How to standardize the pulse-taking method of traditional Chinese medicine pulse diagnosis

Advancements in Bio-Integrated Flexible Electronics for Hemodynamic Monitoring in Cardiovascular Healthcare

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, highlighting the urgent need for effective monitoring and prevention strategies. The rapid advancement of flexible sensing technology and the development of conformal sensors have attracted significant attention due to their potential for continuous, real-time assessment of cardiovascular health over extended periods. This review outlines recent advancements in bio-integrated flexible electronics designed for hemodynamic monitoring and broader CVD healthcare applications. It introduces key physiological indicators relevant to hemodynamics, including heart rate, blood pressure, blood flow velocity, and cardiac output. Next, it discusses flexible bio-integrated electronics engineering strategies, such as working principles and configuration designs. Various non-invasive and invasive bio-integrated devices for monitoring these hemodynamic indicators are then presented. Additionally, the review highlights the role of artificial intelligence algorithms and their practical applications in bio-integrated electronics for hemodynamic detection. Finally, it proposes future directions and addresses potential challenges in the field.

Replication of Radial Pulses Using Magneto-Rheological Fluids

The radial pulse is a critical health marker with expanding applications in wearable technology. To improve these applications, developing a pulse generator that consistently produces realistic pulses is crucial for validation and training. The goal of this study was to design and test a cost-effective pulse simulator that can accurately replicate a wide range of age-dependent radial pulses with simplicity and precision. To this end, this study incorporated a magneto-rheological (MR) fluid device into a cam-based pulse simulator. The MR device, as a key component, enables pulse shaping without the need for additional cams, substantially reducing the cost and complexity of control compared with existing pulse simulators. To evaluate the performance of the MR pulse simulator, the root-mean-square (RMS) error criterion (less than 5%) was used to compare the experimentally obtained pulse waveform with the in vivo pulse waveform for specific age groups. After demonstrating that the MR simulator could produce three representative in vivo pulses, a parametric study was conducted to show the feasibility of the slope-based pulse-shaping method for the MR pulse simulator to continuously generate a range of age-related pulses.

An FBG-based optical pressure sensor for the measurement of radial artery pulse pressure

One of the diagnostic tool for clinical evaluation and disease diagnosis is a pulse waveform analysis. High fidelity radial artery pulse waveforms have been investigated in clinical research to compute central aortic pressure, which has been demonstrated to be predictive of cardiovascular diseases. The radial artery must be inspected from several angles in order to obtain the best pulse waveform for estimate and diagnosis. In this study, we present the design and experimental testing of an optical sensor based on Fiber Bragg Gratings (FBG). A 3D printed device along with the FBG is used to measure the radial artery pulses. The proposed sensor is used for the purpose of quantifying the radial artery pulse waveform across major pulse position point. The suggested optical sensing system can measure the pulse signal with good accuracy. The main characteristic parameters of the pulse can then be retrieved from the processed signal for their use in clinical applications. By conducting experiments under the direction of medical experts, the pulse signals are measured. In order to experimentally validate the sensor, we used it to detect the pulse waveforms at Guan position of the wrist's radial artery in accordance with the diagnostic standards. The findings show that combining optical technologies for physiological monitoring and radial artery pulse waveform monitoring using FBG in clinical applications are highly feasible.

A comprehensive review on technological advancements for sensor-based Nadi Pariksha: An ancient Indian science for human health diagnosis

Nadi Pariksha is a significant, rather symbolic term for Ayurveda. Ancient Ayurvedic literature has prominently stated its importance in the judgment of Tridoshas (Vata, Pitta, and Kapha) which are the base of ailment diagnosis and prediction. The knowledge about Nadi Pariksha is uncovered in various ancient Ayurvedic literature like Ravansamhita, Bhavprakash, Nadivigyan by Kanad, Sharangdhar, and Yogratnakar. The various Nadi parameters are indicative of the diagnosis of diseases. These techniques were used as popular diagnostic tools in Indian culture from ancient days. Still, nowadays, these are not being used explicitly due to the lack of expertise, so it is necessary to establish their results once gained so that they can be used along with technical aspects in today's era. Ayurveda believes that all the elements of the Universe are present in any human body in minute, proportionate quantity, and the Nadi represents these elements, that is, Vata, Pitta, and Kapha (VPK). To facilitate the Nadi Pariksha using appropriate sensors may help the Ayurveda practitioners diagnose Prakriti and predict some diseases, making the Nadi Pariksha more reliable and faster. This review paper lists, 2 books and 67 research papers, mostly from countries like India, China, Japan, Korea, etc., from various reputed databases. The review primarily concentrates on six research themes: sensors and devices used for Nadi signal acquisition, signal pre-processing methods, feature extraction methods, feature selection approaches, classification practices, diseases diagnosed, and results attained. The paper also reviews the challenges in implementing the automated Nadi Pariksha with technological aid, which is a necessity of this period and is a very vibrant research arena. Yet significant work remains to be done, like bridging the gaps between technical and commercial development, and the procedure standardization is also required.

