Traditional Chinese Medicine Pulse Research Articles

Pioneering Data Processing for Convolutional Neural Networks to Enhance the Diagnostic Accuracy of Traditional Chinese Medicine Pulse Diagnosis for Diabetes.

Traditional Chinese medicine (TCM) has relied on pulse diagnosis as a cornerstone of healthcare assessment for thousands of years. Despite its long history and widespread use, TCM pulse diagnosis has faced challenges in terms of diagnostic accuracy and consistency due to its dependence on subjective interpretation and theoretical analysis. This study introduces an approach to enhance the accuracy of TCM pulse diagnosis for diabetes by leveraging the power of deep learning algorithms, specifically LeNet and ResNet models, for pulse waveform analysis. LeNet and ResNet models were applied to analyze TCM pulse waveforms using a diverse dataset comprising both healthy individuals and patients with diabetes. The integration of these advanced algorithms with modern TCM pulse measurement instruments shows great promise in reducing practitioner-dependent variability and improving the reliability of diagnoses. This research bridges the gap between ancient wisdom and cutting-edge technology in healthcare. LeNet-F, incorporating special feature extraction of a pulse based on TMC, showed improved training and test accuracies (73% and 67%, respectively, compared with LeNet's 70% and 65%). Moreover, ResNet models consistently outperformed LeNet, with ResNet18-F achieving the highest accuracy (82%) in training and 74% in testing. The advanced preprocessing techniques and additional features contribute significantly to ResNet18-F's superior performance, indicating the importance of feature engineering strategies. Furthermore, the study identifies potential avenues for future research, including optimizing preprocessing techniques to handle pulse waveform variations and noise levels, integrating additional time-frequency domain features, developing domain-specific feature selection algorithms, and expanding the scope to other diseases. These advancements aim to refine traditional Chinese medicine pulse diagnosis, enhancing its accuracy and reliability while integrating it into modern technology for more effective healthcare approaches.

Classification research of TCM pulse conditions based on multi-label voice analysis

ObjectiveTo explore the feasibility of remotely obtaining complex information on traditional Chinese medicine (TCM) pulse conditions through voice signals. MethodsWe used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning. Audio features were extracted from voice recordings in the TCM pulse condition dataset. The obtained features were combined with information from tongue and facial diagnoses. A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation, and the modeling methods were validated using publicly available datasets. ResultsThe analysis showed that the proposed method achieved an accuracy of 92.59% on the public dataset. The accuracies of the three single-label pulse manifestation models in the test set were 94.27%, 96.35%, and 95.39%. The absolute accuracy of the multi-label model was 92.74%. ConclusionVoice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.

A Protocol for Digitalized Collection of Traditional Chinese Medicine(TCM) Pulse Information Using Bionic Pulse Diagnosis Equipment.

Pulse diagnosis equipment used in Traditional Chinese Medicine (TCM) has long been developed for collecting pulse information and in TCM research. However, it is still difficult to implement pulse taking automaticallyorefficiently in clinical practice. Here, we present a digital protocol for TCM pulse information collection based on bionic pulse diagnosis equipment, which ensures high efficiency, reliability and data integrity of pulse diagnosis information. A four-degree-of-freedom pulse taking platform together with a wrist bracket can satisfy the spatial positioning and angle requirements for individually adaptive pulse acquisition. Three-dimensional reconstruction of a wrist surface and an image localization model are combined to provide coordinates of the acquisition position and detection direction automatically. Three series elastic joints can not only simulate the TCM pulse taking method that "Three fingers in a straight line, the middle finger determining the 'Guan' location and finger pulp pressing on the radial artery," but also simultaneously carry out the force-controlled multi-gradient pressing process. In terms of pulse information integrity, this proposed protocol can generate rich pulse information, including basic individual information, pulse localization distribution, multi-gradient dynamic pulse force time series, and objective pulse parameters, which can help establish the fundamental data sets that are required as the pulse phenotype for subsequent comprehensive analysis of pulse diagnosis. The implementation of this scheme is beneficial to promote the standardization of the digitalized collection of pulse information, the effectiveness of detecting abnormal health status, and the promotion of the fundamental and clinical research of TCM, such as TCM pulse phenomics.

Wearable Photoelectric Fingertip Force Sensing System Based on Blood Volume Changes without Sensory Interference.

