Search

Background Pulse 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. Objective A 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. Method A 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. Results Computing 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. Conclusion Previously, 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.

In order to solve the problem of non-invasive diagnosis and monitoring of women during pregnancy, a piezoelectric film pulse sensing system combined with the mode energy ratio (MER) analysis is utilized to detect human pulses to reveal pregnant conditions. Inspired by traditional Chinese medicine (TCM), pulse diagnosis has a history of more than 2,500 years. The life energy of the human body helps the diagnosis of the disease through the circulation of blood vessels connected to the organs. A PVDF piezoelectric film sensor is used to emulate the pulse taking process in TCM to record the pulse signals. And the algorithm of MER is proposed based on empirical mode decomposition (EMD). Through the MER analysis of 83 female volunteers with different pregnancy statuses, the identification and warning of pregnancy status and physical health indicators are realized.

This study explores American patients' understanding of traditional Chinese medicine (TCM) in the late 19th century by referring to letters from American patients recorded in The Science of Oriental Medicine, written by Tan Fuyuan (谭富园), a Chinese medicine doctor working in the United States in the late Qing dynasty. Identifying a focus on significant effects, pulse diagnosis, herbal teas, dietary control, and long-term treatment, the results also discussed the differences between TCM and Western medicine in simple terms to show that the “ideological” spread of TCM was based on its curative effect. However, the “theoretical” spread of TCM requires more of intercultural exchanges.

To analyze the correlation of pulse characteristics at different acquisition positions, the pulse parameters that can identify different pulse and physiological information are given. In this paper, the relevance and difference of wrist pulse characteristics at Cun, Guan and Chi position were studied by pulse diagnosis instrument and time-frequency, frequency-domain and time-frequency domain analysis methods etc. Firstly, the pulse information of Cun, Guan and Chi position is collected from 206 healthy adults. The 66 pulse characteristics of each sampled location are extracted. In addition, the 66 feature indexes of each position are used to calculate the Spearman correlation coefficients among Cun, Guan, Chi position as well as the both hands in detail. Finally, the relevance and difference of wrist pulse at Cun, Guan and Chi position were studied and analyzed by significance test. The pulse characteristics with high correlation at Cun-Guan-Chi position of left and right hands are mainly power spectrum characteristics and time-frequency characteristics, while the features with high correlation between both hands mostly concentrate in the time domain characteristics. This work has studied the relationship between the position of pulse acquisition from quantitative and qualitative aspects, and laid a foundation for the objective diagnosis of pulse condition and the comprehensive disclosure of pulse information.

Introduction. The study aimed to theoretically substantiate and experimentally test the effectiveness of registering peripheral pulse of freestyle wrestlers, student members of the Russian national team, using the computer-aided pulse diagnosis system. Materials and methods: The paper presents for the first time the results of experimental studies into the heart rate variability with respect to pulse wave during exercise tests using spectral methods, as well as the effect of physical activity on twelve internal organs of an athlete. Research results: The findings indicate the most characteristic deviations in the athlete organism, including a decrease in the function of the digestive organs, and cardiovascular and respiratory systems, due to physical activity. The study provides recommendations to athletes for consultation with specialists. Conclusion: This method can be considered promising for the creation of tools for self-control of the psychophysical state of athletes. Keywords: physical activity, spectral analysis, pulse wave, functional test

Some health complexity happens during driving like heart problem, stroke etc. Driver’s health abnormality may also effect safety of other vehicles. So in recent years, automotive manufacturers and users are interested to implement real-time health monitoring in car system. In cardiac monitoring systems, a sensor is an important element. It is not only the type of sensor that determines the correct operation of a system, but also the processing of the registered signals. This paper analyses the influence of the sensor pressing force on the estimation of the stiffness of the artery to describe the pulse signal processing.

The pulse sensed over radial artery on the wrist is regarded as an important physiological index in traditional Chinese medicine and Ayurvedic medicine. The computerized pulse diagnosis has been obtaining more and more attentions for its noninvasive character and its convenience in judging the health status of people. To acquire more accurate signals of pulse wave and contact pressure, several acquisition devices have been proposed. But most of them use a single type of sensor that cannot obtain both accurate dynamic pulse wave and static contact pressure. In this paper, we design a pressure sensor array comprising two different transducers to collect wrist pulse signal and contact pressure, synchronously. What is more, we propose a signal quality assessment frame by analyzing the types of abnormal signals and their causes. It can be used to improve the level of automatic collection and the performance of data analysis. The experimental results show that the proposed system can record more abundant information of the pulse signal, and the frame can identify different qualities of the signal as well. For computerized pulse diagnosis, the multichannel signals can achieve higher performance than single channel in distinguishing diseases from health after removing the abnormal signals.