Machine learning-coupled tactile recognition with high spatiotemporal resolution based on cross-striped nanocarbon piezoresistive sensor array

Flexible pressure sensor arrays have been playing important roles in various applications of human-machine interface, including robotic tactile sensing, electronic skin, prosthetics, and human-machine interaction. However, it remains challenging to simultaneously achieve high spatial and temporal resolution in developing pressure sensor arrays for tactile sensing with robust function to achieve precise signal recognition. This work presents the development of a flexible high spatiotemporal piezoresistive sensor array (PRSA) by coupling with machine learning algorithms to enhance tactile recognition. The sensor employs cross-striped nanocarbon-polymer composite as an active layer, though screen printing manufacture processes. A miniaturized signal readout circuit and transmission board is developed to achieve high-speed acquisition of distributed pressure signals from the PRSA. Test results indicate that the developed PRSA platform simultaneously possesses the characteristics of high spatial resolution up to 1.5 mm, fast temporal resolution of about 5 ms, and long-term durability with a variation of less than 2%. The PRSA platform also exhibits excellent performance in real-time visualization of multi-point touch, mapping embossed shapes, and tracking motion trajectory. To test the performance of PRSA in recognizing different shapes, we acquired pressure images by pressing the finger-type device coated with PRSA film on different embossed shapes and implementing the T-distributed Stochastic Neighbor Embedding model to visualize the distinction between images of different shapes. Then we adopted a one-layer neural network to quantify the discernibility between images of different shapes. The analysis results show that the PRSA could capture the embossed shapes clearly by one contact with high discernibility up to 98.9%. Collectively, the PRSA as a promising platform demonstrates its promising potential for robotic tactile sensing.

Increasing the sensor channels: a solution for the pressing offsets that cause the physiological parameter inaccuracy in radial artery pulse signal acquisition

Introduction: In studies of pulse wave analysis, single-channel sensors only adopt single temporal pulse signals without spatial information to show pulse-feeling patterns. Multi-channel arterial pulse signals, also named as three-dimensional pulse images (3DPIs), provide the spatial and temporal characteristics of radial pulse signals. When involving single or few-channel sensors, pressing offsets have substantial impacts on obtaining inaccurate physiological parameters like tidal peak (P2). Methods: This study discovers the pressing offsets in multi-channel pulse signals and analyzes the relationship between the pressing offsets and time of P2 (T2) by qualifying the pressing offsets. First, we employ a data acquisition system to capture 3DPIs. Subsequently, the errorT2 is developed to qualify the pressing offsets. Results: The outcomes display a central low and peripheral high pattern. Additionally, the errorT2 increase as the distances from the artery increase, particularly at the radial ends of the blood flow direction. For every 1 mm increase in distances between sensing elements and center sensing elements, the errorT2 in the radial direction escalates by 4.87%. When the distance is greater than 3.42 mm, the errorT2 experiences a sudden increase. Discussion: The results show that increasing the sensor channels can overcome the pressing offsets in radial pulse signal acquisition.

Multichannel Flexible Pulse Perception Array for Intelligent Disease Diagnosis System

Pressure sensors with high sensitivity, a wide linear range, and a quick response time are critical for building an intelligent disease diagnosis system that directly detects and recognizes pulse signals for medical and health applications. However, conventional pressure sensors have limited sensitivity and nonideal response ranges. We proposed a multichannel flexible pulse perception array based on polyimide/multiwalled carbon nanotube-polydimethylsiloxane nanocomposite/polyimide (PI/MPN/PI) sandwich-structure pressure sensor that can be applied for remote disease diagnosis. Furthermore, we established a mechanical model at the molecular level and guided the preparation of MPN. At the structural level, we achieved high sensitivity (35.02 kPa-1) and a broad response range (0-18 kPa) based on a pyramid-like bilayer microstructure with different upper and lower surfaces. A 27-channel (3 × 9) high-density sensor array was integrated at the device level, which can extract the spatial and temporal distribution information on a pulse. Furthermore, two intelligent algorithms were developed for extracting six-dimensional pulse information and automatic pulse recognition (the recognition rate reaches 97.8%). The results indicate that intelligent disease diagnosis systems have great potential applications in wearable healthcare devices.

