[Design of Pulse and Respiratory Signals Detection System].

A pulse and respiration synchronous detection system is designed to explore the changes of physiological signals in different situations. The system obtains the corresponding signal through STM32 control pulse and respiratory acquisition circuit, calculates and displays real-time parameters such as heart rate and respiratory rate, and transmits the data to the upper computer for storage in the database. The experimental test results show that the system can monitor pulse and respiratory waveform in different situations, and the waveform is in good condition. Compared with medical pulse oximeter, the error of measured heart rate and blood oxygen concentration is less than 3%, and the error of respiratory rate is less than 5% compared with the actual value, which verifies the accuracy of system signal acquisition. The system is small in size, low in cost, and comfortable to wear, and can be applied in experimental research related to pulse and respiratory signals.

Analysis of time-domain indices, frequency domain measures of heart rate variability derived from ECG waveform and pulse-wave-related HRV among overweight individuals: an observational study.

Background: Research on the compatibility of time domain indices, frequency domain measurements of heart rate variability obtained from electrocardiogram (ECG) waveforms, and pulse wave signal (pulse rate variability; PRV) features is ongoing. The promising marker of cardiac autonomic function is heart rate variability. Recent research has looked at various other physiological markers, leading to the emergence of pulse rate variability. The pulse wave signal can be studied for variations to understand better changes in arterial stiffness and compliance, which are key indicators of cardiovascular health. Methods: 35 healthy overweight people were included. The Lead II electrocardiogram (ECG) signal was transmitted through an analog-to-digital converter (PowerLab 8/35 software, AD Instruments Pty. Ltd., New South Wales, Australia). This signal was utilized to compute Heart Rate Variability (HRV) and was sampled at a rate of 1024 Hz. The same AD equipment was also used to capture a pulse signal simultaneously. The right index finger was used as the recording site for the pulse signal using photoplethysmography (PPG) technology. Results: The participants' demographic data show that the mean age was 23.14 + 5.27 years, the mean weight was 73.68 +  7.40 kg, the mean body fat percentage was 32.23   +  5.30, and the mean visceral fat percentage was 4.60   +  2.0. The findings revealed no noticeable difference between the median values of heart rate variability (HRV) and PRV. Additionally, a strong correlation was observed between HRV and PRV. However, poor agreement was observed in the measurement of PRV and HRV. Conclusion: All indices of HRV showed a greater correlation with PRV. However, the level of agreement between HRV and PRV measurement was poor. Hence, HRV cannot be replaced with PRV and vice-versa.

Research on heart rate extraction method based on mobile phone video.

As an important indicator of human health, heart rate is related to the diagnosis of cardiovascular diseases. In recent years, extracting the heart rate from the mobile phone image has become a research hotspot. However, the illumination intensity of the background, frame rate of the video, and resolution of the image influence heart rate detection accuracy. To overcome these problems, this study proposed a novel heart rate extraction method based on mobile video. Firstly, the mobile phone camera is engaged to record the finger video, the region of interest (ROI) is extracted through the iterative threshold, and the pulse signal is obtained according to the grayscale change of the resolution within the ROI. Then, a low-pass and a high-pass Butterworth filters are exploited to filter out the noise and interframes from the extracted pulse signal. Finally, an improved adaptive peak extraction algorithm is proposed to detect the pulse peaks and the heart rate derived from the difference in pulse peaks. The experimental results show that light intensity, frame rate and resolution all have an influence on the heart rate extraction accuracy, with the most obvious influence of light, the average accuracy of the experiment can reach 99.32 % under good lighting conditions, while only 72.23 % under poor lighting conditions. In terms of frame rate, increasing the frame rate from 30 fps to 60 fps, the accuracy is improved by 0.9 %. For the resolution, increasing the resolution from 1080 p to 2160 p, the accuracy is improved by 1.12 %. While comparing the proposed method with existing methods, the proposed method has a higher accuracy rate, which has important practical value and application prospects in telemedicine and daily monitoring.

