Wrist Pulse Signals Research Articles

Development of Highly Flexible Piezoelectric PVDF-TRFE/Reduced Graphene Oxide Doped Electrospun Nano-Fibers for Self-Powered Pressure Sensor.

The demand for self-powered, flexible, and wearable electronic devices has been increasing in recent years for physiological and biomedical applications in real-time detection due to their higher flexibility and stretchability. This work fabricated a highly sensitive, self-powered wearable microdevice with Poly-Vinylidene Fluoride-Tetra Fluoroethylene (PVDF-TrFE) nano-fibers using an electrospinning technique. The dielectric response of the polymer was improved by incorporating the reduced-graphene-oxide (rGO) multi-walled carbon nano-tubes (MWCNTs) through doping. The dielectric behavior and piezoelectric effect were improved through the stretching and orientation of polymeric chains. The outermost layer was attained by chemical vapor deposition (CVD) of conductive polymer poly (3,4-ethylenedioxythiophene) to enhance the electrical conductivity and sensitivity. The hetero-structured nano-composite comprises PVDF-TrFE doped with rGO-MWCNTs over poly (3,4-ethylenedioxythiophene) (PEDOT), forming continuous self-assembly. The piezoelectric pressure sensor is capable of detecting human physiological vital signs. The pressure sensor exhibits a high-pressure sensitivity of 19.09 kPa-1, over a sensing range of 1.0 Pa to 25 kPa, and excellent cycling stability of 10,000 cycles. The study reveals that the piezoelectric pressure sensor has superior sensing performance and is capable of monitoring human vital signs, including heartbeat and wrist pulse, masticatory movement, voice recognition, and eye blinking signals. The research work demonstrates that the device could potentially eliminate metallic sensors and be used for early disease diagnosis in biomedical and personal healthcare applications.

A Fully Self‐Healing Patch of Integrated Bio‐Signal Monitoring Sensors with Self‐Healing Microporous Foam and Au Nanosheet Electrodes

AbstractA fully self‐healing sensor patch consisting of an in‐plane integrated dual‐mode sensor and a pressure sensor with a vertically integrated electrocardiogram (ECG) electrode is reported. For a full‐device self‐healing, interlayer self‐bonding by using self‐healing oxime‐carbamate bond‐based polyurethane and room temperature self‐healing TUEG3 capped Au nanosheet (T‐Au NS) electrodes are devised. Via the use of a self‐healing sensor foam prepared by a coating of a self‐healing composite of polyurethane and polyaniline onto a microporous graphene foam, a self‐healing dual‐mode sensor is fabricated to detect the pressure and temperature simultaneously without interference. Furthermore, the interdigitated self‐healing electrode of T‐Au NS is applied to the pressure sensor to enhance the sensitivity up to 208.62 kPa−1 (<1 kPa), enabling the detection of small pulse signals even after multiple self‐healing events. By using the common self‐healing T‐Au NS electrode vertically integrated onto the sensor patch, ECG signals are also detected. With this sensor patch, skin temperature, wrist pulse, and ECG signals are successfully detected after a simultaneous full‐device self‐healing from complete bisection. This study demonstrates the facile fabrication of a high‐performance, fully self‐healing patch of multi‐sensors via the deliberate selection of sensor design and the associated functional materials, confirming its high potential applicability to highly durable health monitoring systems.

Single-Electrode Triboelectric Nanogenerator Device Framework for Wrist Pulse Signal (Nadi) Analysis and Disease Detection

Single-Electrode Triboelectric Nanogenerator Device Framework for Wrist Pulse Signal (Nadi) Analysis and Disease Detection

Evaluation of Current Trends in Biomedical Applications Using Soft Computing

Abstract: With the rapid advancement in analyzing high-volume and complex data, machine learning has become one of the most critical and essential tools for classification and prediction. This study reviews machine learning (ML) and deep learning (DL) methods for the classification and prediction of biological signals. The effective utilization of the latest technology in numerous applications, along with various challenges and possible solutions, is the main objective of this present study. A PICO-based systematic review is performed to analyze the applications of ML and DL in different biomedical signals, viz. electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and wrist pulse signal from 2015 to 2022. From this analysis, one can measure machine learning's effectiveness and key characteristics of deep learning. This literature survey finds a clear shift toward deep learning techniques compared to machine learning used in the classification of biomedical signals.

