pulse wave simulator with hemodynamic characteristics. We use a cuffless device for measuring local PWV as the device under test to measure the PWV of the pulse wave simulator. We then use a hemodynamic model to fit the cuffless BPM and pulse wave simulator results; this model can rapidly calibrate the cuffless BPM's hemodynamic measurement performance. We first used multiple linear regression (MLR) to generate a cuffless BPM calibration model and then investigated differences between the measured PWV with and without MLR model calibration. The mean absolute error of the studied cuffless BPM without the MLR model is 0.77 m/s, which improves to 0.06 m/s when using the model for calibration. The measurement error of the cuffless BPM at BPs of 100-180 mmHg is 1.7-5.99 mmHg before calibration, which decreases to 0.14-0.48 mmHg after calibration. This study proposes a design of a pulse wave simulator based on hemodynamic characteristics and provides a standard performance verification method for cuffless BPMs that requires only MLR modeling on the cuffless BPM and pulse wave simulator. The pulse wave simulator proposed in this study can be used to quantitively assess the performance of cuffless BPMs. The proposed pulse wave simulator is suitable for mass production for the verification of cuffless BPMs. As cuffless BPMs become increasingly widespread, this study can provide performance testing standards for cuffless devices.

Predicting blood pressure from face videos using face diagnosis theory and deep neural networks technique

Hypertension is a major cause of cardiovascular diseases. Accurate and convenient measurement of blood pressure are necessary for the detection, treatment, and control of hypertension. In recent years, face video based non-contact blood pressure prediction is a promising research topic. Interestingly, face diagnosis has been an important part of traditional Chinese medicine (TCM) for thousands of years. TCM practitioners observe some typical regions of the face to determine the health status of the Zang Fu organs (i.e., heart). However, the effectiveness of face diagnosis theory in conjunction with computer vision analysis techniques to predict blood pressure is unclear. We proposed an artificial intelligence framework for predicting blood pressure using deep convolutional neural networks in this study. First, we extracted pulse wave signals through 652 facial videos. Then, we trained and compared nine artificial neural networks and chose the best performed prediction model, with an overall true predict rate of 90%. We also investigated the impact of face reflex regions selection on blood pressure prediction model, and the five face regions outperformed. Our high effectiveness and stability framework may provide an objective and convenient computer-aided blood pressure prediction method for hypertension screening and disease prevention.

Model of an automated biotechnical system for analyzing pulseograms as a kind of edge devices

The rapid development of computer biometrics over the past 2-3 decades is largely due to the development and widespread introduction into clinical practice of new methods of studying human body health, including pulse methods. It is possible to judge changes in hemodynamic characteristics, heart rate, and blood flow rate in the studied part of the body based on the parameters of the pulse wave signal. At the same time, the physical processes of the formation of the pulse wave shape have not been fully studied, although the number of biophysical models of blood circulation is quite significant. The development of such a model will allow us to effectively apply modern developments in digital signal processing to the pulse wave and increase its diagnostic value. A qualitative model of pulse signal can be entrusted to the development of the base unit of the biotechnical system as a type of edge device. The work is devoted to the improvement of methods of rapid diagnosis of the cardiovascular system based on the analysis of model pulse-grams. An adequate mathematical model of the pulse wave, which corresponds to real pulse signals in different states of the human body and contains mathematical relationships between the main parameters of pulse-grams, has been refined. The algorithm of express diagnostics with the established criteria of the analysis of pulsograms is offered.

Application of translation wavelet transform with new threshold function in pulse wave signal denoising.

The wrist pulse wave under the optimal pulse pressure plays an important role in detecting human body's physiological and pathological information. Wavelet threshold filtering is a common method for pulse wave de-noising. However, traditional filtering methods cannot smoothen the whole pulse wave well and highlight the details. In view of this, an attempt is made in this paper to propose the pulse wave denoising algorithm for pulse wave under optimal pulse pressure according to the translation invariant wavelet transform (TIWT) and the new threshold function. Firstly, by using hyperbolic tangent curve and combining the advantages of soft threshold function and hard threshold function, the new threshold function is derived. Secondly, based on the TIWT, pseudo-Gibbs phenomenon gets suppressed. The experiments show that in comparison to the traditional wavelet filtering algorithm, the novel algorithm can better maintain the pulse wave geometric characteristics and has a higher signal to noise ratio (SNR). The TIWT with improved new threshold compensates the shortcomings of the traditional wavelet threshold denoising methods in a better way. It lays a foundation for extracting time-domain characteristics of pulse wave.

