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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. [ABSTRACT FROM AUTHOR]

Radial pulse diagnosis is the most common method to examine the human health state in Traditional East Asian Medicine (TEAM). A cold stress-related suboptimal health state (subhealth) is often undetectable during routine medical examinations, however, it can be detected through the palpation of specific pulse waves, particularly a 'tight pulse', in TEAM. Therefore, this study examined a correlation between 'tight pulse' and vascular changes in the radial artery (RA) induced by a cold pressor trial (CPT). Twenty healthy subjects underwent sequentially control trial and CPT with room-temperature and ice-cold water, respectively, on the right forearm. The radial pulse and vascular changes were then examined on the left arm. The radial pulse scores for frequencies of 'tight pulse' with strong arterial tension increased after the CPT compared with the control trial. The pulse scores were reversely correlated with the RA thickness and volumes in ultrasonography, but not with changes in the systolic/diastolic blood pressure. The RA thickness-based vascular surface and three-dimensional images visualized a 'tight pulse' showing the vasoconstriction and bumpy-/rope-shaped vascular changes in the radial pulse diagnostic region after the CPT. These findings provide valuable insights into the potential integration of clinical radial pulse diagnosis with ultrasonography for cold-related subhealth. [ABSTRACT FROM AUTHOR]

Introduction: Pulse harmonic analysis is a quantitative and objective methodology within traditional Chinese medicine (TCM) used to evaluate pulse characteristics. However, interpreting pulse wave data is challenging due to its inherent complexity. This study aims to provide a comprehensive review and comparison of existing human pulse wave harmonic analysis methods to elucidate their patterns and characteristics. Methods: A systematic review of clinical research reports published from 1990 to 2021 was conducted, focusing on variations in harmonic characteristics across different medical conditions and physiological states. Keyword searches included terms related to analysis methods (e.g., ''Pulse Spectrum,'' ''harmonic analysis,'' ''harmonic index'') and measured indicators (e.g., ''vascular response,'' ''PPG,'' ''Photoplethysmography,'' ''aortic,'' ''arterial,'' ''blood pressure''). Supplementary research using PubMed's Mesh terms specifically targeted ''Pulse wave analysis'' within the methods and statistical analysis domain. Articles were filtered based on predefined criteria, including human participants and research related to pulse pressure or vascular volume changes. Conference papers, animal studies, and irrelevant research were excluded, with literature evaluation scales selected based on the retrieved research reports. Results: Initially, 6487 research reports were identified, and after screening, 50 reports were included in the review. The analysis revealed that low-frequency harmonics increase following vigorous activity or sympathetic excitation but decrease during rest or parasympathetic excitation. Cardiovascular patients exhibited elevated first harmonics associated with the liver meridian, while diabetes patients displayed weakened third harmonics related to the spleen meridian. Liver dysfunction was linked to changes in the first harmonic, and cancer patients showed signs of liver and kidney yin deficiency in the first and second harmonics. These findings underscore the potential of harmonic analysis for TCM disease diagnosis and organ assessment. Moreover, individuals with conditions such as liver dysfunction, cancer, and gynecological disorders displayed distinct intensity patterns across harmonics one through ten compared to healthy controls, albeit with some variations. Heterogeneity in these studies mainly stemmed from differences in measurement methods and study populations. Additionally, research suggested that factors like blood circulation and cognitive activity influenced harmonic intensity. Conclusions: In summary, this report consolidates prior research on pulse wave harmonics analysis, revealing unique patterns associated with various physiological conditions. Despite limitations, such as limited sample sizes in previous studies, the observed associations between physiological states and harmonics hold promise for potential clinical applications. This study lays a solid foundation for future applications of arterial wave harmonics analysis, promoting wider adoption of this analytical approach.