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.

Cross-Channel Dynamic Weighting RPCA: A De-Noising Algorithm for Multi-Channel Arterial Pulse Signal

Pulse wave analysis (PWA) has been widely used in the medical field. A novel multi-channel sensor is employed in arterial pulse acquisition and brings richer physiological information to PWA. However, the noise of this sensor is distributed in the main frequency band of the pulse signal, which seriously interferes with subsequent analyses and is difficult to eliminate by existing methods. This study proposes a cross-channel dynamic weighting robust principal component analysis algorithm. A channel-scaled factor technique is used to manipulate the weighting factors in the nuclear norm. This factor can adaptively adjust the weights among the channels according to the signal pattern of each channel, optimizing the feature extraction in multi-channel signals. A series of performance evaluations were conducted, and four well-known de-noising algorithms were used for comparison. The results reveal that the proposed algorithm achieved one of the best de-noising performances in the time and frequency domains. The mean of h1 in the amplitude relative error (ARE) was 23.4% smaller than for the WRPCA algorithm. Moreover, our algorithm could accelerate convergence and reduce the computational time complexity by approximately 34.6%. These results demonstrate the performance and efficiency of the algorithm. Meanwhile, the idea can be extended to other multi-channel physiological signal de-noising and feature extraction fields.

Wearable multichannel pulse condition monitoring system based on flexible pressure sensor arrays

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.

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.

Two-phase non-invasive multi-disease detection via sublingual region

Non-invasive multi-disease detection is an active technology that detects human diseases automatically. By observing images of the human body, computers can make inferences on disease detection based on artificial intelligence and computer vision techniques. The sublingual vein, lying on the lower part of the human tongue, is a critical identifier in non-invasive multi-disease detection, reflecting health status. However, few studies have fully investigated non-invasive multi-disease detection via the sublingual vein using a quantitative method. In this paper, a two-phase sublingual-based disease detection framework for non-invasive multi-disease detection was proposed. In this framework, sublingual vein region segmentation was performed on each image in the first phase to achieve the region with the highest probability of covering the sublingual vein. In the second phase, features in this region were extracted, and multi-class classification was applied to these features to output a detection result. To better represent the characterisation of the obtained sublingual vein region, multi-feature representations were generated of the sublingual vein region (based on color, texture, shape, and latent representation). The effectiveness of sublingual-based multi-disease detection was quantitatively evaluated, and the proposed framework was based on 1103 sublingual vein images from patients in different health status categories. The best multi-feature representation was generated based on color, texture, and latent representation features with the highest accuracy of 98.05%.

SYmptom-Based STratification of DiabEtes Mellitus by Renal Function Decline (SYSTEM): A Retrospective Cohort Study and Modeling Assessment

Background: Previous UK Biobank studies showed that symptoms and physical measurements had excellent prediction on long-term clinical outcomes in general population. Symptoms and signs could intuitively and non-invasively predict and monitor disease progression, especially for telemedicine, but related research is limited in diabetes and renal medicine. Methods: This retrospective cohort study aimed to evaluate the predictive power of a symptom-based stratification framework and individual symptoms for diabetes. Three hundred two adult diabetic patients were consecutively sampled from outpatient clinics in Hong Kong for prospective symptom assessment. Demographics and longitudinal measures of biochemical parameters were retrospectively extracted from linked medical records. The association between estimated glomerular filtration rate (GFR) (independent variable) and biochemistry, epidemiological factors, and individual symptoms was assessed by mixed regression analyses. A symptom-based stratification framework of diabetes using symptom clusters was formulated by Delphi consensus method. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were compared between statistical models with different combinations of biochemical, epidemiological, and symptom variables. Results: In the 4.2-year follow-up period, baseline presentation of edema (-1.8 ml/min/1.73m2, 95%CI: -2.5 to -1.2, p < 0.001), epigastric bloating (-0.8 ml/min/1.73m2, 95%CI: -1.4 to -0.2, p = 0.014) and alternating dry and loose stool (-1.1 ml/min/1.73m2, 95%CI: -1.9 to -0.4, p = 0.004) were independently associated with faster annual GFR decline. Eleven symptom clusters were identified from literature, stratifying diabetes predominantly by gastrointestinal phenotypes. Using symptom clusters synchronized by Delphi consensus as the independent variable in statistical models reduced complexity and improved explanatory power when compared to using individual symptoms. Symptom-biologic-epidemiologic combined model had the lowest AIC (4,478 vs. 5,824 vs. 4,966 vs. 7,926) and BIC (4,597 vs. 5,870 vs. 5,065 vs. 8,026) compared to the symptom, symptom-epidemiologic and biologic-epidemiologic models, respectively. Patients co-presenting with a constellation of fatigue, malaise, dry mouth, and dry throat were independently associated with faster annual GFR decline (-1.1 ml/min/1.73m2, 95%CI: -1.9 to -0.2, p = 0.011). Conclusions: Add-on symptom-based diagnosis improves the predictive power on renal function decline among diabetic patients based on key biochemical and epidemiological factors. Dynamic change of symptoms should be considered in clinical practice and research design.