Background and objective As a common pathological pulse, unsmooth pulse has important diagnostic value in traditional Chinese medicine (TCM). In modern pulse diagnosis, unsmooth pulse plays an important role in the diagnosis of disease location and nature, but there are few studies on it. In this paper, a pulse diagnosis approach based on acoustic waveforms was proposed, the wrist pulse was divided into five layers vertically for the first time. Five layers acoustic waves of the radial artery in stable coronary heart disease (CHD) patients and relatively healthy people were compared to explore whether there are abnormal changes in acoustic pulse in stable CHD patients. Methods The acoustic features of unsmooth pulse in patients with stable CHD were analyzed in time domain, frequency domain and empirical mode decomposition, combined with shannon entropy and multi-scale entropy. Sixteen pulse characteristics were discovered, and one-way analysis of variance were performed. The characteristics of the two groups were tested by T test. 13 features were used to identify patients with stable CHD by support vector machine (SVM). Results Compared to healthy people, all parameters of the third layer of the stable CHD left Cun pulse were significantly different from those of the healthy people. The identification rates of the fourth and third layer of the left Cun pulse were the first (90.79%) and the second (88.16%), respectively. Conclusion Abnormal acoustic pulse appeared in the radial artery in patients with stable CHD. According to these changes, patients with stable CHD can be effectively identified from the perspective of pulse.

Pulse diagnosis is an important part of traditional Chinese medicine’s four examinations: looking, smelling, asking, and cutting. The pulse signal can reflect the health of the human body’s internal haslets. With the development of information technology, the emergence of various pulse collection equipment and analysis algorithms provides a broad platform for the development of digital Chinese medicine. This article summarizes the currently commonly used pulse collection and analysis techniques, and analyzes the advantages and disadvantages of various collection and analysis methods, and finally proposes the improvement direction for the current research status, which provides a reference for the objectification of TCM diagnosis.

BackgroundFatigue is a common and subjective symptom, which is associated with many diseases and suboptimal health status. A reliable and evidence-based approach is lacking to distinguish disease fatigue and non-disease fatigue. This study aimed to establish a method for early differential diagnosis of fatigue, which can be used to distinguish disease fatigue from non-disease fatigue, and to investigate the feasibility of characterizing fatigue states in a view of tongue and pulse data analysis.MethodsTongue and Face Diagnosis Analysis-1 (TFDA-1) instrument and Pulse Diagnosis Analysis-1 (PDA-1) instrument were used to collect tongue and pulse data. Four machine learning models were used to perform classification experiments of disease fatigue vs. non-disease fatigue.ResultsThe results showed that all the four classifiers over “Tongue & Pulse” joint data showed better performances than those only over tongue data or only over pulse data. The model accuracy rates based on logistic regression, support vector machine, random forest, and neural network were (85.51 ± 1.87)%, (83.78 ± 4.39)%, (83.27 ± 3.48)% and (85.82 ± 3.01)%, and with Area Under Curve estimates of 0.9160 ± 0.0136, 0.9106 ± 0.0365, 0.8959 ± 0.0254 and 0.9239 ± 0.0174, respectively.ConclusionThis study proposed and validated an innovative, non-invasive differential diagnosis approach. Results suggest that it is feasible to characterize disease fatigue and non-disease fatigue by using objective tongue data and pulse data.

Objective: This study proposes a novel wearable pulse monitoring system, which can realize the synchronous measurements of pulse wave, skin, temperature, and pulse wave velocity (PWV). Methods: A flexible sensor based on thermosensation is used to detect pressure and temperature stimuli simultaneously. A total of two sensors are integrated to detect pulse transit along two specific points of the artery, e.g., Cun and Chi at a wrist, the data of which are subsequently used to figure out the PWV by using a tailor-designed algorithm conducted in a microprocessor. Calibration experiments and application cases are conducted to validate the effectiveness of the monitor. Results: The developed monitor detects the physiological signals of pulse wave, PWV, and skin temperature simultaneously. In addition, the monitor can measure the pulse changes before and after exercises and track skin temperature variations when warming and cooling. Moreover, the monitor can be also used to detect the local PWV at the wrist. Conclusion: The synchronous measurements of pulse wave, skin temperature, and PWV using a wearable monitor are feasible. Significance: The monitor is small, simple-structured, with multifunction, and thus provides a promising auxiliary approach for traditional Chinese medicine pulse diagnosis.

Introduction The utilization of information and communications technology (ICT) devices is a new way to record and analyze health data of the elderly. This time-series study aimed to analyze health changes, and the correlation between pulse manifestation and health indicators, in the elderly. Methods We conducted continuous 93-day monitoring of health data and pulse records in 8 elderly participants. A time series method was used to analyze health changes in participants. The correlation between pulse manifestation and health indicators was analyzed using a structural equation model. Results During the use of ICT devices, the number of steps taken by the elderly showed a significant increase (p Conclusion Through the use of ICT devices, we can understand the health status of the elderly and make behavior predictions. Pulse manifestation data can indicate the health status of the elderly. Thus, ICT devices can be used as health management tools and assist doctors in making simple diagnoses.

