Pulse type classification by varying contact pressure

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.

An IoT based Ayurvedic approach for real time healthcare monitoring

<abstract> <p>Pulse diagnosis, also known as Nadi Pariksha, is one of the various diagnostic modalities used in Ayurveda. Nadi Pariksha is a way of determining the underlying cause of a sickness that needs extensive knowledge of the Tridosha signals (i.e. Vata, Pitta and Kapha), as well as the peculiarities of each pulse signal and their relationship to each dominant signal. A Nadi expert can gain a sense of the patient's health status by using this approach and then provide treatment based on that information. In the present day, the health monitoring of people has become an essential requirement. A system which keeps track of the patient's health and continuously captures pulse signals will be helpful. In this work a healthcare monitoring system that uses sensors was developed, and the analysis of Vata, Pitta and Kapha for various patients is discussed, as well as the uploading of the same data to a self-made IoT cloud. The mean values of Vata, Pita and Kapha were compared for different age groups; we found that it is more significant for the age group of 41‒50.</p> </abstract>

An Alignment-Free Sensing Module for Noninvasive Radial Artery Blood Pressure Measurement

Sensor–artery alignment has always been a significant problem in arterial tonometry devices and prevents their application to wearable continuous blood pressure (BP) monitoring. Traditional solutions are to use a complex servo system to search for the best measurement position or to use an inefficient pressure sensor array. In this study, a novel solid–liquid mixture pressure sensing module is proposed. A flexible film with unique liquid-filled structures greatly reduces the pulse measurement error caused by sensor misplacement. The ideal measuring location was defined as −2.5 to 2.5 mm from the center of the module and the pressure variation was within 5.4%, which is available in the real application. Even at a distance of ±4 mm from the module center, the pressure decays by 23.7%, and its dynamic waveform is maintained. In addition, the sensing module is also endowed with the capability of measuring the pulse wave transmit time as a complementary method for BP measuring. The capability of the developed alignment-free sensing module in BP measurement was been validated. Twenty subjects were selected for the BP measurement experiment, which followed IEEE standards. The experimental results showed that the mean error of SBP is −4.26 mmHg with a standard deviation of 7.0 mmHg, and the mean error of DBP is 2.98 mmHg with a standard deviation of 5.07 mmHg. The device is expected to provide a new solution for wearable continuous BP monitoring.

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.

Quantitative Comparison of the Performance of Piezoresistive, Piezoelectric, Acceleration, and Optical Pulse Wave Sensors

The accurate measurement of the arterial pulse wave is beneficial to clinical health assessment and is important for the effective diagnosis of many types of cardiovascular disease. A variety of sensors have been developed for the non-invasive detection of these waves, but the type of sensor has an impact on the measurement results. Therefore, it is necessary to compare and analyze the signals obtained under a range of conditions using various pulse sensors to aid in making an informed choice of the appropriate type. From the available types we have selected four: a piezoresistive strain gauge sensor (PESG) and a piezoelectric Millar tonometer (the former with the ability to measure contact force), a circular film acceleration sensor, and an optical reflection sensor. Pulse wave signals were recorded from the left radial, carotid, femoral, and digital arteries of 60 subjects using these four sensors. Their performance was evaluated by analyzing their susceptibilities to external factors (contact force, measuring site, and ambient light intensity) and by comparing their stability and reproducibility. Under medium contact force, the peak-to-peak amplitude of the signals was higher than that at high and low force levels and the variability of signal waveform was small. The optical sensor was susceptible to ambient light. Analysis of the intra-class correlation coefficients (ICCs) of the pulse wave parameters showed that the tonometer and accelerometer had good stability (ICC > 0.80), and the PESG and optical sensor had moderate stability (0.46 < ICC < 0.86). Intra-observer analysis showed that the tonometer and accelerometer had good reproducibility (ICC > 0.75) and the PESG and optical sensor had moderate reproducibility (0.42 < ICC < 0.91). Inter-observer analysis demonstrated that the accelerometer had good reproducibility (ICC > 0.85) and the three other sensors had moderate reproducibility (0.52 < ICC < 0.96). We conclude that the type of sensor and measurement site affect pulse wave characteristics and the careful selection of appropriate sensor and measurement site are required according to the research and clinical need. Moreover, the influence of external factors such as contact pressure and ambient light should be fully taken into account.

Estimation of wave reflection in aorta from radial pulse waveform by artificial neural network: a numerical study

BACKGROUND AND OBJECTIVE

Wave reflection in aorta has been shown to have incremental value for predicting cardiovascular events. However, its estimation by wave separation analysis (WSA) is complex.