Development of a novel wrist pulse system for early diagnosis of pathogenic bacterial infections using optimized feature selection with machine learning approaches

Pelvic inflammatory disease (PID) and urinary tract infections (UTI) are two Pathogenic bacterial infections. PID affects the female reproductive system, whereas UTI affects the urine system. Females are more vulnerable to both forms of illnesses and challenging to detect simultaneously due to similar symptoms. Many Clinical procedures have previously been used to diagnose the diseases, but they are painful, costly, prone to radiation, and invasive. Therefore, this paper proposed the Ayurvedic and Traditional Chinese Medicine [TCM] based wrist pulse measurement to diagnose these bacterial infections non-invasively. An Artery tonometry-based test prototype was designed to measure the pulse signals at three doshas (Vata, Pitta, Kapha) locations on the radial artery under variable static force. The data is recorded at an intermediate force level to get maximum accuracy. Signals feature viz., time domain feature, autoregressive (AR) model features, approximation entropy, sample entropy, and multiscale entropy have been calculated. Further, the efficiency of the machine learning model is enhanced through optimization-based feature selection techniques. Three different optimization-based feature selection, viz., whale optimization (WOA), ant colony optimization (ACO), and proposed binary Greywolf optimization (BGWO), has been proposed to acquire the optimized features subsets for accurate classification of disease subjects. These subsets have been used by popular machine learning techniques to classify the PID and UTI. The results of the experiment demonstrate that as the static force changed, the wrist pulse signal first increased to an intermediate force level and then decreased. Second, the classification accuracy of the machine learning model with feature selection is more than conventional feature classification. Third, the wrist pulse analysis (WPA) shows the maximum accuracy in comparison to conventional clinical PID diagnosis, where Vata dosha has the highest classification accuracy (94.1%) when using BGWO-SVM, followed by Pitta dosha (88.2%) when using BGWO-BNN. Similarly, specificity has been used for UTI classification, where Vata dosha has the highest specificity of 100% with BGWO-SVM, followed by pitta dosha with 83.3% BGWO-BNN. Finally, three dosha classification findings demonstrate that Vata and Pitta doshas have been more significantly impacted, as they have the highest classification accuracy compared to conventional clinical diagnosis, which produces similar results consistent with ayurvedic literature. Therefore, WPA can provide a unique diagnostic method for early PID/UTI identification.

An early screening model of pulse detection technology for hepatic steatosis

BackgroundThe increasing prevalence of hepatic steatosis presents a considerable challenge to public health. There is a critical need for the development of novel preventive and screening strategies for this condition. This study evaluated the potential applications of wrist pulse detection technology for the early detection of liver diseases. The pulse time-domain features of a medical exam population with and without hepatic steatosis were assessed to develop a screening model for this disease. MethodsParticipants were consecutively recruited from March 2021 to March 2022 in the medical examination centers of the Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and the Shanghai Municipal Hospital of Traditional Chinese Medicine. Clinical data from 255 participants, including general information (sex, age, and body mass index), and data related to glucose and blood lipids (fasting plasma glucose, triglyceride, total cholesterol, high-density lipoprotein, and low-density lipoprotein levels) were collected. Wrist pulse signals were acquired using a pulse detection device, and the pulse time-domain features, including t1, t4, t5, T, w1, w2, h2/h1, h3/h1, and h5/h1 were extracted. Participants were assigned to hepatic steatosis and non-hepatic steatosis groups according to their abdominal ultrasound examination results. Their clinical data and pulse time-domain features were compared using chi-square and parametric or non-parametric statistical methods. Three datasets were used to construct screening models for hepatic steatosis based on the random forest algorithm. The datasets for modeling were defined as Dataset 1, containing blood glucose and lipid data and general information; Dataset 2, containing time-domain features and general information; Dataset 3, containing time-domain features, blood glucose and lipid data, and general information. The evaluation metrics, accuracy, precision, recall, F1-score, and areas under the receiver operating characteristic curve (AUC) were compared for each model. ResultsThe time-domain features of the two groups differed significantly. The t1, t4, t5, T, h2/h1, h3/h1, w1, and w2 features were higher in the hepatic steatosis group than in the non-hepatic steatosis group (P < 0.05), while the h5/h1 features were lower in the hepatic steatosis group than in the non-hepatic steatosis group (P < 0.05). The screening models for hepatic steatosis based on both time-domain features and blood glucose and lipid data outperformed those based on time-domain features or blood markers alone. The accuracy, precision, recall, F1-score, and AUC of the combined model were 81.18%, 80.56%, 76.32%, 79%, and 87.79%, respectively. These proportions were 1.57%, 1.86%, 1.76%, 2%, and 3.54% higher, respectively, than those of the model based on time-domain features alone and 3.14%, 4.2%, 2.64%, 4%, and 6.47% higher, respectively, than those of the model based on blood glucose and lipid alone. ConclusionThe early screening model for hepatic steatosis using datasets that included pulse time-domain features achieved better performance. The findings suggest that pulse detection technology could be used to inform the development of a mobile medical device or remote home monitoring system to test for hepatitis steatosis.