Monitoring the force of fingertip manipulation without disturbing the natural sense of touch is crucial for digitizing the skills of experienced craftsmen. However, conventional force sensors need to be put between the skin and the objects, which affects the natural sense of the skin. Here, we proposed a fingertip force sensing method based on changes of blood volume and designed a wearable photoelectric fingertip force sensing system (PFFS) for digitalization of traditional Chinese medicine (TCM) pulse diagnosis. The PFFS does not interfere with the fingertips' tactile sense while detecting fingertip force. This PFFS detects the change of blood volume in fingertip by photoelectric plethysmography and can obtain the change of output current under different fingertip forces. We also studied the effect of various factors on PFFS output signals, including emission lights of different wavelengths, ambient temperature, and the user's heartbeat artifact. We further established the relationship between the change of blood volume and fingertip force by combining experimental and theoretical methods. Moreover, we demonstrated the feasibility of the PFFS to detect fingertip forces under commonly used conditions in TCM pulse diagnosis without sensory interference. This PFFS also shows promise for perceiving the viscosity of objects and recognizing gestures in human-computer interaction. This work paves the way for the digitalization of fingertip forces during TCM pulse diagnosis and other fingertip forces under natural conditions.

Investigation of the Test–Retest Reliability and Inter-Rater Agreement of Traditional Chinese Medicine-Based Pulse Diagnosis Among Indian Traditional Chinese Medicine Practitioners

Objective: The objective of this study was to assess the test–retest reliability and inter-rater agreement between the traditional Chinese medicine (TCM) pulse diagnosis between two Indian TCM practitioners. Methods: A total of 300 participants were evaluated for their bilateral pulse based on TCM independently by two investigators with similar years of experience but who graduated from different medical schools. The investigators who were blinded diagnosed the participants as per TCM based on the depth, rate, and strength of their pulse. Cronbach α and weighted kappa were used for assessing the reliability and repeatability. Results: The data of all the 300 participants were included in the analysis. The test–retest reliability computed using Cronbach’s alpha indicated high reliability between the investigators (α =0.963). There were a total of 37 different types of diagnoses identified by both investigators. The investigators diagnoses matched for 116 participants out of 300 participants; whereas 184 cases were not matched between the investigators. The inter-rater agreement for the TCM diagnosis tested by weighted Kappa did not returned any significant agreement (Kappa = 0.370). Discussion: The present study shows higher reliability between the pulse diagnoses between the investigators. However, the inter-rater agreement for TCM diagnosis based on pulse diagnosis was not significant probably due to the variation in subjective assessments. This calls for the standardization of training in TCM diagnostic methods among practitioners. Future studies should include the differences in training, medical education, and experience among the practitioners while determining the reliability of pulse diagnosis.

A low-cost flexible piezoresistive sensor with high sensitivity and broad detection range based on porous random microstructures for wearable electronics

Due to their simple structures and high sensitivity, flexible piezoresistive sensors have great potential for physiological monitoring, tactile recognition of prosthetics, and electronic skin. Here, a flexible piezoresistive sensor based on porous random microstructures and having high sensitivity and a wide detection range is proposed. Conductive carbon black was used as an active filler and thermoplastic expandable microspheres and sandpaper were used as templates for the fabrication of the pore structures and random microstructures of the substrates, respectively. Synergistic interactions between these two components in the sensor led to a high sensitivity of 11.02 kPa−1 (0–6 kPa), wide measuring range (0–30 kPa), rapid response time (<200 ms), and long-term durability (>2000 cycles). The sensor was effective for monitoring a variety of hand movements, including bending of the fingers and wrist. In addition, the sensors were effective when attached to a robotic hand to simulate the traditional Chinese medicine pulse diagnosis and to grasp objects. This device thus has broad application prospects in the field of human-machine interactions and robot teleoperation.