Computational Traditional Chinese Medicine diagnosis: A literature survey

BACKGROUND AND OBJECTIVE

Traditional Chinese Medicine (TCM) diagnosis is based on the theoretical principles and knowledge, where it is steeped in thousands of years of history to diagnose various types of diseases and syndromes. It can be generally divided into four main diagnostic approaches: 1. Inspection, 2. Auscultation and olfaction, 3. Inquiry, and 4. Palpation, which are widely used in TCM hospitals in China and around the world. With the development of intelligent computing technology in recent years, computational TCM diagnosis has grown rapidly.

METHODS

In this paper, we aim to systematically summarize the development of computational TCM diagnosis based on four diagnostic approaches, mainly focusing on digital acquisition devices, collected datasets, and computational detection approaches (algorithms). Furthermore, all related works of this field are compared and explored in detail.

RESULTS

This survey provides the principles, applications, and current progress in computing for readers and researchers in terms of computational TCM diagnosis. Moreover, the future development direction, prospect, and technological trend of computational TCM diagnosis will also be discussed in this study.

CONCLUSIONS

Recent computational TCM diagnosis works are compared in detail to show the pros/cons, where we provide some meaningful suggestions and opinions on the future research approaches in this area. This work is useful for disease detection in computational TCM diagnosis as well as health management in the smart healthcare area.

INDEX TERMS

Computational diagnosis, Traditional Chinese Medicine, survey, smart healthcare.

The Interrelationships between Intestinal Permeability and Phlegm Syndrome and Therapeutic Potential of Some Medicinal Herbs

The gastrointestinal (GI) tract has an intriguing and critical role beyond digestion in both modern and complementary and alternative medicine (CAM), as demonstrated by its link with the immune system. In this review, we attempted to explore the interrelationships between increased GI permeability and phlegm, an important pathological factor in CAM, syndrome, and therapeutic herbs for two disorders. The leaky gut and phlegm syndromes look considerably similar with respect to related symptoms, diseases, and suitable herbal treatment agents, including phytochemicals even though limitations to compare exist. Phlegm may be spread throughout the body along with other pathogens via the disruption of the GI barrier to cause several diseases sharing some parts of symptoms, diseases, and mechanisms with leaky gut syndrome. Both syndromes are related to inflammation and gut microbiota compositions. Well-designed future research should be conducted to verify the interrelationships for evidence based integrative medicine to contribute to the promotion of public health. In addition, systems biology approaches should be adopted to explore the complex synergistic effects of herbal medicine and phytochemicals on conditions associated with phlegm and leaky gut syndromes.

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 Noncontact Method for Locating Radial Artery above Radial Styloid Process in Thermal Image