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

BackgroundThe 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.ObjectiveTo evaluate the potential of wrist pulse signals for use in the cardiac monitoring of patients with CHD.MethodsA 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.ResultsThe 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.ConclusionsThe wrist pulse detection technology employed in this study is useful for assessing the cardiac function of patients with CHD.

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.

Wrist Pulse Feature of Pneumoconiosis and its Application

AbstractPneumoconiosis has been the most harmful occupational disease for coal miners for decades and is difficult to cure completely. If this disease can be diagnosed and cured in time, it can be effectively controlled. Currently, the most reliable clinical diagnosis method is using X‐ray or high‐resolution computerized tomography (CT). Here, we propose a method to diagnose pneumoconiosis using wrist pulse signals. First, a pulse sensor is used to collect wrist pulse signals, then, preprocessing is conducted, and a single period of a pulse signal is separated. Second, a 13‐dimensional feature is extracted in the time, frequency, and wavelet domains. Finally, a support vector machine, back propagation neural network, or random forest classifier are used to classify the pulse signals, two voting procedures are used and the diagnosis result can be achieved. The lowest recognition accuracy is 85.16%. The experimental results show that our 13‐dimensional feature can be used as the main feature of pneumoconiosis diagnosis. The proposed method provides a more reliable auxiliary means for the diagnosis of coal mine pneumoconiosis. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Automated Segmentation of the Systolic and Diastolic Phases in Wrist Pulse Signal Using Long Short-Term Memory Network.

Purpose Single-period segmentation is one of the important steps in time-domain analysis of pulse signals, which is the basis of time-domain feature extraction. The existing single-period segmentation methods have the disadvantages of generalization, reliability, and robustness. Method This paper proposed a period segmentation method of pulse signals based on long short-term memory (LSTM) network. The preprocessing was performed to remove noises and baseline drift of pulse signals. Thus, LabelMe was used to label each period of the pulse signals into two parts according to the location of the starting point of main wave and the dicrotic notch, and the dataset of the pulse signal period segmentation was established. Consequently, the labeled dataset was input into the LSTM for training and testing, and the results were compared with sum slope function method. Result The remarkable result with the whole period segmentation accuracy of 92.8% was achieved for the segmentation of seven types of pulse signals. And the segmentation accuracies of the systolic phase, diastolic phase, and whole period using this method were higher than those of the sum slope function method. Conclusion LSTM-based pulse signal segmentation method can achieve outstanding, robust, and reliable segmentation effects of the systolic phase, diastolic phase, and whole period of pulse signals. The research provides a new idea and method for the segmentation of pulse signals.

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

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. 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 augmentationindex (DAI) and pulse transit time (PTT) with those of CA with these six indices. 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). 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.