Introduction Gender differences are important intrinsic factors in wrist arterial pulse assessment in traditional Chinese and Korean medicine. The current study aimed to examine gender differences in wrist pulse waves at the Cun, Guan, and Chi positions of the wrist and to identify associations between physical indices and anatomical properties at all three positions. Methods One hundred thirty-nine middle-aged Korean men and women participated in this cross-sectional study. A blood analysis was performed, and blood pressure and medical history were recorded. Artery diameter and depth, blood flow velocity, and pulse wave were measured. Results The pulse power index (PPI), pulse depth index (PDI), and power spectrum density at the third harmonic frequency/first harmonic frequency (PSD_w3_w1) showed highly significant differences according to gender. Men exhibited larger PPI values than women at all three positions. The PDI and PSD_w3_w1 in men were higher at the Cun position than those in women, whereas the PDI at the Chi position was higher in women than in men. Gender differences in the spectral harmonic energy ratio were greater at the Guan and Chi positions than those at the Cun position. The subendocardial viability ratio differed significantly between genders. In women, the PDI was highly positively correlated with radial artery depth at all positions; however, in men, the PDI was only marginally correlated with artery depth at the Guan and Chi positions. Conclusion We suggest that gender differences should be considered in wrist artery pulse diagnosis, including those related to pulse diagnostic positions and anatomical properties.

This study proposes the prototype of a wrist pulse acquisition device with three-dimensional pulse image (3DPI) obtained at the radial artery. The proposed prototype is designed with mechanical and electronic circuit hardware as well as the control and data acquisition software to have fourteen degrees of movement freedom against the sophisticated geometry of the wrist radial artery for accurate pulse-taking. Three robot fingers with nine degrees of movement freedom and a wrist fixer with two degrees of rotation freedom and one degree of manual movement freedom are designed to mimic the fine pulse-taking skill of a Chinese medicine physician. The customer-designed array sensors with $5\times5$ -1 sensing elements in an array are attached on the robot fingertips to get 3DPI with spatial information for direct translation of pulse feeling patterns in Chinese medicine texts, in addition to being compatible with 1D pulse wave analysis. To check reliability of the prototype, adopted is SphygmoCor with pulse data correlation. Consequently, the prototype passes safety tests and has high correlation of pulse data (waveform and peripheral augmented index) with those obtained by SphygmoCor. The automatic multi-depth pulse-taking with robot fingers attached array sensors on their tips can get acceptably repeatable pulse data with coefficient of variation less than 10%. By simultaneous pulse-taking at Cun-Guan-Chi positions, 3DPIs demonstrate great different stereoscopic waveshapes against almost unchanged single temporal pulse waveshape. Hence, the proposed prototype may be a useful tool to perform array pulse-taking for Chinese medicine pulse diagnosis and pulse wave analysis for cardiovascular examination.

Pulse localization is the basic task of the pulse diagnosis with robot. More accurate location can reduce the misdiagnosis caused by different types of pulse. Traditional works usually use a collection surface with a certain area for contact detection, and move the collection surface to collect changes of power for pulse localization. These methods often require the subjects place their wrist in a given position. In this paper, we propose a novel pulse localization method which uses the infrared camera as the input sensor, and locates the pulse on wrist with the neural network. This method can not only reduce the contact between the machine and the subject, reduce the discomfort of the process, but also reduce the preparation time for the test, which can improve the detection efficiency. The experiments show that our proposed method can locate the pulse with high accuracy. And we have applied this method to pulse diagnosis robot for pulse data collection.

The authors have requested that this preprint be removed from Research Square.

The objectification of Traditional Chinese Medicine(TCM) pulse diagnosis has become one of the important development directions of pulse diagnosis analysis. In this paper, Z-M300 intelligent pulse diagnosis instrument was used to collect pulse images of college students with different body weights, and then expert screening, data preprocessing and data marking were carried out to obtain standard data sets. Then, the improved VGG-16 convolutional neural network(VGG-16Net) is used to realize the feature self-extraction of pulse image and establish the pulse image classification model. The experimental results show that the classification accuracy of the method for the pulse image of obese people and normal people reaches 90.28\(\%\), which greatly improves the accuracy compared with the traditional method of pulse image classification. This study proves the validity of the one-dimensional convolutional neural network for pulse image classification, which is of great significance for the objectification of pulse diagnosis.

Traditional Chinese medicine (TCM) is an empirical and subjective medical discipline. The rapid development of information technology requires the objectification of traditional Chinese medicine diagnosis and treatment. A wearable pulse diagnosis device, which aims at making pulse diagnosis objective, is designed. The device utilizes 7 kind of signals to characterize human health level. This paper especially focuses on the application of sEMG signal in the analysis of muscle tension and classifies muscle tension level using the algorithm of CART. In actual wearing cases, different wearing positions will induce to big difference of sEMG signal. In order to solve the problem above, random forest is applied in a position matching method based on prior calibration. The experimental results prove the effectiveness of the muscle tension level classification algorithm with an accuracy up to 81.4%.