Cocklebur-inspired “branch-seed-spininess” 3D hierarchical structure bionic electronic skin for intelligent perception

Traditional electronic skin (e-skin) with single micro-nano structures has a limited capacity to optimize the comprehensive sensing properties, which greatly hinders the intellectualization process. Herein, cocklebur-inspired bionic e-skin consisting of the “branch-seed-spininess” three-dimensional hierarchical structure, namely the P(VDF-TrFE) fiber-TiO2 pillar-TiO2 thorn structure (FPTS), is proposed. Benefiting from the hierarchical characteristics of the FPTS, the e-skin exhibits a high sensitivity of 0.163 kPa−1 (<20 kPa), wide pressure sensing range of 0–200 kPa, and ultrafast response/relaxation time of < 5.6 ms. In light of these salient features, versatile intelligent perception applications are explored. First, robot fingers are equipped with the FPTS e-skins and realize the traditional Chinese medicine pulse diagnosis of the radial artery in different positions and pressing degrees. Second, a flexible perception array with 100 FPTS e-skin units enables the spatial pressure mapping of flat/curved surfaces and the perception of different Braille characters. Most importantly, by making full use of the FPTS e-skin with a five-layer deep neural network, an intelligent gesture perception system is developed for accurate recognition (average accuracy: 98.33%) of sign language and precise control of the robot hand, which is of great significance for the development of intelligent sign language translation in artificial intelligence.

A new interpretation of TCM pulse diagnosis based on quantum physical model of the human body

Following the quantum theory-based physical model of the human body, a new interpretation of the traditional Chinese medicine (TCM) principle of “Cunkou reads viscera” is presented. Then, a Gaussian pulse wave model as a solution to the Schrodinger equation is shown to accurately describe 19 different pulse shapes, and to quantitatively capture the degree of Yin-Yang attributes of 13 pulse shapes. Furthermore, the model suggests using pulse depth and strength as leading-order quantity and pulse shape as first-order quantity, to characterize the hierarchical resonance between the human body and the environment. The future pulse informatics will focus on determining an individual’s unique quantum human equilibrium state, and diagnose its health state according to the pulse deviation from its equilibrium state, to truly achieve the high level of TCM: “knowing the normal state and reaching the change”.

Assessment Parameters for Arrayed Pulse Wave Analysis and Application in Hypertensive Disorders.

Study on the objectivity of pulse diagnosis is inseparable from the instruments to obtain the pulse waves. The single-pulse diagnostic instrument is relatively mature in acquiring and analysing pulse waves, but the pulse information captured by single-pulse diagnostic instrument is limited. The sensor arrays can simulate rich sense of the doctor's fingers and catch multipoint and multiparameter array signals. How to analyse the acquired array signals is still a major problem in the objective research of pulse diagnosis. The goal of this study was to establish methods for analysing arrayed pulse waves and preliminarily apply them in hypertensive disorders. While a sensor array can be used for the real-time monitoring of twelve pulse wave channels, for each subject in this study, only the pulse wave signals of the left hand at the “guan” location were obtained. We calculated the average pulse wave (APW) per channel over a thirty-second interval. The most representative pulse wave (MRPW) and the APW were matched by their correlation coefficient (CC). The features of the MRPW and the features that corresponded to the array pulse volume (APV) parameters were identified manually. Finally, a clinical trial was conducted to detect these feature performance indicators in patients with hypertensive disorders. The independent-samples t-tests and the Mann–Whitney U-tests were performed to assess the differences in these pulse parameters between the healthy and hypertensive groups. We found that the radial passage (RP) APVh1, APVh3, APVh4, APVh3/h1 (P < 0.01), and APVh4/h1 (P < 0.05) were significantly higher in the hypertensive group than in the healthy group; the intermediate passage (IP) APVh4, APVh3/h1 (P < 0.05), and APVh4/h1 (P < 0.01) and the mean APVh3, APVh3/h1 (P < 0.05), and APVh4/h1 (P < 0.01) were significantly higher in the hypertensive group than in the healthy group, and the ulnar passage (UP) APVh4/h1 (P < 0.05) was clearly elevated in the hypertensive group. These results provide a preliminary validation of this novel approach for determining the APV by arrayed pulse wave analysis. In conclusion, we identified effective indicators of hypertensive vascular function. Traditional Chinese medicine (TCM) pulses comprise multidimensional information, and a sensor array could provide a better indication of TCM pulse characteristics. In this study, the validation of the arrayed pulse wave analysis demonstrates that the APV can reliably mirror TCM pulse characteristics.

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.