A radial artery above the radial styloid process is called GUAN and is a critical position for collecting pulse wave in traditional Chinese medicine theory. Locating GUAN is a precondition for collecting radial pulse wave. However, existing methods for locating GUAN lead to large deviations. This paper proposes a novel nontouch method for locating GUAN based on thermal imaging and image processing. This method consists of three parts: the infrared thermal imaging location imaging platform, the wrist edge contour extraction algorithm based on arbitrary angle edge recognition, and radial protrusion recognition algorithm (x coordinate identification algorithm of GUAN) and radial artery fitting algorithm (y coordinate identification algorithm of GUAN). The infrared thermal imaging positioning imaging platform is used to ensure that the wrist of the subject enters the fixed imaging area in a fixed position during each measurement and transmits the thermal imaging images carrying the image information of radial processes and radial arteries to the upper computer. Arbitrary angle edge recognition algorithm is used to extract wrist contour and radial artery edge information. The x-axis coordinates of the radial artery were provided by the identification algorithm, and the y-axis coordinates of the radial artery were provided by the fitting algorithm. Finally, the x and y coordinates determine the GUAN position. The algorithm for locating GUAN could provide repeatable and reliable x and y coordinates. The proposed method shows that relative standard deviation (RSD) of x distance of GUAN is less than 9.0% and RSD of y distance of GUAN is less than 5.0%. The proposed method could provide valid GUAN coordinates and reduce deviations of locating GUAN.

Fourier Series Analysis for Novel Spatiotemporal Pulse Waves: Normal, Taut, and Slippery Pulse Images

In this article, a three-dimensional pulse image (3DPI) instead of a one-dimensional temporal pulse wave is studied to elucidate its spatiotemporal characteristics. To check the spatial and temporal properties of 3DPI, adopted is Fourier series, in which a ratio (r) is defined as one amplitude divided by the sum of the first three amplitudes of harmonics. A ratio sequence is constituted from 70 to 90 ratios in a heartbeat with 70–90 3DPIs by sampling. Twenty-four subjects (14 males and 10 females with age of 22.2 ± 3.7 years, 20.4 ± 1.4 BMI, and 112.1 ± 4.7 mmHg systolic blood pressure) are involved in this research. There are significant statistical differences in the groups of the normal, taut, and slippery 3DPIs by the first harmonic ratio average (r1¯) and ratio difference (Δr₁) produced from the ratio sequence. The proposed method of this study gives us a novel viewpoint to clarify the spatiotemporal characteristics of pulse images, which can translate and quantize the pulse feeling in Chinese medicine texts.

A 3D Wrist Pulse Signal Acquisition System for Width Information of Pulse Wave

During pulse signal collection, width information of pulse waves is essential for the diagnosis of disease. However, currently used measuring instruments can only detect the amplitude while can't acquire the width information. This paper proposed a novel wrist pulse signal acquisition system, which could realize simultaneous measurements of the width and amplitude of dynamic pulse waves under different static forces. A tailor-packaged micro-electro-mechanical system (MEMS) sensor array was employed to collect pulse signals, a conditioning circuit was designed to process the signals, and a customized algorithm was developed to compute the width. Experiments were carried out to validate the accuracy of the sensor array and system effectiveness. The results showed the system could acquire not only the amplitude of pulse wave but also the width of it. The system provided more information about pulse waves, which could help doctors make the diagnosis.

Assessment of Intermingled Phlegm and Blood Stasis Syndrome in Coronary Heart Disease: Development of a Diagnostic Scale

BACKGROUND

Intermingled Phlegm and Blood Stasis Syndrome (IPBSS) is a common feature in patients with coronary heart disease (CHD). In clinical practice, the diagnostic agreement of clinical doctor of Chinese Medicine (CM) is poor. We previously developed a IPBSS diagnostic scale for use by general practitioner.

OBJECTIVES

To assess a IPBSS diagnostic scale that we previously developed for use by non-experts.

METHODS

This is a multicenter, prospective study involving eight study sites across China. Eligible patients were adults (≥18 years) with CHD as demonstrated by a history of myocardial infarction, stenosis, or past coronary revascularization. IPBSS was assessed using a scale that consisted of 14 items in two domains (e.g., phlegm and blood stasis). The score range for each item was 0 to 3 points. Maximum total score was 72 points. Diagnostic accuracy was verified using consensus opinion by two independent experts as reference.

RESULTS

A total of 1,142 CHD patients were included. IPBSS was established in 729 subjects using the IPBSS diagnostic scale. In ROC curve analyses, at the optimal cut-off of 25.5, the sensitivity and specificity of the IPBSS scale were 67.6% and 72.4%, respectively. The area under the ROC curve was 0.741 (95%CI: 0.711-0.772).

CONCLUSIONS

The newly developed IPBSS scoring system showed moderate performance in diagnosing IPBSS in CHD patients. Data from further large-scale diagnostic test accuracy studies are warranted. This trial is registered with ChiCTR-OOC-15006599.

