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

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

Development and Application of Traditional Chinese Medicine Using AI Machine Learning and Deep Learning Strategies.

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.

Visualizing a Cold Stress-Specific Pulse Wave in Traditional Pulse Diagnosis ('Tight Pulse') Correlated with Vascular Changes in the Radial Artery Induced by a Cold Pressor Trial.

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.

Peripheral pulse harmonic analysis and its clinical application: A systematic review.

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.

Diagnosis of kidney insufficiency by using the pressure waveforms of wrist-type sphygmomanometers: toward a convenient point-of-care device.

OBJECTIVES: Digital sphygmomanometers have been used for more than 40 years in Western medicine for accurately measuring systolic and diastolic blood pressures, which are vital signs observed for the diagnosis of different diseases. Similarly, traditional Chinese medicine (TCM) has been using wrist pulse diagnosis for thousands of years. Some studies have combined digital wrist pulse signals and the diagnosis method of TCM to quantify pulse waves and identify diseases. However, the effectiveness of this approach is limited because of scattered methods and complex pathological features. Moreover, the literature on TCM does not provide quantitative data or objective indicators. METHODS: In this prospective study, we developed a diagnostic system that contains a modified sphygmomanometer. In addition, we designed a procedure for analyzing pulse waves with 156 features of harmonic modes and a decision tree method for diagnosing kidney insufficiency. RESULTS: In the decision tree method, at least three features of harmonic modes can achieve an accuracy of 0.86, a specificity of 0.91, and a Cohen's kappa coefficient of 0.72. By comparison, the random forest method can achieve an accuracy of 0.99, a specificity of 0.99, and a Cohen's kappa coefficient of 0.94 within 200 trees. The results of this study indicated that even in patients with kidney insufficiency and complex etiology, common features can be distinguished by identifying changes in pulse waveforms. CONCLUSION: By using the modified sphygmomanometer to measure blood pressure, people can monitor their health status and take care of it in advance by simply measuring their blood pressure.

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

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

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

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

A new model for predicting the occurrence of polycystic ovary syndrome: Based on data of tongue and pulse.

Background and objective: Polycystic ovary syndrome is one of the most common types of endocrine and metabolic diseases in women of reproductive age that needs to be screened early and assessed non-invasively. The objective of the current study was to develop prediction models for polycystic ovary syndrome based on data of tongue and pulse using machine learning techniques. Methods: A dataset of 285 polycystic ovary syndrome patients and 201 healthy women were investigated to identify the significant tongue and pulse parameters for predicting polycystic ovary syndrome. In this study, feature selection was performed using least absolute shrinkage and selection operator regression. Several machine learning algorithms (multilayer perceptron classifier, eXtreme gradient boosting classifier, and support vector machine) were used to construct the classification models to predict the presence of polycystic ovary syndrome. Results: TB-L, TB-a, TB-b, TC-L, TC-a, h3, and h4/h1 in tongue and pulse parameters were statistically associated with polycystic ovary syndrome presence. Among the several machine learning techniques, the support vector machine model was optimal for the comprehensive evaluation of this dataset and deduced the area under the receiver operating characteristic curve, DeLong test, calibration curve, and decision curve analysis. Conclusion: The machine learning model with tongue and pulse factors can predict the existence of polycystic ovary syndrome precisely.

Experiences, Expressions, and Boundary-Crossings: East Asian Tactile Diagnostics in South Korea.

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.

Current status and trends in the modernization of pulse diagnosis research: a bibliometric analysis based on CiteSpace and VOSviewer / 数字中医药（英文）

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 andtrends 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.

Perceptions of traditional Chinese medicine doctors about using wearable devices and traditional Chinese medicine diagnostic instruments: A mixed-methodology study.