Pulse-line intersection method with unboxed artificial intelligence for hesitant pulse wave classification

State-of-the-art artificial intelligence (AI) methods are progressively strengthened in Traditional Chinese Medicine (TCM) pulse palpation, aiding physicians to make comprehensive preliminary clinical decisions through non-invasive diagnostics. One of the well-known proven examinations i.e., hesitant pulse wave diagnosis, is a sign that the blood circulation of a person is sluggish. This examination provides a preliminary diagnosis for physiological problems. Modern AI methods such as artificial neural networks achieve better performance than traditional methods; however, the final decision of such examination lacks of interpretability. In clinical situations, patients need an easy-to-understand diagnosis to be provided for selecting appropriate clinical treatment. Therefore, this study presents feature extraction and clinical decision support systems based on Pulse-Line Intersection (PLI) and eXplainability AI (XAI) methods. The pulses were recorded from 46 patients in six different measurement points for six seconds. In addition, a comparison of several AI methods was provided to classify hesitant and normal pulse. The contribution of each feature in the classification process was analyzed by unboxing each predictive intelligence model. The results revealed that all models performed comparably, evaluated using performance matric on the testing data with average F1-score of Logistic Regression, Support Vector Machine, Random Forest, XGBoost, Multi-Layer Perceptron, and Long Short-Term Memory were 0.74, 0.74, 0.74, 0.78, 0.73, and 0.80, respectively. This work suggests that modern AI methods can provide more comprehensive explainability and higher accuracy than traditional method rankings.

Application research of pulse signal physiology and pathology feature mining in the field of disease diagnosis

This experiment is based on the principle of traditional Chinese medicine (TCM) pulse diagnosis, the human pulse signal collected by the sensor is organized into a dataset, and the algorithms are designed to apply feature extraction. After denoising, smoothing and eliminating baseline drift of the photoelectric sensors pulse data of several groups of subjects, we designed three algorithms to describe the difference between the two-dimensional images of the pulse data of normal people and patients with chronic diseases. Convert the calculated feature values into multi-dimensional arrays, enter the decision tree (DT) to balance the differences in human physiological conditions, then train in the support vector machine kernel method (SVM-KM) classifier. Experimental results show that the application of these feature mining algorithms to disease detection greatly improves the reliability of TCM diagnosis.

Research on feature mining algorithm and disease diagnosis of pulse signal based on piezoelectric sensor

The human pulse contains various information reflecting the internal environment of the human body. However, the classical method of pulse diagnosis in traditional Chinese medicine (TCM) has the disadvantages of relying too much on the doctor's experience and the diagnosis result is too subjective. Based on the principle of TCM pulse diagnosis, the use of photoelectric sensors to collect the pulse signals of multiple healthy people and patients with chronic diseases, and organize the detailed pulse information into a data set and analyze it with algorithms, is a solution to overcome this problem through modern technology. However, this method is still difficult to understand the patient's physiological condition in detail, and it is also difficult to explain the internal connection between abnormal pulse conditions and their physiological conditions. In the experiment, after denoising, smoothing, and eliminating the baseline drift of the subjects' pulse data, we designed two algorithms to describe the difference between the two-dimensional images of the pulse data of normal people and patients with chronic diseases. The specific feature values obtained are converted into a multi-dimensional array and trained in a support vector machine (SVM) classifier. The classification accuracy is higher than the basic temporal features. Experimental results show that it is feasible to use specific feature mining algorithms for disease detection. Through analysis, this paper found the pathological characteristics reflected in the two-dimensional pulse image, discovered the internal connection between the pulse waveform characteristics of the human body and the disease, and tried to describe it through algorithms, trying to establish a method for detecting specific diseases using photoelectric signals.

Traditional Chinese Medicine Pulse Diagnosis on a Smartphone Using Skin Impedance at Acupoints: A Feasibility Study.

In traditional Chinese medicine (TCM), pulse diagnosis is one of the most important methods for diagnosis. A pulse can be felt by applying firm fingertip pressure to the skin where the arteries travel. The pulse diagnosis has become an important tool not only for TCM practitioners but also for several areas of Western medicine. Many pulse measuring devices have been proposed to obtain objective pulse conditions. In the past, pulse diagnosis instruments were single-point sensing methods, which missed a lot of information. Later, multi-point sensing instruments were developed that resolved this issue but were much higher in cost and lacked mobility. In this article, based on the concept of sensor fusion, we describe a portable low-cost system for TCM pulse-type estimation using a smartphone connected to two sensors, including one photoplethysmography (PPG) sensor and one galvanic skin response (GSR) sensor. As a proof of concept, we collected five-minute PPG pulse information and skin impedance on 24 acupoints from 80 subjects. Based on these collected data, we implemented a fully connected neural network (FCN), which was able to provide high prediction accuracy (>90%) for patients with a TCM wiry pulse.