Traditional Chinese medicine (TCM) has been used for thousands of years and has been proven to be effective at treating many complicated illnesses with minimal side effects. The application and advancement of TCM are, however, constrained by the absence of objective measuring standards due to its relatively abstract diagnostic methods and syndrome differentiation theories. Ongoing developments in machine learning (ML) and deep learning (DL), specifically in computer vision (CV) and natural language processing (NLP), offer novel opportunities to modernize TCM by exploring the profound connotations of its theory. This review begins with an overview of the ML and DL methods employed in TCM; this is followed by practical instances of these applications. Furthermore, extensive discussions emphasize the mature integration of ML and DL in TCM, such as tongue diagnosis, pulse diagnosis, and syndrome differentiation treatment, highlighting their early successful application in the TCM field. Finally, this study validates the accomplishments and addresses the problems and challenges posed by the application and development of TCM powered by ML and DL. As ML and DL techniques continue to evolve, modern technology will spark new advances in TCM. [ABSTRACT FROM AUTHOR]

Objective In the theories of pulse disgnosis in traditional Chinese medicine (TCM), it is emphasized that pulse manifestations at the radial artery within the wrist (called Cunkou) signify the physiological and pathological conditions of different internal organs in the human body. However, different opinions exist among researchers about the objectiveness of the pulse diagnosis technique. Some researchers mentioned that no significant differences were observed in pulse manifestations at various Cunkou areas, hence there might be some difficulty in evaluating the status of different organs through checking pulse manifestations at Cunkou. This research aims to analyze the pulse response at Cunkou from the aspect of the characteristics of tactile sensing, thus to give a preliminary explanation to the above question. Methods This research utilized the Weber-Fechner law to model the tactile sensing as a dynamic low-pass signal filter with varying bandwidths under different compression levels during pulse diagnosis. The model was applied to analyzing the clinical data collected previously by our group. The arterial pressures measured invasively with equipment from 14 patients with aorta coarctation were processed to simulate different pulse manifestations at Cun, Guan, and Chi positions of Cunkou when different compression levels were applied. Results Due to the characteristics of tactile sensing, significant variations were observed in pulse manifestations at different pulse-depths under the application of changing compression levels; while no such changes in pulse manifestations were observed from the employment of transducer only, without tactile sensing involved. The results explained why different understandings on pulse manifestations were formed between direct pulse-taking technique in TCM and modern sphygmography using transducers. The features of pulse manifestations at Cunkou, using direct pulse-taking technique but at different depths, superficial, middle, and deep positions, respectively, predicted by the developed tactile sensing model were in line with those described in TCM pulse theories. Conclusion Based on the developed tactile sensing model, this study preliminarily explains the phenomenon that pulse manifestation at Cunkou changes in response to the compression force applied during TCM pulse-taking. Integrating the tactile sensing model with the study of TCM pulse diagnosis opens a new chapter for visualizing and quantitatively interpreting pulse manifestations. This not only expands the scope of pulse diagnosis study effectively, but also provide a scientific basis for further technical upgrading and optimization of existing pulse diagnosis equipment. [ABSTRACT FROM AUTHOR]

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Objective: To provide ideas for the modernization of pulse diagnosis in traditional Chinese medicine (TCM) by comparing and analyzing the current status and trends of modern research on pulse diagnosis in China and abroad, using bibliometric and visualization software. Methods: Modern research literature on pulse diagnosis was searched in China National Knowledge Infrastructure (CNKI) database from the foundation to May 31, 2023, and in Science Citation Index Expanded (SCIE) from January 1, 2003, to May 31, 2023. After further screening, Microsoft Excel 2019 was used for statistical analysis of publication volume, and CiteSpace (6.1.R6) and VOSviewer (1.6.20) softwares were employed for visual analysis of journals, countries/regions, authors, institutions, keywords, etc. Results: This study included a total of 764 articles in Chinese and 1 459 articles in English. The publication trend of pulse diagnosis research in SCIE database showed an overall fluctuating upward trend, while it exhibited a fluctuating downward trend after 2007 in CNKI database. The volume of English research literature has consistently exceeded that of Chinese literature since 2009. Publications on pulse diagnosis research involved 74 countries/regions. The related journals covered various disciplinary fields, including mathematics, physics, chemistry, and computer science. The most prolific author in CNKI database was WANG Yiqin (Shanghai University of Traditional Chinese Medicine), while the highest number of publications was attributed to ZHANG David (The Hong Kong Polytechnic University) in SCIE database. High-volume institutions in pulse diagnosis research in China and abroad were predominantly TCM research institutions. However, comprehensive universities and other research institutions also made noteworthy contributions. In recent years, hot topics in the modernization of pulse diagnosis research in China included pulse waves, sensors, and artificial intelligence. Foreign research focused on pulse diagnosis systems, sensors, pulse feature extraction, pulse signal analysis, pulse detection, and efficiency of use. Conclusion: Chinese scholars have shown notable participation and emphasis in the modernization research of pulse diagnosis, involving a wide range of disciplinary fields and indicating a characteristic of multidisciplinary cross-fusion development. The hotspots and trends in the modernization of pulse diagnosis research primarily concentrate on the study of pulse condition and signal acquisition, the integration, development, and optimization of various algorithms with pulse diagnosis equipment, and the practical application research of existing objectified outcomes of pulse diagnosis.