Analysis of Pulse Signals Based on Array Pulse Volume

OBJECTIVE

To collect and analyze multi-dimensional pulse diagram features with the array sensor of a pressure profile system (PPS) and study the characteristic parameters of the new multi-dimensional pulse diagram by pulse diagram analysis technology.

METHODS

The pulse signals at the Guan position of left wrist were acquired from 105 volunteers at the Shanghai University of Traditional Chinese Medicine. We obtained the pulse data using an array sensor with 3×4 channels. Three dimensional pulse diagrams were constructed for the validated pulse data, and the array pulse volume (APV) parameter was computed by a linear interpolation algorithm. The APV differences among normal pulse (NP), wiry pulse (WP) and slippery pulse (SP) were analyzed using one-way analysis of variance. The coefficients of variation (CV) were calculated for WP, SP and NP.

RESULTS

The APV difference between WP and NP in the 105 volunteers was statistically significant (6.26±0.28 vs. 6.04±0.36, P=0.048), as well as the difference between WP and SP (6.26±0.28 vs. 6.07±0.46, P=0.049). However, no statistically significant difference was found between NP and SP (P=0.75). WP showed a similar CV (4.47%) to those of NP (5.96%) and SP (7.58%).

CONCLUSION

The new parameter APV could differentiate between NP or SP and WP. Accordingly, APV could be considered an useful parameter for the analysis of array pulse diagrams in Chinese medicine.

Diagnostic Accuracy of Chinese Medicine Diagnosis Scale of Phlegm and Blood Stasis Syndrome in Coronary Heart Disease: A Study Protocol

BACKGROUND

Phlegm and blood stasis syndrome (PBSS) is one of the main syndromes in coronary heart disease (CHD). Syndromes of Chinese medicine (CM) are lack of quantitative and easy-implementation diagnosis standards. To quantify and standardize the diagnosis of PBSS, scales are usually applied.

OBJECTIVE

To evaluate the diagnostic accuracy of CM diagnosis scale of PBSS in CHD.

METHODS

Six hundred patients with stable angina pectoris of CHD, 300 in case group and 300 in control group, will be recruited from 5 hospitals across China. Diagnosis from 2 experts will be considered as the "gold standard". The study design consists of 2 phases: pilot test is used to evaluate the reliability and validity, and diagnostic test is used to assess the diagnostic accuracy of the scale, including sensitivity, specificity, likelihood ratio and area under the receiver operator characteristic (ROC) curve.

DISCUSSION

This study will evaluate the diagnostic accuracy of CM diagnosis scale of PBSS in CHD. The consensus of 2 experts may not be ideal as a "gold standard", and itself still requires further study. (No. ChiCTR-OOC-15006599).

Detection of multipoint pulse waves and dynamic 3D pulse shape of the radial artery based on binocular vision theory

BACKGROUND AND OBJECTIVE

Pulse signals contain a wealth of human physiological and pathological information. How to get full pulse information is especially challenging, and most of the traditional pulse sensors can only get the pulse wave of a single point. This study is aimed at developing a binocular pulse detection system and method to obtain multipoint pulse waves and dynamic three-dimensional pulse shape of the radial artery.

METHODS

The proposed pulse detection approach is image-based and implemented by two steps. First, a new binocular pulse detection system is developed based on the principle of pulse feeling used in traditional Chinese medicine. Second, pulse detection is achieved based on theories and methods of binocular vision and digital image processing. In detail, the sequences of pulse images collected by the designed system as experimental data are sequentially processed by median filtering, block binarization and inversion, area filtering, centroids extraction of connected regions, to extract the pattern centroids as feature points. Then stereo matching is realized by a proposed algorithm based on Gong-shape scan detection. After multipoint spatial coordinate calculation, dynamic three-dimensional reconstruction of the thin film shape is completed by linear interpolation. And then the three-dimensional pulse shape is achieved by finding an appropriate reference time. Meanwhile, extraction of multipoint pulse waves of the radial artery is accomplished by using a suitable reference origin. The proposed method is analyzed from three aspects, which are pulse amplitude, pulse rate and pulse shape, and compared with other detection methods.

RESULTS

Analysis of the results shows that the values of pulse amplitude and pulse rate are consistent with the characteristics of pulse wave of the radial artery, and pulse shape can correctly present the shape of pulse in space and its change trend in time. The comparison results with the other two previously proposed methods further verify the correctness of the presented method.