Accurate pulse diagnosis is often based on extensive clinical experience. Recently, modern computer-aided pulse diagnostic methods have been developed to help doctors to quickly determine patients' physiological conditions. Most pulse diagnostic methods used low-dimensional feature vectors to classify pulse types. Therefore, some important but subtle pulse information might be ignored. In this study, a novel high-dimensional pulse classification method was developed to improve pulse diagnosis accuracy. To understand the underlying physical meaning or implications hidden in pulse discrimination, 71 pulse features were extracted from the time, spatial, and frequency domains to cover as much pulse information as possible. Then, Principal Component Analysis (PCA) was applied to extract the most representative components. Artificial neural networks were trained to classify 10 different pulse types. The results showed that PCA accounted for 95% of the total variances achieved the highest accuracy of 98.2% in pulse classification. The results also showed that pulse energy, local instantaneous characteristics, main frequency, and waveform complexity were the major factors determining pulse discriminability. This study demonstrated that using high-dimensional features could retain more pulse information and thus, effectively improve pulse diagnostic accuracy.

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.

In traditional Chinese medicine (TCM), wrist pulse is of great significance to help doctors in diagnosis. With the development of sensing technology, the computerized wrist pulse analysis has been attracting more attention in modern medicine for its non-invasive and convenient. Considering the TCM pulse diagnosis theory, it is necessary to develop effective feature extraction methods for computerized diagnosis. In this paper, we decompose the pressure pulse waveform of the radial artery to several components by sparse decomposition with Gabor function. In order to better represent the pulse waveform signal, we use an Gabor function based on the characteristics of the pulse waveform to generate a time-frequency dictionary. Compared with the conventional representation methods, the shape of the Gabor function is more variable, which can better represent both the contour and specific peaks. In addition, due to the limitation of the windowing, the Gabor function can reduce the influence on other positions when representing specific position. The feature vector composed of the decomposed components can be used for the computerized pulse signal analysis and disease diagnosis. The experimental results show that the proposed method can exhibit superior performance in distinguishing between the signals collected from patients and healthy individuals.

Ayurveda engineering, one of the areas which is getting attention nowadays. In Ayurveda, pulse diagnosis helps to get into the root cause of illness or disease of human body by observing fingertip palpations over the radial artery of a wrist. The Wrist Pulse Signals (WPS) dictate the physiological status of entire human body and a close association is considered between heart and mind. As the association gets weak, it introduces imbalance (vikruti) within body and results in various types of diseases. Emotion plays an important role to study the state of mind of a person. The origin of many diseases is linked with human emotions. Machines with the capability of emotion recognition can look inside the physiological changes in human body. Emotion detection using physiological and peripheral signals like electroencephalogram, electrocardiogram, skin conductance, photoplethysmography, galvanic skin response has been in continuous use. The wrist pulse signals, non-invasive approach of health diagnosis, relies on the understanding and analysis of the characteristics of pulse pressure signals. This paper mainly focuses on the framework to map human emotion with diseases based on the (Vata, Pitta and Kapha) natural imbalance using WPS analysis with modernization.

Rotary blood pump (RP) is one of the most important devices in the treatment of profound heart failure and is known to reduce the pulse in the blood pressure waveform, especially when it is used for axial flow. In an outpatient clinic, checking the pulse of a patient implanted with an RP can help diagnose the patient’s condition. For that purpose, animal experiments with healthy adult goats implanted with the EVAHEART system were carried out after obtaining ethical committee approval. Visual imaging of the goats’ faces was recorded using a video camera. The pulse waves were clearly recorded using the newly developed pulse diagnosis system with video imaging and compared with laser Doppler flowmeter and time series data. Spectral analysis of the time series data showed the usefulness of video imaging from outside the body. Clinical applications are planned, and this newly developed method is expected to be a useful diagnostic method for evaluating the cardiac function in patients implanted with RPs in the future.

Pulse diagnosis is an important part of Chinese medicine and has played an important role in the development of Chinese medical science. However, the pulse is traditionally determined by cutting it off, which leads to a lack of objective standard pulse identification methods and affects their accuracy and feasibility. This research has studied and discussed the processing and identification of four kinds of pulse: normal pulse, wiry pulse, smooth pulse, and thready pulse. Four frequency-domain characteristics of the pulse wave and six kinds of wavelet scale energy characteristic information were extracted, and a three-layer BP (backprocessing) neural network was established. The LM (Levenberg-Marquard) algorithm and a genetic algorithm were used to improve the BP neural network, to train on and predict experimental samples, and to obtain classification accuracies of 90% and 95% respectively. Moreover, improved BP neural network based on a genetic algorithm has shown highly superior performance in terms of convergence speed and low error rate.