A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis.

BACKGROUND: The timely assessment of B-type natriuretic peptide (BNP) marking chronic heart failure risk in patients with coronary heart disease (CHD) helps to reduce patients' mortality. OBJECTIVE: To evaluate the potential of wrist pulse signals for use in the cardiac monitoring of patients with CHD. METHODS: A total of 419 patients with CHD were assigned to Group 1 (BNP < 95 pg/mL, n = 249), 2 (95 < BNP < 221 pg/mL, n = 85), and 3 (BNP > 221 pg/mL, n = 85) according to BNP levels. Wrist pulse signals were measured noninvasively. Both the time-domain method and multiscale entropy (MSE) method were used to extract pulse features. Decision tree (DT) and random forest (RF) algorithms were employed to construct models for classifying three groups, and the models' performance metrics were compared. RESULTS: The pulse features of the three groups differed significantly, suggesting different pathological states of the cardiovascular system in patients with CHD. Moreover, the RF models outperformed the DT models in performance metrics. Furthermore, the optimal RF model was that based on a dataset comprising both time-domain and MSE features, achieving accuracy, average precision, average recall, and average F1-score of 90.900%, 91.048%, 90.900%, and 90.897%, respectively. CONCLUSIONS: The wrist pulse detection technology employed in this study is useful for assessing the cardiac function of patients with CHD.

A fast sample entropy for pulse rate variability analysis.

Sample entropy is an effective nonlinear index for analyzing pulse rate variability (PRV) signal, but it has problems with a large amount of calculation and time consumption. Therefore, this study proposes a fast sample entropy calculation method to analyze the PRV signal according to the microprocessor process of data updating and the principle of sample entropy. The simulated data and PRV signal are employed as experimental data to verify the accuracy and time consumption of the proposed method. The experimental results on simulated data display that the proposed improved sample entropy can improve the operation rate of the entropy value by a maximum of 47.6 times and an average of 28.6 times and keep the entropy value unchanged. Experimental results on PRV signal display that the proposed improved sample entropy has great potential in the real-time processing of physiological signals, which can increase approximately 35 times.

Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN.

For the past several years, there has been an increasing focus on deep learning methods applied into computational pulse diagnosis. However, one factor restraining its development lies in the small wrist pulse dataset, due to privacy risks or lengthy experiments cost. In this study, for the first time, we address the challenging by presenting a novel one-dimension generative adversarial networks (GAN) for generating wrist pulse signals, which manages to learn a mapping strategy from a random noise space to the original wrist pulse data distribution automatically. Concretely, Wasserstein GAN with gradient penalty (WGAN-GP) is employed to alleviate the mode collapse problem of vanilla GANs, which could be able to further enhance the performance of the generated pulse data. We compared our proposed model performance with several typical GAN models, including vanilla GAN, deep convolutional GAN (DCGAN) and Wasserstein GAN (WGAN). To verify the feasibility of the proposed algorithm, we trained our model with a dataset of real recorded wrist pulse signals. In conducted experiments, qualitative visual inspection and several quantitative metrics, such as maximum mean deviation (MMD), sliced Wasserstein distance (SWD) and percent root mean square difference (PRD), are examined to measure performance comprehensively. Overall, WGAN-GP achieves the best performance and quantitative results show that the above three metrics can be as low as 0.2325, 0.0112 and 5.8748, respectively. The positive results support that generating wrist pulse data from a small ground truth is possible. Consequently, our proposed WGAN-GP model offers a potential innovative solution to address data scarcity challenge for researchers working with computational pulse diagnosis, which are expected to improve the performance of pulse diagnosis algorithms in the future.

Intelligent Wearable Wrist Pulse Detection System Based on Piezoelectric Sensor Array.