Pressure wrist pulse signal analysis by sparse decomposition using improved Gabor function

Background and objective: In traditional Chinese medicine and Ayurvedic medicine, wrist pulse wave fluctuations are an important indicator for distinguishing different health states. Owing to the development of modern sensing technology, computational methods have been used in the analysis of pulse wave signals. The description and quantification of the peaks in the pulse wave is significant for the identification of health status.Methods: In this study, we decomposed the pressure pulse waveform of the radial artery into several components by sparse decomposition with an improved Gabor function. To better represent the position, shape, and relationship of the peaks, we designed an improved Gabor function structure based on the characteristics of the pulse waveform to generate a time-frequency dictionary. Compared with conventional representation methods, the shape of the Gabor function is more variable. In addition, owing to the limitation of windowing, the Gabor function can reduce the influence on other positions when it represents a specific position. Feature vectors consisting of decomposed components can be used for computerized pulse signal analysis and disease diagnosis.Results: In the binary classification of healthy and diseased pulse signals, the proposed method achieved the best results for health/diabetes, health/cardiac disease, health/hypertension, and health/nephropathy with accuracies of 93.54%, 73.42%, 88.42%, and 82.28%, respectively. The multi-classification performance of the different types of features was evaluated by six classifiers, and the proposed method obtained the highest classification performance with support vector machine-radial basis function for both balanced and imbalanced data.Conclusions: The results indicated that the proposed method enabled to obtain a smaller representation error and exhibited superior performance in distinguishing between the signals collected from patients and healthy individuals. Moreover, for the multi-classification of the pulse signals, the proposed method performed better than the state-of-the-art methods.

Refined Multiscale Entropy Analysis of Wrist Pulse for Gender Difference in Traditional Chinese Medicine among Young Individuals.

Pulse signal analysis plays an important role in promoting the objectification of traditional Chinese medicine (TCM). Like electrocardiogram (ECG) signals, wrist pulse signals are mainly caused by cardiac activities and are valuable in analyzing cardiac diseases. A large number of studies have reported ECG signals can distinguish gender characteristics of normal healthy subjects using entropy complexity measures, consistently showing more complexity in females than males. No research up to date, however, has been found on examining gender differences with wrist pulse signals of healthy subjects on entropy complexity measures. This paper is aimed to fill in the research gap, which could, in turn, provide a deeper insight into the pulse signal and might identify potential differences between ECG signals and pulse signals. In particular, several complementary entropy measures with corresponding refined composite multiscale versions are established to perform the analysis for the filtered TCM pulse signals. Experimental results reveal that regardless of entropy measures used, there is no statistically significant gender difference in terms of entropy complexity, indicating that the pulse signal holds less gender characteristics than the ECG signal. In view of these findings, wrist pulse signals could be likely to provide different pieces of information to ECG signals. The present study is the first to quantitatively evaluate gender differences in healthy pulse signals with measures of entropy complexity and more importantly; we expect this study could make contribution to the ongoing pulse intelligent diagnosis and objective analysis, further facilitating the modernization of TCM pulse diagnosis.

NA-MEMD Is Actually What You Need for Computational Pulse Analysis

Time-frequency analysis is of necessity for wrist pulse signal due to its complexity, among which, empirical mode decomposition (EMD) algorithm and its improved noise-assisted versions (such as ensemble EMD, noise-assisted multivariate EMD (NA-MEMD) and very recently median EMD) are deemed to be the most representative ones. In this study, we provide an in-depth evaluation of these well-established noise-assisted EMD algorithms in computational pulse analysis for the first time. In particular, we compare the performance of the different algorithms systematically and quantitatively based on objective quantitative criteria: number and central frequency of intrinsic mode function (IMF) components, total orthogonality index and mode mixing. Rather than using synthetic signals with visual inspection in most existing literature, the wrist pulse signals used in the evaluation are real recorded samples acquired from both healthy and patient subjects. Through extensive experiments, we found that: 1) Advanced EMD algorithm that has the best performance in other areas may not be the most suitable method for pulse signal analysis, which indicates its high dependence on the type of signal to be analyzed; 2) Adding noise can significantly improve algorithm performance, but tends to produce physiologically not relevant components, which however are usually neglected throughout the intelligent pulse diagnosis literature. Therefore, excluding redundant components and then extracting features is expected to improve performance further. Together, currently NA-MEMD achieves a better performance consistently, potential to become a powerful tool for computational pulse analysis, which in itself have not been applied in wrist pulse analysis before. We believe our works can bring up a new perspective to application of EMD-like algorithms in computational pulse analysis/diagnosis with effective information and guidance. Additionally, considering the similarity between physiological signals, especially such as photoplethysmogram/electrocardiogram, our research can be extended to wearable health monitoring technologies, including smart watches and fitness trackers, and their potential future applications, such as in heart rate estimation and evaluate various cardiovascular-related diseases. The present study underscored the necessity of evaluation noise-assisted EMDs or other adaptive decomposition algorithms based on real recorded signals with more objective measures. Especially, caution the possible redundant components that are introduced.