Pulse diagnosis is one of the diagnostic methods in traditional Chinese medicine (TCM). Such diagnoses are made subjectively by a TCM doctor, who requires expert knowledge. If pulse diagnosis could be automated, it would be beneficial for health management. In our previous study, we showed that pulse diagnosis might be related to personal health data, such as step count and sleep score. In this study, we propose a new approach to classifying pulse diagnoses based on a combination of features from pulse and health data. Pulse characteristics are extracted from electronically recorded pulse shapes, and health data feature analysis is augmented by considering the periodicity of daily health metrics. Using these features, we perform both single- and multi-label classifications, and investigate the possibility to improve classification accuracy. We further adopt two classification methods for multi-label classification: random forests and deep learning. Our results show that our approach improves classification accuracy for pulse diagnoses.

OBJECTIVE The study aim was to use existing relevant indexes to establish an index series for the quantitative measurement of Qi deficiency pattern (QDP) in Traditional Chinese Medicine (TCM). METHODS Seventeen indexes of arterial oxygen saturation (SaO2), TCM pulse diagnosis, heart rate, and blood pressure were integrated into an index series, and 94 selected subjects were divided into a healthy control group, a mild QDP group, and a typical QDP group. All three groups were then synchronously given a breath-holding (BH) test and index detections using TCM apparatus for auxiliary diagnosis and treatment and a photoplethysmograph. The detections were carried out repeatedly until there was no monotonic change in the value of the minimal SaO2 during a BH measurement (SaO2MIN) or in the duration of BH during a BH measurement (TBH). RESULTS BH measurement and index detection data from 380 eligible cases were analyzed and a significant intergroup difference was found for 12 indexes. CONCLUSION An index series for quantitative QDP measurement could comprise the 12 effective indexes: RSaO2d (rate of decline of SaO2 during a BH measurement), RSaO2r (recovery rate of SaO2 after a BH measurement), SaO2min, BFV (blood flow velocity), TSaO2d (the time at which SaO2 started declining during a BH measurement), TBH, TSaO2min (the time of occurrence of the minimal SaO2 during a BH measurement), TSaO2max (the time of occurrence of the maximal SaO2 after a BH measurement), PTP (pulse-touching pressure), SBP (systolic blood pressure), PWV (pulse wave velocity), and DBP (diastolic blood pressure). Of the indexes, RSaO2d could play a key role in quantitative QDP measurement; RSaO2r and SaO2min could be used to differentiate the QDP symptom of shortness of breath and as early-warning indexes for chronic obstructive pulmonary disease. BFV, TSaO2d, and TBH could be used to quantitatively assess the QDP symptoms of spontaneous perspiration and shortness of breath.

A dynamic L-cube polynomial is proposed to analyze dynamic three-dimensional pulse images (d3DPIs), as an extension of the previous static L-cube polynomial. In this paper, a weighted least squares (WLS) method is proposed to fit the amplitude C(t) of d3DPI at four physiological key points in addition to the best fit of L-cube polynomials to the measured normal and cold-pressor-test (CPT)-induced taut 3DPIs. Compared with other two fitting functions, C(t) of a dynamic L-cube polynomial can be well matched by the proposed WLS method with the least relative error at four physiological key points in one beat with statistical significance, in addition to the best fit of the measured 3DPIs. Therefore, a dynamic L-cube polynomial can reflect dynamic time characteristics of normal and CPT-induced hypertensive taut 3DPIs, which can be used as an evidence of hypertension diagnosis.

This work demonstrates a flexible piezoelectric pulse sensing system combined with the mode energy ratio (MER) analysis in order to detect human pulses and reveal pregnant conditions. According to the pulse diagnosis principle of traditional Chinese medicine (TCM), the flexible piezoelectric sensor is used to realize digital and intelligent pulse acquisition. And the algorithm of MER based on empirical mode decomposition (EMD) is proposed to describe energy distribution of pulses in different pregnancy status. The experiment results of 83 female volunteers' MER analysis indicate that the identification and warning of pregnancy status and physical health indicators are realized.