Objective: This study aimed to investigate the perceptions of traditional Chinese medicine doctors about wearable devices and diagnostic instruments and explore the factors that influence them. Methods: Data on the perceptions of the traditional Chinese medicine doctors in Hangzhou, China, about wearable devices and diagnostic instruments were collected through face-to-face semi-structured interviews. The author coded the interview responses using grounded theory. A cross-sectional survey was conducted in four traditional Chinese medicine hospitals in Hangzhou, China. The responses of 385 traditional Chinese medicine doctors were considered valid. Descriptive statistics and binary logistic regression models were used for analysis. Results: This study categorized the perceptions of traditional Chinese medicine about wearable devices and traditional Chinese medicine diagnostic instruments under convenience, reliability, suitable population, machine usage scenario, and the integration of traditional Chinese medicine and information communication technology. Convenience encompassed portability and the convenience of carrying instruments or wearing the devices and operating them and the human-device interface. Reliability encompassed the underlying principles, accuracy, durability, and reference to diagnosis. Suitability for people encompassed age distinction and disease differentiation. Machine usage scenarios included use in daily life, educational institutions, and primary medical institutions. The combination of traditional Chinese medicine and information communication technology encompassed the integration of traditional Chinese medicine and wearable functions and diagnostic interpretation. The perceptions of traditional Chinese medicine doctors were affected by age, title, type of hospital, and specialty. Conclusions: The use of wearable devices and traditional Chinese medicine diagnostic instruments has gradually been accepted by traditional Chinese medicine doctors. Traditional Chinese medicine doctors need to improve their knowledge and skills for information communication technology integration, and their standardized training should incorporate information communication technology and digital health.

Wrist pulse signal acquisition and analysis for disease diagnosis: A review.

Pulse diagnosis (PD) plays an indispensable role in healthcare in China, India, Korea, and other Orient countries. It requires considerable training and experience to master. The results of pulse diagnosis rely heavily on the practitioner's subjective analysis, which means that the results from different physicians may be inconsistent. To overcome these drawbacks, computational pulse diagnosis (CPD) is used with advanced sensing techniques and analytical methods. Focusing on the main processes of CPD, this paper provides a systematic review of the latest advances in pulse signal acquisition, signal preprocessing, feature extraction, and signal recognition. The most relevant principles and applications are presented along with current progress. Extensive comparisons and analyses are conducted to evaluate the merits of different methods employed in CPD. While much progress has been made, a lack of datasets and benchmarks has limited the development of CPD. To address this gap and facilitate further research, we present a benchmark to evaluate different methods. We conclude with observations of the status and prospects of CPD.

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

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

Wrist pulse diagnosis of stable coronary heart disease based on acoustics waveforms.

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.

A new interpretation of TCM pulse diagnosis based on quantum physical model of the human body / 数字中医药（英文）

@#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”.

Three-Dimensional Arterial Pulse Signal Acquisition in Time Domain Using Flexible Pressure-Sensor Dense Arrays.

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.

Can Traditional Chinese Medicine Diagnosis Be Parameterized and Standardized? A Narrative Review.

The integration of Traditional Chinese Medicine (TCM) in Western health systems and research requires a rational communicable theory, scientific proof of efficacy and safety, and quality control measures. The existence of clear definitions and the diagnosis standardization are critical factors to establish the patient's vegetative functional status accurately and, therefore, systematically apply TCM therapeutics such as the stimulation of reflex skin areas known as acupoints. This science-based conceptualization entails using validated methods, or even developing new systems able to parameterize the diagnosis and assess TCM related effects by objective measurements. Traditionally, tongue and pulse diagnosis and the functional evaluation of action points by pressure sensitivity and physical examination may be regarded as essential diagnostic tools. Parameterizing these techniques is a future key point in the objectification of TCM diagnosis, such as by electronic digital image analysis, mechanical pulse diagnostic systems, or the systematic evaluation of acupoints' electrophysiology. This review aims to demonstrate and critically analyze some achievements and limitations in the clinical application of device-assisted TCM diagnosis systems to evaluate functional physiological patterns. Despite some limitations, tongue, pulse, and electrophysiological diagnosis devices have been reported as a useful tool while establishing a person's functional status.

The novel three-dimensional pulse images analyzed by dynamic L-cube polynomial model.

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.

A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning.

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.

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

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