Analysis of Wrist Pulse Signal: Emotions and Physical Pain

BackgroundPulse diagnosis (wrist pulse signal) is a well-known traditional technique used for a health examination. It has the potential to detect cardiac and non-cardiac diseases. ObjectiveA study was conducted to investigate human emotions using wrist pulse signal assessment. The aim was to categorize anxiety, boredom, physical pain, and reference state by processing and analysis of acquired signals. MethodA protocol was designed to induce emotions. Data were acquired from 24 healthy volunteers. Signals were processed and further analyzed using paired t-test and Analysis of Variance (ANOVA). Machine learning algorithms, Linear Discriminant Function (LDF), Quadratic Discriminant Function (QDF), and Support Vector Machine using kernel Gaussian radial basis function (RBF-SVM) were used to evaluate significant features and classify the emotions. ResultsComputing significant plus ranked features performed better over randomly selected features for pairwise emotion classification. Here, the QDF classifier outperforms LDF. Additionally, ANOVA validated the effectiveness of statistically prominent features to classify emotional states. Ratio_Pulse_Strength, total_power, Spectral Entropy, and meancd5 came out as the four most significant features to classify the emotion “Anxiety”, “Boredom”, “Pain”, and “Reference” with positive prediction rate of 100%, 73%, 100%, and 86% respectively using RBF-SVM in the user-independent model. ConclusionPreviously, WPS has been used mainly to detect physical abnormality in the human body. The results endorse the potential of user-independent human emotion detection using a wrist pulse signal. The present work was focusing on a few emotional states. Results are encouraging and may be well applied to many more states.

Exploring Wrist Pulse Signals using Empirical Mode Decomposition: Emotions

Emotion recognition is attracting considerable interest among the research community. In this work, Empirical Mode Decomposition has been implemented to derive both statistical and nonlinear features from Wrist Pulse Signal to classifying emotions namely anxiety and boredom. Wrist Pulse signals were extracted from 24 subjects using TETRIS game as a stimulus using Fission and Fusion approach. The acquired signals were pre-processed to remove unwanted noise and artefacts present within the signal. In addition, various classifiers namely Naiive Byes, Support Vector Machine, K-Nearest Neighbour, Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis were considered. Results from these classifiers indicate that both Logistic Regression and Quadratic Discriminant Analysis gave an indistinguishable accuracy of 99.71% (fission) and 77.08% (fusion) for anxiety state. Moreover, for boredom state, the highest classification accuracy was 66.67 % for Naiive Bayes using fission and 64.58% for fusion. Results highlight the impact of empirical mode decomposition with hilbert transform for the recognition of emotion from wrist pulse signals.

Graph Based Multichannel Feature Fusion for Wrist Pulse Diagnosis.

It is well known in Traditional Chinese Medicine (TCM) that a person's wrist pulse signal can reflect their health condition. Recently, many computerized wrist pulse AI systems have been proposed to simulate a practitioner's three fingers in order to acquire the wrist pulse signals (three positions/channels) from a candidate's wrist dynamically, before evaluating their health status based on the various feature extraction and detection methods. However, few works have investigated the correlation of the extracted features from the three wrist channels and comprehensively fused the various features together, which can improve the performance of wrist pulse diagnosis. In this paper, we propose a graph based multichannel feature fusion (GBMFF) method to utilize the multichannel features of the wrist pulse signals effectively. In detail, two different sensors, i.e., pressure and photoelectricity are used to capture the three channels of the wrist pulse signals. These are used to generate two different features by applying the stacked sparse autoencoder and wavelet scattering. Each feature of one wrist pulse sample is regarded as a node associated with its corresponding feature vector, and used to construct a graph for one candidate. A novel algorithm is implemented to construct different graphs for different candidates, which are used for wrist pulse diagnosis by developing graph convolutional networks. Experimental results indicate that our proposed AI-based method can obtain superior performances compared to other state-of-the-art approaches.