The human radial artery pulse carries a rich array of biomedical information. Accurate detection of pulse signal waveform and the identification of the corresponding pulse condition are helpful in understanding the health status of the human body. In the process of pulse detection, there are some problems, such as inaccurate location of radial artery key points, poor signal noise reduction effect and low accuracy of pulse recognition. In this system, the pulse signal waveform is collected by the main control circuit and the new piezoelectric sensor array combined with the wearable wristband, creating the hardware circuit. The key points of radial artery are located by an adaptive pulse finding algorithm. The pulse signal is denoised by wavelet transform, iterative sliding window and prediction reconstruction algorithm. The slippery pulse and the normal pulse are recognized by feature extraction and classification algorithm, so as to analyze the health status of the human body. The system has accurate pulse positioning, good noise reduction effect, and the accuracy of intelligent analysis is up to 98.4%, which can meet the needs of family health care.

Electrophysiological predictors of hyperfunctional dysphonia.

BACKGROUND: The relation between the autonomic nervous system (ANS) and muscles of the vocal tract is of particular importance when considering the pathomechanism of a functional voice disorder. AIMS: The aim of this study was to record electrophysiological indicators from the ANS as well as the tone of the external laryngeal muscle and test whether together they could point to an enhanced risk of primary functional voice disorder. MATERIALS AND METHODS: The study material consisted of 81 people, 27 of whom were professional opera singers. None reported any voice complaints. The research comprised ENT and phoniatric examination, superficial electromyography (SEMG), and recording of physiological indicators (pulse rate, skin resistance). RESULTS: All subjects had a clear voice with no sign of vocal disability. Endoscopy revealed laryngeal hyperfunction in 26 people. SEMG revealed that the 26 had increased external laryngeal muscle tone during phonation, and this finding correlated with a change in certain electrophysiological indicators HRV, BVP, EDA. CONCLUSIONS: We conclude that anomalies in electrophysiological parameters in individuals with subclinical symptoms of functional voice disorder may be at risk of developing fully symptomatic hyperfunctional dysphonia in the future. Vocal training, which differentiates singers and non-singers, is known to have an effect on subclinical hyperfunctional dysphonia. SIGNIFICANCE: By measuring indicators of hyperfunctional dysphonia, it may be possible to take remedial action before symptomatic dysphonia develops.

Vascular age acquired from the pulse signal: A new index to screen early vascular aging.

BACKGROUND: Chronological age (CA) has been adopted as an important independent risk factor in cardiovascular risk assessment. However, different individuals with same CA may have distinct actual vascular aging due to various lifestyles. Therefore, it is difficult to fully describe the difference of actual vascular aging by CA. OBJECTIVE: This study proposes a new index vascular age (VA) to avoid the limitations of CA. METHOD: In this work, VA refers to the sum of CA and lifestyle impact (AgeLI). Firstly, we take the pulse signal features and CA as independent variables and dependent variable respectively, and adopt cross validation to train Support Vector Regression model. Then we acquire the predicted chronological age (PA) of all subjects with the model. Secondly, we obtain the function model between CA and PA, and calculate the expectation of PA (ePA) for each subject. Simultaneously, we take the difference between PA and ePA as the estimated value of AgeLI to further calculate VA. Finally, in order to evaluate the effectiveness of VA, we compare the correlations between CA, PA, VA and 8 objective indices such as augmentation index, pulse transit time, diastolic augmentation index, etc. RESULTS: In general, VA and PA are closer to these 8 objective indices than CA. Moreover, VA is also superior to PA in vascular aging evaluation. CONCLUSION: The VA suggested in this study emphasizes the difference of vascular aging in same CA group, which can better reflect the actual vascular aging than CA and PA.

Head hemodynamics and systemic responses during auditory stimulation.