Objective To explore the effects of joy and sorrow on pulse-graph parameters in healthy subjects and analyze their potential effect to provide preliminary evidence that pulse diagnosis can identify human emotional changes. Design and intervention Forty healthy female college students from Beijing University of Chinese Medicine were recruited. The emotion-evoking experiment was conducted to observe the effects of joy and sorrow on pulse-graph parameters, taking neutral emotion as a control. The experiment included the following criteria. (1) Emotions evoked and evaluated: the same subjects watched three emotional videos which were, respectively, neutral, joyful and sorrowful to evoke the corresponding emotions. They completed the “subjective emotion evaluation form” (SEEF) before watching each video (T0), immediately after watching the video (T1) and 15 min after watching the video (T2) to conduct subjective emotion evaluation. Simultaneously, their heart rate, galvanic skin response and heart rate variability were collected with a 16-lead physiological recorder to conduct an objective evaluation of emotional arousal. (2) Collection of pulse-graph parameters: pulse-graph parameters of the subjects at T0, T1 and T2 were collected with a pulse condition analyzer. Results When watching the joy video, the rapid ejection phase extended significantly, both the left ventricular diastole and the cardiac cycle shortened significantly. When watching the sorrow video, the left ventricular diastole extended significantly, the cardiac cycle showed an upward trend, and heart rate showed a downward trend. Conclusion Joy and sorrow have a certain effect on pulse-graph parameters of the subjects. The mechanisms may be that joy contributes to rapid ejection phase extension and a shortening of the left ventricular diastole and the cardiac cycle, while sorrow extends the left ventricular diastole. According to the theory of Traditional Chinese Medicine (TCM), the visceral functions can be mediated through human qi flow. Moderate joy and sorrow can promote, respectively, the smooth qi flow of the heart and the lung. However, excessive or long-term joy and sorrow can lead to disharmonious qi flow of the two viscera. Therefore, maintaining moderate emotions is beneficial to the physiological functions of the viscera. Great emotional fluctuations may damage the visceral functions. Future studies with effects of other emotions on pulse-graph parameters are warranted to determine the reliability of the association of emotions and pulse diagnosis.

Pulse diagnosis, with a history of more than 2,000 years, is a non-invasive method to measure the holistic health of the human body. To extract spatial features from wrist pulse signal, three-dimensional pulse images (3DPI) was proposed by Luo since 2012. A new problem, 3D image denoising, has arisen in research on pulse signal. Robust principal component analysis (RPCA) was used for 3DPIs denoising, but the result was unsatisfactory for lacking prior knowledge of the signal and noise. In order to achieve better denoising performance and three-dimensional visual performance of 3DPIs, we proposed a novel method, Channel-Weighted Robust Principal Component Analysis (CWRPCA), replacing RPCA from the previous 3DPI preprocessing process. In this research, we make a comparison by three indicators: (1) percentage of valid periods, (2) error be-tween channels, and (3) peak signal-to-noise ratio (PSNR). The experimental results prove that CWRPCA has the most optimal result, evaluated by the three indicators. In conclusion, CWRPCA has better performance in image denoising for 3DPI and helps subsequent analysis in pulse diagnosis research.

Pulse Diagnosis (PD) is a well-established diagnostic technique used in Traditional Chinese Medicine (TCM). The positions for PD are the radial arteries in the left and right wrists, and the pulse assessment is done by placing index, middle, and ring fingers on each wrist. The pulse of patients is then measured in the superficial, middle, and deep levels by TCM doctors to obtain more information regarding energy or Qi imbalance of the patients. For modern medical researches, PD is also used for indicating some status of patients. However, it is a challenging issue for all medical staff to properly assess the pulse of various patients. Therefore, this work proposes a systematic scheme to measure patient pulse by Photoplethysmography (PPG) sensors. An evaluation framework of some treatments, e.g., acupuncture therapy, is then built on top of the PPG sensing system with machine learning solutions. In our implementation, sensor data will be collected and transferred from Arduino platforms to our server for the PD assessment. The effect of the evaluated treatments will be estimated by the analysis of the PD results with machine learning supports. The major performance index is on the accuracy of the assessment for the treatments.

Pulse diagnosis is an efficient method in traditional Chinese medicine for detecting the health status of a person in a non-invasive and convenient way. Jin's pulse diagnosis (JPD) is a very efficient recent development that is gradually recognized and well validated by the medical community in recent years. However, no acceptable results have been achieved for lung cancer recognition in the field of biomedical signal processing using JPD. More so, there is no standard JPD pulse feature defined with respect to pulse signals. Our work is designed mainly for care giving service conveniently at home to the people having lung cancer by proposing a novel wrist pulse signal processing method, having an insight from JPD. We developed an iterative slide window (ISW) algorithm to segment the de-noised signal into single periods. We analyzed the characteristics of the segmented pulse waveform and for the first time summarized 26 features to classify the pulse waveforms of healthy individuals and lung cancer patients using a cubic support vector machine (CSVM). The result achieved by the proposed method is found to be 78.13% accurate.