Denoising wrist pulse signals using variance thresholding technique

Background/Objectives: Denoising of the wrist pulse is a significant preprocessing stage for accurate investigation of the disease. The objective is to improve and analyze performance metrics of denoising techniques. Methods/Statistical analysis: Denoising of wrist pulse with the evaluation parameters such as PSNR, SNR, AE and RMSE has been implemented using wavelets such as Daubechies, Symlet and Biorthogonal. The performance of wavelets depends on the choice of decomposition level N and thresholding techniques. Findings: Variance thresholding technique showed significant improvement in Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR) and reduction in Absolute Error (AE) and Root Mean Square Error (RMSE) compared to other thresholding methods. Novelty/Applications: Experimental results showed drastic improvement in PSNR and SNR retaining the pathophysiological information of the wrist pulse signal for future analysis.

Three-Dimensional MOF-Assisted Self-Polarized Ferroelectret: An Effective Autopowered Remote Healthcare Monitoring Approach.

In recent years, flexible and sensitive pressure sensors are of extensive interest in healthcare monitoring, artificial intelligence, and national security. In this context, we report the synthetic procedure of a three-dimensional (3D) metal-organic framework (MOF) comprising cadmium (Cd) metals as nodes and isoniazid (INH) moieties as organic linkers (CdI2-INH═CMe2) for designing self-polarized ferroelectret-based highly mechano-sensitive skin sensors. The as-synthesized MOF preferentially nucleates the stable piezoelectric β-phase in poly(vinylidene fluoride) (PVDF) and also gives rise to a porous ferroelectret composite film. Benefiting from the porous structure of 3D MOFs, composite ferroelectret film-based ultrasensitive pressure sensor (mechano-sensitivity of 8.52 V/kPa within 1 kPa pressure range) as well as high-throughput ( power density of 32 μW/cm2) mechanical energy harvester (MEH) has been designed. Simulation-based finite element method (FEM) analysis indicates that the geometrical stress confinement effect within the interpore region of the ferroelectret structure synergistically influences the mechano-electrical property of the MEH. In addition, 143 pC/N (∼4.5 times higher than commercial piezoelectric PVDF films) piezoelectric charge coefficient (d33) magnitude and superior response time (tr ∼ 8 ms) of this composite ferroelectret film enable the detection of different physiological signals such as coughing, pronunciation, and gulping behavior, making it a promising candidate for early intervention of healthcare, which may play a significant role in accurate alert of influenza and chronic obstructive pulmonary disease (COPD)-related symptoms. In addition, MEH enables the tracking of the subtle pressure change in the wrist pulse, indicating its usefulness in effective mechano-sensitivity. Since the cardiovascular signal is one of the vital parameters that can determine the on-going physiological conditions, the wireless transmission of the detected wrist pulse signal has been demonstrated. All of these features coupled with wireless data transmission indicate the promising application of MOF-assisted composite ferroelectret films in noninvasive real-time remote healthcare monitoring.

A CNT-PDMS wearable device for simultaneous measurement of wrist pulse pressure and cardiac electrical activity.

Simultaneous measurement of multi-physiological signals can provide effective diagnosis and therapeutic assessment of diseases. This paper reports a carbon nanotube (CNT) - Polydimethylsiloxane (PDMS) - based wearable device with piezo-resistive and voltage-sensing capabilities for simultaneously capturing wrist pulse pressure and cardiac electrical signal. The layout design of sensing elements in the device was guided by analyzing strain distribution and electric field distribution for minimizing the interference between wrist pulse and cardiac electric activity during measurement. Each device was preconditioned under the strain of 20% until the resistance change of the device reached equilibrium. After preconditioning, the relationship between the resistance change and the pressure was calibrated, which determined the device sensitivity to be 0.01Pa-1 and the linear pressure range of the device to be 0.4kPa to 14.0kPa. Mechanisms of CNT-PDMS for sensing strain signal and electrical pulse signal were explored by scanning electron microscopy (SEM) imaging and equivalent circuit modeling. The device was applied to monitor the wrist pulse and ECG signals of volunteers during the recovering process after physical exercises.