The present study aims to analyze the systemic response to auditory stimulation by means of hemodynamic (cephalic and peripheral) and autonomic responses in a broad range of auditory intensities (70.9, 77.9, 84.5, 89.5, 94.5 dBA). This approach could help to understand the possible influence of the autonomic nervous system on the cephalic blood flow. Twenty-five subjects were exposed to auditory stimulation while electrodermal activity (EDA), photoplethysmography (PPG), electrocardiogram, and functional near-infrared spectroscopy signals were recorded. Seven trials with 20 individual tones, each for the five intensities, were presented. The results showed a differentiated response to the higher intensity (94.5 dBA) with a decrease in some peripheral signals such as the heart rate (HR), the pulse signal, the pulse transit time (PTT), an increase of the LFnu power in PPG, and at the head level a decrease in oxygenated and total hemoglobin concentration. After the regression of the visual channel activity from the auditory channels, a decrease in deoxyhemoglobin in the auditory cortex was obtained, indicating a likely active response at the highest intensity. Nevertheless, other measures, such as EDA (Phasic and Tonic), and heart rate variability (Frequency and time domain) showed no significant differences between intensities. Altogether, these results suggest a systemic and complex response to high-intensity auditory stimuli. The results obtained in the decrease of the PTT and the increase in LFnu power of PPG suggest a possible vasoconstriction reflex by a sympathetic control of vascular tone, which could be related to the decrease in blood oxygenation at the head level.

A Procedure for Precise Determination and Compensation of Lead-Wire Resistance of a Two-Wire Resistance Temperature Detector.

A procedure for the precise determination and compensation of the lead-wire resistance of a resistance transducer is presented. The proposed technique is suitable for a two-wire resistance transducer, especially the resistance temperature detector (RTD). The proposed procedure provides a technique to compensate for the lead-wire resistance using a three-level pulse signal to excite the RTD via the long lead wire. In addition, the variation in the lead-wire resistance disturbed by the change in the ambient temperature can also be compensated by using the proposed technique. The determination of the lead-wire resistance from the proposed procedure requires a simple computation method performed by a digital signal processing unit. Therefore, the calculation of the RTD resistance and the lead-wire resistance can be achieved without the requirement of a high-speed digital signal processing unit. The proposed procedure is implemented on two platforms to confirm its effectiveness: the LabVIEW computer program and the microcontroller board. Experimental results show that the RTD resistance was accurately acquired, where the measured temperature varied from 0 °C to 300 °C and the lead-wire resistance varied from 0.2 Ω to 20 Ω, corresponding to the length of the 26 American wire gauge (AWG) lead wire from 1.5 m to 150 m. The average power dissipation to the RTD was very low and the self-heating of the RTD was minimized. The measurement error of the RTD resistance observed for pt100 was within ±0.98 Ω or ±0.27 °C when the lead wire of 30 m was placed in an environment with the ambient temperature varying from 30 °C to 70 °C. It is evident that the proposed procedure provided a performance that agreed with the theoretical expectation.

Wrist pulse signal based vascular age calculation using mixed Gaussian model and support vector regression.

Purpose: Vascular age (VA) is the direct index to reflect vascular aging, so it plays a particular role in public health. How to obtain VA conveniently and cheaply has always been a research hotspot. This study proposes a new method to evaluate VA with wrist pulse signal. Methods: Firstly, we fit the pulse signal by mixed Gaussian model (MGM) to extract the shape features, and adopt principal component analysis (PCA) to optimize the dimension of the shape features. Secondly, the principal components and chronological age (CA) are respectively taken as the independent variables and dependent variable to establish support vector regression (SVR) model. Thirdly, the principal components are fed into the SVR model to predicted the vascular aging of each subject. The predicted value is regarded as the description of VA. Finally, we compare the correlation coefficients of VA with pulse width (PW), inflection point area ratio (IPA), Ratio b/a (RBA), augmentation index (AIx), diastolic augmentation index (DAI) and pulse transit time (PTT) with those of CA with these six indices. Results: Compared with the CA, the VA is closer to PW (r = 0.539, P < 0.001 to r = 0.589, P < 0.001 in men; r = 0.325, P < 0.001 to r = 0.400, P < 0.001 in women), IPA (r = - 0.446, P < 0.001 to r = - 0.534, P < 0.001 in men; r = - 0.623, P < 0.001 to r = - 0.660, P < 0.001 in women), RBA (r = 0.328, P < 0.001 to r = 0.371, P < 0.001 in women), AIx (r = 0.659, P < 0.001 to r = 0.738, P < 0.001 in men; r = 0.547, P < 0.001 to r = 0.573, P < 0.001 in women), DAI (r = 0.517, P < 0.001 to r = 0.532, P < 0.001 in men; r = 0.507, P < 0.001 to r = 0.570, P < 0.001 in women) and PTT (r = 0.526, P < 0.001 to r = 0.659, P < 0.001 in men; r = 0.577, P < 0.001 to r = 0.814, P < 0.001 in women). Conclusion: The VA is more representative of vascular aging than CA. The method presented in this study provides a new way to directly and objectively assess vascular aging in public health.