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.

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.

Radial pulse diagnosis is the most common method to examine the human health state in Traditional East Asian Medicine (TEAM). A cold stress-related suboptimal health state (subhealth) is often undetectable during routine medical examinations, however, it can be detected through the palpation of specific pulse waves, particularly a ‘tight pulse’, in TEAM. Therefore, this study examined a correlation between ‘tight pulse’ and vascular changes in the radial artery (RA) induced by a cold pressor trial (CPT). Twenty healthy subjects underwent sequentially control trial and CPT with room-temperature and ice-cold water, respectively, on the right forearm. The radial pulse and vascular changes were then examined on the left arm. The radial pulse scores for frequencies of ‘tight pulse’ with strong arterial tension increased after the CPT compared with the control trial. The pulse scores were reversely correlated with the RA thickness and volumes in ultrasonography, but not with changes in the systolic/diastolic blood pressure. The RA thickness-based vascular surface and three-dimensional images visualized a ‘tight pulse’ showing the vasoconstriction and bumpy-/rope-shaped vascular changes in the radial pulse diagnostic region after the CPT. These findings provide valuable insights into the potential integration of clinical radial pulse diagnosis with ultrasonography for cold-related subhealth.

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The eight extraordinary vessels have been one of the more controversial issues in the long history of Chinese medicine. Using pulse diagnosis, this mysterious subject can be clarified and shown to be a mechanism for guiding the energetic development of each individual from conception until the end of life. These vessels integrate the theories of yīnyáng and the five elements (especially focused on the water and fire elements), and the pulse-guided approach described makes both diagnosis and acupuncture treatment easier and more accurate to successfully apply. A case history illustrates this remarkably powerful methodology, curing a debilitating case of 'Long Covid' with just one treatment. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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”.

Pneumoconiosis 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. [ABSTRACT FROM AUTHOR]

The name Bian Que, like that of the Yellow Emperor (Huangdi), has reverberated through the development of Chinese medicine since the time of the Warring States. The discovery of a human figurine showing channels and strategic points, together with a number of medical texts, during the excavation of the Laoguanshan Han tomb in Chengdu, Sichuan, in 2012–13 has reignited controversies about whether it is correct to speak of a specific Bian Que school, or whether, as this paper argues, these texts were written by the Han physicians who used Bian Que as a mouthpiece to record their own medical expositions. The paper begins by examining the main characteristics of the Laoguanshan human figurine and discusses what this excavated artifact reveals about the early history of Chinese medicine. It questions the existence of the so-called Bian Que school and, obliquely, the suggested relationships between the school and the figurine and between the school and medical texts found in the same tomb. The paper shows how diverse the disjointed knowledge of medicine was and that the idea of a "school" does not accurately reflect what was happening in the transmission of medical knowledge during the Warring States, Qin, and Han periods (475 BCE–220 CE). [ABSTRACT FROM AUTHOR]

Pulse diagnosis has remained underexamined in medical anthropology, despite the potential of ethnographic investigation to enrich and enliven the discussion of the tactility-centered practice and its associated issues of experience, language, and the body. By capturing boundary-crossing interactions between the diagnosing and diagnosed, experience and expression, and activity and passivity, in this article I provide ethnographic details of how East Asian tactile diagnostics operate. My argument illustrates various ways of knowing and experiencing things and beings, hitherto dominated by vision and visualization as well as the divide between active knower and fixed objects. [ABSTRACT FROM AUTHOR]