Machine Learning Assisted Wearable Wireless Device for Sleep Apnea Syndrome Diagnosis

Sleep apnea syndrome (SAS) is a common but underdiagnosed health problem related to impaired quality of life and increased cardiovascular risk. In order to solve the problem of complicated and expensive operation procedures for clinical diagnosis of sleep apnea, here we propose a small and low-cost wearable apnea diagnostic system. The system uses a photoplethysmography (PPG) optical sensor to collect human pulse wave signals and blood oxygen saturation synchronously. Then multiscale entropy and random forest algorithms are used to process the PPG signal for analysis and diagnosis of sleep apnea. The SAS determination is based on the comprehensive diagnosis of the PPG signal and blood oxygen saturation signal, and the blood oxygen is used to exclude the error induced by non-pathological factors. The performance of the system is compared with the Compumedics Grael PSG (Polysomnography) sleep monitoring system. This simple diagnostic system provides a feasible technical solution for portable and low-cost screening and diagnosis of SAS patients with a high accuracy of over 85%.

PSS modified by 3-aminopropyltrimethoxysilane linking large-area GNPs/PSS to silicone rubber with stable interface combination for high sensitivity flexible resistive sensor

Flexible resistive sensors based on crack structures have been widely studied in the field of electronic skin and health monitoring. However, it is still a challenge to develop resistive sensors with strong interface bonding forces due to the problem that the sensitive layer of traditional cracked sensors falls off easily. This paper reports a new type of graphene-based resistive sensor, which uses modified poly(sodium 4-phenylene sulfonate) and graphene nanosheets to prepare a highly conductive, fragile, film sensitive layer. Siloxane at the end of modified poly(sodium 4-phenylene sulfonate) is used to enhance the interface between the sensitive layer and the silicone rubber layer to prepare a flexible resistive sensor with a crack structure. The sensor has a high sensitivity (gauge factor up to 21980.28) and a high response speed (∼65 ms), with an effective working range of 0–4.7 % strain. The sensor has been successfully applied to human physiological signal detection. The neural network algorithm successfully predicted the corresponding blood pressure based on the pulse wave signal detected by the sensor. The average error of systolic blood pressure and diastolic blood pressure was 0.79 and 0.42 mmHg, and the error of ± 5 mmHg accounted for 91.2 % and 94.3 % of the total, respectively. The error value passed the Association for the Advancement of Medical Instrumentation standard, and reached the Class A standard of British Hypertension Society.

Research on a Non-Invasive Hemoglobin Measurement System Based on Four-Wavelength Photoplethysmography

Hemoglobin is an essential parameter in human blood. This paper proposes a non-invasive hemoglobin concentration measurement method based on the characteristic parameters of four-wavelength photoplethysmography (PPG) signals combined with machine learning. The DCM08 sensor and NRF52840 chip form a data acquisition system to collect 58 human fingertip photoelectric volumetric pulse wave signals. The 160 four-wavelength PPG signal feature parameters were constructed and extracted. The feature parameters were screened by combining three feature selection methods: reliefF, Chi-square score, and information gain. The top 10, 20, and 30 features screened were used as input to evaluate the prediction performance of different feature sets for hemoglobin. The prediction models used were XGBoost, support vector machines, and logistic regression. The results showed that the optimal performance of the 30 feature sets screened using the Chi-square test was achieved by the XGBoost model with a coefficient of determination (R2) of 0.997, root mean square error (RMSE) of 0.762 g/L, and mean absolute error (MAE) of 0.325 g/L. The study showed that the four-wavelength-based PPG signal feature parameters with the XGBoost algorithm could effectively achieve non-invasive detection of hemoglobin, providing a new measurement method in clinical practice.

A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: An in silico study.