A high gauge-factor wearable strain sensor array via 3D printed mold fabrication and size optimization of silver-coated carbon nanotubes

The development of 3D print technology provided an opportunity to achieve fast and accurate fabrication of wearable sensor arrays. In this paper, high-sensitivity flexible and stretchable silver-coated carbon nanotube (Ag@CNT) wearable strain sensor arrays are fabricated using 3D printing technology and composite nanomaterial synthesis. Ag@CNTs with uniform and compact particles were synthesized with different sizes of carbon nanotubes (CNTs) using a reduction method. Strain sensor arrays were fabricated accurately and efficiently with the aid of 3D printed molds. Sensors with different Ag@CNTs were then compared comprehensively, and it was found that the Ag@CNT (short) sensor, which had a gauge factor (GF) of 62.8 in the 0% to 14.44% stretch range and a GF of 831.3 in the 14.44% to 21.11% stretch range, can significantly enhance the detection of small movements. These wearable strain sensor arrays were utilized in the application of traditional Chinese medicine pulse diagnosis and gesture recognition.

Relationship between the blood-vessel coupling characteristics and the propagation of pulse waves

Pulse waves cannot be understood simply as pressure waves (longitudinal waves) propagating in compressible blood fluid, nor as radially expanding-contracting displacement waves (transverse waves) propagating along solid blood vessels, but rather as complex waves with fluid-solid coupling and longitudinal wave-transverse wave coupling beyond ordinary imagination. Starting from a new approach to analyze the coupling constitutive relation, a series model is proposed, providing more information for traditional Chinese medicine (TCM) pulse diagnosis in terms of the “position, rate, shape and potential”. It is shown that the equivalent volumetric compression modulus Ks and the corresponding pulse wave propagation velocity cs of the coupling pulse wave system, mainly depend on two dimensionless parameters: the ratio of the blood modulus to the vessel modulus, Kb(p)/E(p) and the ratio of the diameter to the thickness, D(p)/h0, of thin-walled blood vessels, which may vary from person to person and from different pulse locations for the same person. The influences of them on the cs are quantitatively analyzed, showing that for human body the magnitude of Kb/E is in the order of 103 so that the magnitude of cs is in the order of 100–101 m/s to adapt to the human physio-biochemical reactions. By clinical invasive measurements, it is confirmed that the pulse volume transverse wave and the pulse pressure longitudinal wave are coupled and propagate at the same speed, and it is shown that the pulse wave is actually a “biological wave” with oxygenation and biochemical reactions on the wave front. Furthermore, the relations of the “pulse pressure amplification” with the nonlinear constitutive relation and with the load enhanced reflection at the bifurcation of blood vessels, as well as the Lewis’s hypothesis about the formation of dicrotic wave are discussed.

Colectomy influences the radial pulse parameters of traditional Chinese medicine pulse diagnosis in patients with colorectal cancer

IntroductionIn Traditional Chinese Medicine (TCM), manual palpation of the radial pressure pulse (RPP) has remained a traditional method of clinical diagnoses for centuries. Arterial mechanics have been studied by various researchers. Time-domain and frequency-domain analyses of the RPP has typically provided a quantitative and non-biased assessment that gives complex physiological and pathological information. To date, no preceding research has clarified the relationship of the radial pulse and the large intestine in TCM theory. In this pre-post intervention study, we compared the RPP parameters of patients with colorectal cancer before and after colectomy. MethodsThirty patients aged between 18 and 80 years old from the Department of Colorectal Surgery in Taichung Tzu Chi Hospital, Taiwan were enrolled into this study based on predefined inclusion and exclusion criteria before the colectomy. The assessment of RPP was performed before the colectomy and within 24 h after the colectomy. The mean value of the spectral energy (SE0―10 Hz, SE10―50 Hz, and SE13―50 Hz) and the radial augmentation index (AI) were obtained. ResultsWe observed that the SE10―50 Hz and SE13―50 Hz of the right Chy was significantly increased after the colectomy while the SE13―50 Hz of the right Chun was significantly decreased after the colectomy procedures. The radial AI of the right Chy was significantly reduced after the colectomy compared to the remaining pulse positions. ConclusionsTo our best knowledge, this preliminary study is the first attempt to investigate the influence of colectomy on the radial pulse parameters in patients with colorectal cancer. The major findings from this study include a significant reduction of the radial AI at the right Chy pulse, and a significant difference in the SE at the right Chy and Chun pulses after colectomy. It is also the first study using modern scientific tools to validate the TCM theory that the right Chy pulse corresponds to the changes in the large intestine through surgical removal of the visceral organs.