Background The theory of pulse diagnosis is to assess the physiological condition of the human body using radial pulse. However, pulses can vary markedly from person to person. Further, pulse diagnosis is difficult to learn and requires one-on-one teaching. Methods To address this problem, we built a home-made pulse diagnoser and measured human pulses for studying the standardization of pulse diagnosis. Our pulse diagnoser was composed of a piezoelectric transducer, differential amplifier, data acquisition instrument, and a Matlab analysis program. The measured pulses were converted into electronic signals by a piezoelectric transducer and saved on a computer. The digitalized data were then refined and analyzed by fast Fourier transform for frequency analysis. Simulations were performed to assess the factors that affected the pulse, including phase shifts of reflected pulse waves (generate sub-peaks in pulses), inconsistent heart rates (deform pulse waves), the vessel stiffness (alter harmonic frequencies of the pulses), and the wave amplitudes (determined by the pulse strength). Results By comparing a published report and our simulation findings, we characterized the pulse types and the effects of various factors, and then applied the findings to study actual pulses in patients. Three types of pulses were determined from the frequency spectrum—choppy pulse (Se mai) without apparent harmonic peaks, the harmonic frequencies of wiry pulse (Xian mai) that are non-integer multiples of the fundamental frequency, and surging pulse (Hong mai) that exhibit strong amplitudes in the spectrum of frequency. A normal pulse and a slippery pulse were differentiated by a phase shift, but not by assessing the frequency spectrum. Conclusion These findings confirm that frequency domain analysis can avoid ambiguity arising in assessing the three types of pulses in the time domain. Further studies of other pulses in the frequency domain are required to develop a precise electronic pulse diagnoser.

Pulse diagnosis is an essential part in four basic diagnostic methods (inspection, listening, inquiring and palpation) in traditional Chinese medicine, which depends on longtime training and rich experience, so computerized pulse acquisition has been proposed and studied to ensure the objectivity. To imitate the process that doctors using three fingertips with different pressures to feel fluctuations in certain areas containing three acupoints, we established a five dimensional pulse signal acquisition system adopting a non-contacting optical metrology method, 3D digital image correlation, to record the full-field displacements of skin fluctuations under different pressures. The system realizes real-time full-field vibration mode observation with 10 FPS. The maximum sample frequency is 472 Hz for detailed post-processing. After acquisition, the signals are analyzed according to the amplitude, pressure, and pulse wave velocity. The proposed system provides a novel optical approach for digitalizing pulse diagnosis and massive pulse signal data acquisition for various types of patients.

The accurate, non-invasive, measuring of the continuous arterial blood pressure waveform faces some difficulties and an innovative blood pressure measurement technology is urgently needed. However, the arterial blood pressure waveform plays an essential role in health care by providing diagnostic information and base for calculating several heart function parameters. The aim of this study is to introduce a novel non-invasive measuring system that can measure the arterial blood pressure waveform with high accuracy in comparison to an applanation tonometry system. The applied measuring device utilizes a new measurement strategy enabled by the OptoForce 3D force sensor, which is attached to the wrist at the radial artery. To validate the accuracy, 30 simultaneous measurements were taken with a Millar tonometer. For the simultaneously recorded non-invasive signals, the similarity was high (the average correlation was $$0.9213\pm 0.063$$ ). The differences in the systolic and the diastolic blood pressure measured by the two systems are small. The average differences ( $$\pm \hbox {SD}$$ ) for simultaneously recorded systolic, diastolic, mean arterial and incisura pressures were: $$0.35\pm 1.75$$ , $$0.02 \pm 0.19$$ , $$2.88\pm 2.42$$ and $$3.84\pm 3.90 \,\text {mmHg}$$ , respectively. These results satisfy the AAMI criteria. Based on our results, this new system requires further development and validation against invasive arterial blood pressure monitoring in order to prove its usefulness in patient monitoring, emergency care, and pulse diagnosis.

In this study, we developed a radial artery pulse acquisition system based on finger-worn dense pressure sensor arrays to enable three-dimensional pulse signals acquisition. The finger-worn dense pressure-sensor arrays were fabricated by packaging 18 ultra-small MEMS pressure sensors (0.4 mm × 0.4 mm × 0.2 mm each) with a pitch of 0.65 mm on flexible printed circuit boards. Pulse signals are measured and recorded simultaneously when traditional Chinese medicine practitioners wear the arrays on the fingers while palpating the radial pulse. Given that the pitches are much smaller than the diameter of the human radial artery, three-dimensional pulse envelope images can be measured with the system, as can the width and the dynamic width of the pulse signals. Furthermore, the array has an effective span of 11.6 mm—3–5 times the diameter of the radial artery—which enables easy and accurate positioning of the sensor array on the radial artery. This study also outlines proposed methods for measuring the pulse width and dynamic pulse width. The dynamic pulse widths of three volunteers were measured, and the dynamic pulse width measurements were consistent with those obtained by color Doppler ultrasound. The pulse wave velocity can also be measured with the system by measuring the pulse transit time between the pulse signals at the brachial and radial arteries using the finger-worn sensor arrays.

Nowadays, traditional Chinese medicine (TCM) is attracting more attention. As the most complete methods of pulse diagnosis, the method named ‘three regions nine subdivisions’ has come to the fore. It is important to collect accurate pulse signals in this way. Hence a pulse signal acquisition system is developed, which includes a pulse sensor based on PVDF (Polyvinylidene fluoride) piezoelectric film, charge amplifier, low-pass filter, voltage amplifier, voltage follower, and a software system based on LabVIEW for controlling the acquisition process and storing data. And PVDF piezoelectric film has suitable performance for this system which as a new kind of polymer piezoelectric material. Finally, the experiment results demonstrate that the system can extract effective pulse signals without heavy pressure, laying a solid foundation for the subsequent analysis on pulse signals.