CONCLUSIONS

The designed binocular pulse detection system and proposed algorithm can effectively detect pulse information. This tactile visualization-based pulse detection method has important scientific significance and broad application prospects and will promote further development of objective pulse diagnosis.

Association of hypertension with physical factors of wrist pulse waves using a computational approach: a pilot study

BACKGROUND

The objectives of this pilot study were to examine the association between hypertension and physical factors of wrist pulse waves to avoid subjective diagnoses in Traditional Chinese Medicine (TCM) and Traditional Korean Medicine (TKM). An additional objective was to assess the predictive power of individual and combined physical factors in order to identify the degree of agreement between diagnosis accuracies using physical factors and using a sphygmomanometer in the prediction of hypertension.

METHODS

In total, 393 women aged 46 to 73 years participated in this study. Logistic regression (LR) and a naïve Bayes algorithm (NB) were used to assess statistically significant differences and the predictive power of hypertension, and a wrapper-based machine learning method was used to evaluate the predictive power of combinations of physical factors.

RESULTS

In both wrists, L-PPI and R-PPI (maximum pulse amplitudes in the left Gwan and right Gwan) were the factors most strongly associated with hypertension after adjusting for age and body mass index (p = <0.001, odds ratio (OR) = 2.006 on the left and p = <0.001, OR = 2.504 on the right), and the best predictors (NB-AUC = 0.692, LR-AUC = 0.7 on the left and NB-AUC = 0.759, LR-AUC = 0.763 on the right). Analyses of both individual and combined physical factors revealed that the predictive power of the physical factors in the right wrist was higher than for the left wrist. The predictive powers of the combined physical factors were higher than those of the best single predictors in both the left and right wrists.

CONCLUSION

We suggested new physical factors related to the sum of the area on the particular region of pulse waves in both wrists. L-PPI and R-PPI among all variables used in this study were good indicators of hypertension. Our findings support the quantification and objectification of pulse patterns and disease in TCM and TKM for complementary and alternative medicine.

Neural network study for standardizing pulse-taking depth by the width of artery

To carry out a pulse diagnosis, a traditional Chinese medicine (TCM) physician presses the patient's wrist artery at three incremental depths, namely Fu (superficial), Zhong (medium), and Chen (deep). However, the definitions of the three depths are insufficiently clear for use with modern pulse diagnosis instruments (PDIs). In this paper, a quantitative method is proposed to express the pulse-taking depths based on the width of the artery (WA). Furthermore, an index, α, is developed for estimating WA for PDI application. The α value is obtained using an artificial neural network (ANN) model with contact pressure (CP) and sensor displacement (SD) as the inputs. The WA and SD data from an ultrasound instrument and CP and SD data from a PDI were analyzed. The results show that the mean prediction error and the standard deviation (STD) of the ANN model was 1.19% and 0.0467, respectively. Comparing the ANN model with the SD model by statistical method, it showed significant difference and the improvement in the mean prediction error and the STD was 71.62% and 29.78%, respectively. The α value can thus map WA with less individual variation than that of the values estimated directly using the SD model. Pulse signals at different depths thus can be acquired according to α value while using a PDI, providing TCM physicians with more reliable pulse information.

Multi-Gaussian fitting for pulse waveform using Weighted Least Squares and multi-criteria decision making method

Analysis of pulse waveform is a low cost, non-invasive method for obtaining vital information related to the conditions of the cardiovascular system. In recent years, different Pulse Decomposition Analysis (PDA) methods have been applied to disclose the pathological mechanisms of the pulse waveform. All these methods decompose single-period pulse waveform into a constant number (such as 3, 4 or 5) of individual waves. Furthermore, those methods do not pay much attention to the estimation error of the key points in the pulse waveform. The estimation of human vascular conditions depends on the key points' positions of pulse wave. In this paper, we propose a Multi-Gaussian (MG) model to fit real pulse waveforms using an adaptive number (4 or 5 in our study) of Gaussian waves. The unknown parameters in the MG model are estimated by the Weighted Least Squares (WLS) method and the optimized weight values corresponding to different sampling points are selected by using the Multi-Criteria Decision Making (MCDM) method. Performance of the MG model and the WLS method has been evaluated by fitting 150 real pulse waveforms of five different types. The resulting Normalized Root Mean Square Error (NRMSE) was less than 2.0% and the estimation accuracy for the key points was satisfactory, demonstrating that our proposed method is effective in compressing, synthesizing and analyzing pulse waveforms.