High‐Performance Flexible Tactile Sensor Enabling Intelligent Haptic Perception for a Soft Prosthetic Hand

AbstractCompared to the traditional motor‐driven prosthetic hand, a soft prosthetic hand (SPD) has intrisic advantages such as kinematics dexterity and friendly external interaction. In order for an SPD to realize the function of a real hand, the integration of flexible high‐performance tactile sensors is essential. Current research on flexible tactile sensors is mainly focused on increasing sensitivity, but ignores other issues, such as the complexity of arraying and compatibility with soft actuators. A high‐performance flexible force sensor enabling intelligent tactile perception for an SPD is reported. With the aid of reasonable arrangement of functional material and effective 3D structure design, the force sensor exhibits good linearity (R2 = 0.982), accuracy and repeatability, very low hysteresis, fast dynamical response (&lt;1 ms), high stability (30000 loading–unloading cycles), stable performance under high frequency (&gt;50 Hz), and can easily be arrayed and integrated with an SPD. With such a sensor mounted on its surface, an SPD achieves various complicated tasks in the same manner as a real hand, including feeling the softness of objects and imitating the process of traditional Chinese medicine pulse diagnosis to monitor a pulse wave in the radial artery. Finally, through use of a force‐sensor array, an SPD provides real‐time spatial distribution of pressure during the grabbing of objects.

Accurate targeting in robot-assisted TCM pulse diagnosis using adaptive sensor fusion

Accurate targeting plays an important role in the study of human-robot interaction under dynamic environments. Especially for robot-assisted Traditional Chinese Medicine (TCM) pulse diagnosis, the localization and accuracy of diagnose positions at wrist needs to be addressed. In this work, imaging photoplethysmography (iPPG) which measures the physiological changes of blood flow in artery is used as an extra modal information in addition to computer vision at localization, to alleviate the effect of approaching distance varying during the robot arm movement. Both computer vision and iPPG are fed into an adaptive fusion expert of convolutional neural networks (CNN) architecture, and this boosts the accuracy at targeting of TCM radial artery at wrist. A coherence weight of their contributions was calculated and reflected the adaptation of the CNN to distance varying.

The Association of Radial Artery Pulse Wave Variables with the Pulse Wave Velocity and Echocardiographic Parameters in Hypertension.

This study aims at exploring the cardiovascular pathophysiological mechanism of TCM (traditional Chinese medicine) pulse by detecting the correlation between radial artery pulse wave variables and pulse wave velocity/echocardiographic parameters. Two hundred Chinese subjects were enrolled in this study, which were grouped into health control group, hypertension group, and hypertensive heart disease group. Physical data obtained in this study contained TCM pulse images at “Guan” position of the left hand, pulse wave velocity, and echocardiographic parameters. Linear and stepwise regression analysis was performed to assess the association of radial artery pulse wave variables with pulse wave velocity and echocardiographic parameters in the total population and in each different group. After adjusting for related confounding factors, decrease of t1, t5 and increase of h1, h3/h1 were statistically associated with arterial stiffness in the total population (P<0.05). Moreover, the correlation study in each group showed that the decrease of both t3 and h5 was also related to arterial stiffness (P<0.05). In terms of echocardiographic parameters, the height of dicrotic wave indicated by h5 was the most relevant pulse wave variable. For the health control, h5 was negatively associated with interventricular septal thickness (VST) and left ventricular posterior wall thickness (PWT) (P<0.05), while for the hypertension population and those with target-organ damage to heart, increase of h5 might be associated with decrease of ejection fraction (EF) and increase of all the remaining echocardiographic parameters especially for left ventricular end-systolic diameter (LVDs) and Left ventricular end-diastolic diameter (LVDd) (P<0.05). In conclusion, we found radial artery pulse wave variables were in association with the arterial stiffness and echocardiographic changes in hypertension, which would provide an experimental basis for cardiovascular pathophysiological mechanism of radial artery pulse wave variables.