Computational pulse diagnosis is a convenient, non-invasive, and effective Diabetes Mellitus (DM) detection technique. Generally, diverse pulse features are extracted from different views to represent pulse signals and then used for achieving the pulse diagnosis. However, current pulse-based DM detection methods only used one pulse feature for detection, ignoring the fact that diverse pulse features can be combined together to boost the diagnosis performance. To this end, we propose a novel Multi-Feature Complementary Learning (MFCL) model for DM detection. By designing feature-specific projections, multiple features are separately projected into a shared observation space and effectively fused into one vector. Besides, a mapping function is built to correlate the fused vectors to category labels to make the fused vectors suitable for classification. Inspired by the graph Laplacian matrix, which effectively preserves the correlations among samples from different categories, we integrate it in MFCL and design a discriminative prior to make the fused vectors sufficiently discriminative. Finally, an optimization algorithm is proposed to alternatively optimize the projection variables and then generate fused feature vectors. The proposed method reaches an accuracy of 92.85% in DM detection, outperforming state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this study, we have developed a new pen‐type pulse wave measurement system to evaluate heart failure treatment based on cardiovascular information. In order to acquire minute pathological changes in diseases such as heart failure, the pen‐type pulse wave measurement system is equipped with a high‐resolution MEMS tactile sensor that achieves a displacement resolution of 0.4 μm and an input resolution of 50μN. This measurement system consists of a small sensor package, a pen‐type measurement system, and a glass sensor package to enable physicians to measure the radial artery of men and women of all ages in a minimally invasive, rapid, and precise manner. Realizing this measurement system, simple and precise pulse wave measurement by direct measurement and measurement of pulse wave characteristics for individual differences has been possible. [ABSTRACT FROM AUTHOR]

Objective To establish a model for assessing the degree of coronary artery obstruction based on features of their pulse-graph parameters, so as to explore the clinical diagnostic value of pulse diagnosis. Methods Smart TCM-I electropulsograph was used to collect pulse samples. According to degrees of coronary artery occlusion based on coronary angiography (CAG) result, 531 patients with coronary heart disease (CHD) or suspicious CHD were divided into three groups: non-occlusion group, mild occlusion group and moderate/severe occlusion group. The time-domain features and MSE features of pulse samples were extracted by time-domain analysis and Multiscale entropy (MSE), and the differences in pulse features among different groups were compared with nonparametric test. Based on the features of pulse-graph parameters, the model for assessing the degree of coronary artery occlusion was established with the random forest (RF) machine leaning method. Results Compared with the non-occlusion group, h2/h1 and h3/h1 among time-domain features of pulse-graph were significantly increased in mild occlusion group and moderate/severe occlusion group (P<0. 05). Compared with the non-occlusion group and mild occlusion group, MSE1, MSE2, MSE3, MSE4 and MSE5 were significantly decreased in moderate/severe occlusion group (P<0. 01). Based on the characteristics of pulse-graph parameters of 531 cases, a model for assessing degree of coronary artery occlusion was established with a 86.79% average accuracy rate. Conclusion Pulse detection technology has potential application value in the assessment of coronary risk events. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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Artificial intelligence (AI) aims to mimic human cognitive functions and execute intellectual activities like that performed by humans dealing with an uncertain environment. The rapid development of AI technology provides powerful tools to analyze massive amounts of data, facilitating physicians to make better clinical decisions or even replace human judgment in healthcare. Advanced AI technology also creates novel opportunities for exploring the scientific basis of traditional Chinese medicine (TCM) and developing the standardization and digitization of TCM pulse diagnostic methodology. In the present study, we review and discuss the potential application of AI technology in TCM pulse diagnosis. The major contents include the following aspects: (1) a brief introduction of the general concepts and knowledge of TCM pulse diagnosis or palpation, (2) landmark developments in AI technology and the applications of common AI deep learning algorithms in medical practice, (3) the current progress of AI technology in TCM pulse diagnosis, (4) challenges and perspectives of AI technology in TCM pulse diagnosis. In conclusion, the pairing of TCM with modern AI technology will bring novel insights into understanding the scientific principles underlying TCM pulse diagnosis and creating opportunities for the development of AI deep learning technology for the standardization and digitalization of TCM pulse diagnosis.