Profuse medical information about cardiovascular properties can be gathered from pulse waveforms. Therefore, it is desirable to design a smart pulse monitoring device to achieve noninvasive and real-time acquisition of cardiovascular parameters. The majority of current pulse sensors are usually bulky or insufficient in sensitivity. In this work, a graphene-based skin-like sensor is explored for pulse wave sensing with features of easy use and wearing comfort. Moreover, the adjustment of the substrate stiffness and interfacial bonding accomplish the optimal balance between sensor linearity and signal sensitivity, as well as measurement of the beat-to-beat radial arterial pulse. Compared with the existing bulky and nonportable clinical instruments, this highly sensitive and soft sensing patch not only provides primary sensor interface to human skin, but also can objectively and accurately detect the subtle pulse signal variations in a real-time fashion, such as pulse waveforms with different ages, pre- and post-exercise, thus presenting a promising solution to home-based pulse monitoring.

Introduction Clinical holistic diagnosis (CHD) in Chinese medicine (CM) has tried to evaluate most of the whole body functions non-invasively but subjectively via pulse diagnosis (PD). There was no useful tool to validate this model quantitatively until visible pulse feeling by three-dimensional pulse mapping (3DPM) was proposed. Methods An ingestion test was designed to explore the relation between digestive activity and the corresponding spatial locations of pulses in CHD. 3DPMs were recorded 5 min before ingestion and 30, 60, and 90 min after a light meal. The signals were acquired at three palpation positions with five incremental pulse-taking pressures for both hands of 19 subjects. An index, α, calculated from 3DPMs was used to check the variation of pulse patterns in response to the ingestion stimulation. The paired t -test was used to evaluate the significance between the α before and after ingestion for the experimental group in comparison to the control group. Results Pulses with significant differences after ingestion occurred at the middle depth of the right Guan (at the radial artery) at 30 and 60 min, at the middle depth of the left Guan at 60 and 90 min, and at the middle to deep depths of the right Guan at 90 min. Conclusion These three position-depths which demonstrated significant differences mapped to the stomach/intestines, liver/gallbladder, and pancreas, respectively in CHD. This is consistent with the process of digestive activity in physiology. This preliminary study partially testified the relationship between organs and palpation positions in CHD by 3DPM.

Introduction Pulse diagnosis (PD) in Chinese medicine (CM) is a well-established clinical tool used to aid holistic diagnosis. However, most of the recent efforts focusing on single-point pulse waves (SPPWs) have been unable to successfully measure and translate how practitioners feel and interpret the pulse. Methods A prerequisite to validating the holistic model used in CM requires pulse-taking standards and a method to quantify the pulse feeling during PD. Regarding the former, the flatness of the wrist radial artery and the pulse-taking depth as defined by width of the radial artery were proposed. Regarding the latter, three-dimensional pulse mapping (3DPM) was obtained using an array sensor to visibly and quantitatively measure the palpation of the pulse. Cold stimulation was used to incite local vascular stiffness and the increase of the systematic blood pressure, to create the string-like pulse feeling, in contrast to the normal gentle pulse feeling. Results Three types of 3DPM were found, namely gentle, string-like, and slippery pulse mappings. All SPPWs had similar one-dimensional shapes whereas the corresponding 3DPMs had quite different three-dimensional shapes. For gentle pulse mapping (indicating health), an initial peak appeared and reached the top at the upstroke peak of SPPWs. As the local artery became stiffer by applying cold stimulation, the peak was replaced by a string-like shape. Additionally, a slippery 3DPM was recorded, with two peaks appearing sequentially like a bead flowing through the artery under a fingertip. Conclusion 3DPM provides quantitatively and non-invasively accesses information to inform holistic health assessment compared to SPPWs. CM can be improved and further developed quantifying pulse palpation and verify its use in assessing a CM clinical holistic diagnosis.

To develop an effective Chinese Medicine (CM) diagnostic model of coronary heart disease (CHD) and to confifirm the scientifific validity of CM theoretical basis from an algorithmic viewpoint. Four types of objective diagnostic data were collected from 835 CHD patients by using a self-developed CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm (REAL). REAL was employed to establish a Xin (Heart) qi defificiency, Xin yang defificiency, Xin yin defificiency, blood stasis, and phlegm fifive-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively. The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.

A new style of acupuncture, named constitutional/conditional acupuncture (CCA), based on traditional Asian medical practices, was revealed in Kunming in 2017. This method is primarily based on pulse diagnosis, as developed in China, Korea, and India. The basic methods of CCA are briefly described, and a few case results from the 3-day Kunming workshop are presented to reveal the efficacy of CCA.