A Wearable and Real-Time Pulse Wave Monitoring System Based on a Flexible Compound Sensor.

Continuous monitoring of pulse waves plays a significant role in reflecting physical conditions and disease diagnosis. However, the current collection equipment cannot simultaneously achieve wearable and continuous monitoring under varying pressure and provide personalized pulse wave monitoring targeted different human bodies. To solve the above problems, this paper proposed a novel wearable and real-time pulse wave monitoring system based on a novel flexible compound sensor. Firstly, a custom-packaged pressure sensor, a signal stabilization structure, and a micro pressurization system make up the flexible compound sensor to complete the stable acquisition of pulse wave signals under continuously varying pressure. Secondly, a real-time algorithm completes the analysis of the trend of the pulse wave peak, which can quickly and accurately locate the best pulse wave for different individuals. Finally, the experimental results show that the wearable system can both realize continuous monitoring and reflecting trend differences and quickly locate the best pulse wave for different individuals with the 95% accuracy. The weight of the whole system is only 52.775 g, the working current is 46 mA, and the power consumption is 160 mW. Its small size and low power consumption meet wearable and portable scenarios, which has significant research value and commercialization prospects.

Study of a full-digital multi-waveform nuclear pulse signal generator.

The traditional nuclear pulse signal generator outputs the nuclear pulse signal of specific waveform according to the input pulse amplitude probability distribution and counting rate, following the signal output laws of radiation detector in both pulse amplitude and time interval. However, the output waveform is generally regulated by an analog circuit, with the single waveform and difficult parameter adjustment. In this study, the digital C-R and R-C filters were explored, a cascading digital C-R or R-C filter algorithm was proposed, realizing multiple pragmatic nuclear pulse signal outputs through the serial or parallel connection of multiple digital filters. The actual test results show that the nuclear pulse signal generator constructed by this algorithm can simulate the nuclear pulse signals under different detectors and counting rates, thus expanding the scope of application and improving the flexibility of digital nuclear pulse signal generators.

A Novel Pulse-Taking Device for Persian Medicine Based on Convolutional Neural Networks.

Background: In Persian medicine (PM), measuring the wrist pulse is one of the main methods for determining a person's health status and temperament. One problem that can arise is the dependence of the diagnosis on the physician's interpretation of pulse wave features. Perhaps, this is one reason why this method has yet to be combined with modern medical methods. This paper addresses this concern and outlines a system for measuring pulse signals based on PM. Methods: A system that uses data from a customized device that logs the pulse wave on the wrist was designed and clinically implemented based on PM. Seven convolutional neural networks (CNNs) have been used for classification. Results: The pulse wave features of 34 participants were assessed by a specialist based on PM principles. Pulse taking was done on the wrist in the supine position (named Malmas in PM) under the supervision of the physician. Seven CNNs were implemented for each participant's pulse characteristic (pace, rate, vessel elasticity, strength, width, length, and height) assessment, and then, each participant was classified into three classes. Conclusion: It appears that the design and construction of a customized device combined with the deep learning algorithm can measure the pulse wave features according to PM and it can increase the reliability and repeatability of the diagnostic results based on PM.

Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study.

BACKGROUND: In pulse signal analysis and identification, time domain and time frequency domain analysis methods can obtain interpretable structured data and build classification models using traditional machine learning methods. Unstructured data, such as pulse signals, contain rich information about the state of the cardiovascular system, and local features of unstructured data can be extracted and classified using deep learning. OBJECTIVE: The objective of this paper was to comprehensively use machine learning and deep learning classification methods to fully exploit the information about pulse signals. METHODS: Structured data were obtained by using time domain and time frequency domain analysis methods. A classification model was built using a support vector machine (SVM), a deep convolutional neural network (DCNN) kernel was used to extract local features of the unstructured data, and the stacking method was used to fuse the above classification results for decision making. RESULTS: The highest average accuracy of 0.7914 was obtained using only a single classifier, while the average accuracy obtained using the ensemble learning approach was 0.8330. CONCLUSIONS: Ensemble learning can effectively use information from structured and unstructured data to improve classification accuracy through decision-level fusion. This study provides a new idea and method for pulse signal classification, which is of practical value for pulse diagnosis objectification.

[Portable Pulse Detection System Based on IoT].

Aiming at the current situation of high cost, huge volume, complex operation and difficulty in real application of pulse analyzer, this study designs and implements a portable pulse detection system based on IoT. The design utilizes Raspberry Pi 3B+, STM32 series MCU and cloud server to collect, store, display and recognize pulse signals at CUN, GUAN and CHI. The system is small in size and low in cost, which can be connected with cloud server through network to make full use of resources. The experimental results show that the recognition accuracy of the main feature points of the pulse signal by the portable pulse analyzer is higher than 97%, which has a broad prospect of development and application.

High selectivity sensing of bovine serum albumin: The combination of glass nanopore and molecularly imprinted technology.

Recently, resistive-pulse based nanopore analysis has made great progress. However, when it is used for the detection of similar substances, such as proteins, DNA, RNA and other biological molecules, the signal is poor. In order to realize the purpose of good testing, the solutions proposed by researchers include surface modification of nanopore or special data processing equipment and software, which are still complicated. In this manuscript, molecularly imprinted technology and nanopore sensing technology are combined together, based on the specific rebinding of molecularly imprinted polymer coated SiO2 nanoparticles to bovine serum albumin, the nanoparticles at different imprinting stages can be distinguished through resistive-pulse signals when they pass through the nanopores, achieving high selective and sensitive detection of bovine serum albumin in complex samples. The linear range of this nanopore-based sensor is from 0.5 nmol/L to 50 nmol/L with a 0.3 nmol/L detection limit. This method also possesses the advantages of strong specificity, good selectivity of molecularly imprinted polymer for target recognition and adsorption, and the controllable diameter and easy preparation of solid nanopores. Compared with the antibody-based or aptamer-based protein detection strategy, method in this manuscript is simple and easy to implement, which not only provides good selectivity and sensitivity for the detection of proteins in complex biomedical samples, but also opens up new application fields for nanopore sensing.

Portable Pulse Detection System Based on IoT / 中国医疗器械杂志

Aiming at the current situation of high cost, huge volume, complex operation and difficulty in real application of pulse analyzer, this study designs and implements a portable pulse detection system based on IoT. The design utilizes Raspberry Pi 3B+, STM32 series MCU and cloud server to collect, store, display and recognize pulse signals at CUN, GUAN and CHI. The system is small in size and low in cost, which can be connected with cloud server through network to make full use of resources. The experimental results show that the recognition accuracy of the main feature points of the pulse signal by the portable pulse analyzer is higher than 97%, which has a broad prospect of development and application.

[Study on a quantitative analysis method for pulse signal by modelling its waveform in time and space domain].

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.