The vague depiction of the pulse in traditional pulse diagnosis and the lack of an objective physical measure not only hinder pulse diagnosis, but they also make it impossible to interface with modern scientific systems. In the following, we report our scientific and modern reconstruction of the pulse diagnosis system based on the principles of Huangdi Neijing, "the diagnosis of abnormal pulse", and Jingui Yaolue," the pulse should be taken as too much or too little". We conducted Systematic Sphygmology for Pattern Differentiation breaking through the limitations of physical images, and we extended the concept of "pulse condition elements" beyond the physical level. Our analysis clearly answers the question of "too much or too little" of cognitive pulse diagnosis put forward by the ancients. "Physical image" refers to the use of descriptive language that is difficult to quantify to describe an object; in contrast to the physical image, "physics" refers to the use of measurable physical quantities to objectively describe things, so as to more clearly present pulse information. The reconstruction of the pulse diagnosis system is very important for clinical and scientific research. [ABSTRACT FROM AUTHOR]

Pulse diagnosis is an irreplaceable part of traditional Chinese medical science. However, application of the traditional pulse monitoring method was restricted in the modernization of Chinese medical science since it was difficult to capture real signals and integrate obscure feelings with a modern data platform. Herein, a novel multichannel pulse monitoring platform based on traditional Chinese medical science pulse theory and wearable electronics was proposed. The pulse sensing platform simultaneously detected pulse conditions at three pulse positions (Chi, Cun, and Guan). These signals were fitted to smooth surfaces to enable 3-dimensional pulse mapping, which vividly revealed the shape of the pulse length and width and compensated for the shortcomings of traditional single-point pulse sensors. Moreover, the pulse sensing system could measure the pulse signals from different individuals with different conditions and distinguish the differences in pulse signals. In addition, this system could provide full information on the temporal and spatial dimensions of a person's pulse waveform, which is similar to the true feelings of doctors' fingertips. This innovative, cost-effective, easily designed pulse monitoring platform based on flexible pressure sensor arrays may provide novel applications in modernization of Chinese medical science or intelligent health care. [ABSTRACT FROM AUTHOR]

Background: Fatigue 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. Methods: Tongue 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. Results: The 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. Conclusion: This 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. [ABSTRACT FROM AUTHOR]

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.

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.

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. [ABSTRACT FROM AUTHOR]

Constitutional Conditional Acupuncture (CCA) is a novel style of acupuncture based on traditional Chinese, Korean and Indian medical texts and practices. While effective in the treatment of a broad range of health conditions, it appears to be remarkably useful in the diagnosis and treatment of cancer - treating the illness itself in addition to relieving the presenting symptoms and the side-effects of orthodox medical treatment. This article presents protocols for treating cancer on the basis of constitutional diagnosis, and describes a pulse diagnostic signal that correlates with metastatic activity. The encouraging results of a pilot study of ten cases from the clinical practice of the author are reported. [ABSTRACT FROM AUTHOR]

Pulse palpation is the cornerstone of the classical medicine of the Huángdì Neijīng (Yellow Emperor's Inner Classic), and forms the majority of the text's approach to diagnostics and subsequent needling of the primary channels (jīngmai). Rényíng/cùnk;ŏu pulse diagnosis is a ratio-based palpation method dependent on the strength of two pulses felt simultaneously at the wrist and neck, in order to determine a state of excess or insufficiency in the six pairings of the twelve primary channels. Considered a central diagnostic method of the Hàn dynasty (206 BCE - 220 CE) era of Chinese medicine, this paper will discuss the theory behind this unique diagnostic method and the basic principles of practice and application based on primary source and clinical research of the Huángdì Neijīng. [ABSTRACT FROM AUTHOR]

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.

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