Carotid-to-femoral pulse wave velocity (cf-PWV) is considered a critical index to evaluate arterial stiffness. For this reason, estimating Carotid-to-femoral pulse wave velocity (cf-PWV) is essential for diagnosing and analyzing different cardiovascular diseases. Despite its broader adoption in the clinical routine, the measurement process of carotid-to-femoral pulse wave velocity is considered a demanding task for clinicians and patients making it prone to inaccuracies and errors in the estimation. A smart non-invasive, and peripheral measurement of carotid-to-femoral pulse wave velocity could overcome the challenges of the classical assessment process and improve the quality of patient care. This paper proposes a novel methodology for the carotid-to-femoral pulse wave velocity estimation based on the use of the spectrogram representation from single non-invasive peripheral pulse wave signals [photoplethysmography (PPG) or blood pressure (BP)]. This methodology was tested using three feature extraction methods based on the semi-classical signal analysis (SCSA) method, the Law's mask for texture energy extraction, and the central statistical moments. Finally, each feature method was fed into different machine learning models for the carotid-to-femoral pulse wave velocity estimation. The proposed methodology obtained an $R2\geq0.90$ for all the peripheral signals for the noise-free case using the MLP model, and for the different noise levels added to the original signal, the SCSA-based features with the MLP model presented an $R2\geq0.91$ for all the peripheral signals at the level of noise. These results provide evidence of the capacity of spectrogram representation for efficiently assessing the carotid-to-femoral pulse wave velocity estimation using different feature methods. Future work will be done toward testing the proposed methodology for in-vivo signals.

Effect of Temperature on Current Pulse Characteristics of Negative Corona Discharge Based on Numerical Model

Temperature is an important factor affecting the dynamic processes of corona discharge, since the ionization, attachment, and mobility of charged particles are directly related to temperature. Most existing research has investigated the temperature effect on corona discharge through experimental works. However, they are hard to acquire the corona discharge microphysical processes, which are related to the formation of the current pulse and electromagnetic (EM) wave signals. This article proposes a numerical model with temperature-related transport parameters to study the temperature influence on microphysical processes and current pulses for negative corona. By comparing microphysical quantities such as the effective ionization coefficient, density, and velocity of space charges, the effect of temperature on the current pulse parameters including the rise and fall times, amplitude, and pulsewidth is systematically explained. It shows that as the temperature rises, the current pulse amplitude increases, while the other three parameters decrease. The rise time is dependent on the effective ionization coefficient. The fall time is affected by the velocities of negative and positive ions. The experimental results indicate the simulated current pulses are consistent with the measured ones, proving the effectiveness of the proposed numerical model with temperature variation.

PS-BPC01-4: NONINVASIVE CONTINUOUS BLOOD PRESSURE MONITORING BASED ON WEARABLE RADAR SENSOR WITH PRELIMINARY CLINICAL VALIDATION

This paper proposes a novel sensing system for noninvasive and continuous estimation of blood pressure (BP), which is based on a custom designed wearable 120 GHz Doppler radar sensor. With radar signals detected from brachial artery at the elbow, the moving trajectory of the pulses can be accurately demodulated with micrometer accuracy, and thus the reflective pulse transit time(RPTT) can be obtained for the prediction model to estimate blood pressure. Clinical trials have been launched with invasive blood pressure as reference. Patients keep their arms straight with palms facing upward, and a transducer set for medical invasive blood pressure measurement called DTXPlus is set up. DTXPlus has one end connected to a monitor, and the other end connected to the arterial catheter that reaches the brachial artery. The blood pressure signals for reference standard are collected by a data acquisition card (DAQ) from the transmission wire between DTXPlus and monitor. Meanwhile, the 120 GHz radar sensor with concentrated energy and high frequency is used to obtain the pulse wave signals from the brachial artery at the elbow, which is almost the same place detected by both invasive and noninvasive methods. The radar sensor continuously transmits electromagnetic signals to the human body, and the throb of the artery modulates the electromagnetic waves in the phase and the backscattered signals return to the radar receiver. For comparison later, the radar outputs are acquired by the DAQ in synchronization with the blood pressure signals. Experiments were carried out in the operating room with permission, patients with cardiovascular diseases were tested for several minutes after operations. The experimental results show a great performance of the designed wearable radar sensor in the detection of weak pulse signatures. Compared to the ground truth of the invasive blood pressure, the estimated blood pressures by radar have mean errors below 3mmHg for most subjects. The results reveal the possibility for the proposed radar-based BP estimation technique to be employed inside and outside the clinic, such as applications in the vast internet-of-things (IoT) and in-home health monitoring.