METHODS

In this study, a novel method was proposed based on a cascade artificial neural network (ANN) for wave reflection estimation by the frequency features of radial pressure waveform alone. The simulation database of 4000 samples was generated by a 55-segment transmission line model of human arterial tree and was used for evaluating the ANN with 10-fold cross validation for the estimation of reflection magnitude (RM_ANN) and reflection index (RI_ANN) of wave reflection in aorta. RM and RI also were estimated by the WSA with a triangle waveform of aortic flow (RM_WSA and RI_WSA) and with a real aortic flow waveform (RM_Ref and RI_Ref) as reference values.

RESULTS

The results showed the correlation coefficient and mean difference between RM_ANN and RM_Ref (R² = 0.92, mean ± standard deviation (SD) = 0.0 ± 0.02) and those between RI_ANN and RI_Ref (R² = 0.91, mean ± SD = 0.0 ± 0.01) were better than those between RM_WSA and RM_Ref (R² = 0.51, mean ± SD = 0.01 ± 0.07) and those between RI_WSA and RI_Ref (R² = 0.50, mean ± SD = 0.0 ± 0.02). As the sample diversity in the simulation database was increased but the total number of samples keeps constant, the advantage of the ANN, though decreased slightly, became more significant than those of WSA (RM_ANN VS. RM_Ref and RI_ANN VS. RI_Ref: R² = 0.88 and 0.88, mean ± SD = 0.0 ± 0.05 and 0.0 ± 0.05; RM_WSA VS. RM_Ref and RI_WSA VS. RI_Ref: R² = 0.24 and 0.24, mean ± SD = 0.07 ± 0.24 and 0.02 ± 0.08, respectively). In addition, the ANN can achieve better results than the traditional method WSA even only two hidden neurons are used.

CONCLUSIONS

ANN is a potential method for the estimation of wave reflection in aorta by a single radial pulse waveform, but further validation of this method in clinic trials is needed.

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.

Estimation of aortic systolic blood pressure from radial systolic and diastolic blood pressures alone using artificial neural networks

BACKGROUND

Current aortic SBP estimation methods require recording of a peripheral pressure waveform, a step with no consensus on method. This study investigates the possibility of aortic SBP estimation from radial SBP and DBP using artificial neural networks (ANN) with [ANNSBP.DBP.heart rate (HR)] and without HR (ANNSBP.DBP).

METHODS

Ten-fold cross validation was applied to invasive, simultaneously recorded aortic and radial pressure during rest and nitroglycerin infusion (n = 62 patients). The results of the ANN models were compared with an ANN model using additional waveform features (ANNwaveform), to an N-point moving average method (NPMA) and to existing, validated generalized transfer function (GTF).

RESULTS

Estimated aortic SBP for all methods was on average less than 1 mmHg away from measured aortic SBP with the exception of NPMA (difference 2.0 ± 3.5 mmHg, P = 0.62). Variability of the difference was significantly greater in ANNSBP.DBP.HR and ANNSBP.DBP (both SD of ± 5.9 mmHg, P < 0.001 compared with GTF, ± 4.0 mmHg, P < 0.001). Inclusion of waveform features decreased the variability (ANNwaveform ± 3.9 mmHg, P = 0.264). Estimated aortic SBP in all models was correlated with measured SBP, with ANN models providing statistically similar results to the GTF method, only the NPMA being statistically different (P = 0.031).

CONCLUSION

These findings indicate that use of radial SBP, DBP, and HR alone can provide aortic SBP estimation comparable with the GTF, albeit with slightly greater variance. Pending noninvasive validation, the technique provides plausible aortic SBP estimation without waveform analysis.

A Novel Modular Tonometry-Based Device to Measure Pulse Pressure Waveforms in Radial Artery

The paper presents the development of a new device for measuring continuous pulse pressure waveforms (PPW) from the radial artery via applanation tonometry. The development focuses on improved accuracy, open and affordable design using off-the-shelf components, and greater user control in setting operational and calibration parameters to address user variability. The device design parameters are optimized through a tissue device interaction study based on a computational model. The design incorporates modular components and includes a sensor module for arterial flattening and pressure pick-up, a differential screw mechanism and a related algorithm for controlled stepwise motion and data collection during flattening, and a brace for wrist-flexion adjustment. Maximum pulse amplitude (PA) was used as an indicator of the optimum level of arterial flattening for recording the PPW. The PPW was observed to distort due to changes in parameters like gel-head placement, hold-down pressure (HDP), and wrist extension. The pressure waveforms collected using the device were validated using limited data against established products and showed good correlation within ±1.96 standard deviation of the mean difference in a Bland–Altman plot. This paper thus details the development of a simple and validated mechanical design to measure PPW using arterial tonometry.

Estimation of Pulse Transit Time From Radial Pressure Waveform Alone by Artificial Neural Network

OBJECTIVE

To validate the feasibility of the estimation of pulse transit time (PTT) by artificial neural network (ANN) from radial pressure waveform alone.

METHODS

A cascade ANN with ten-fold cross validation was applied to invasively and simultaneously recorded aortic and radial pressure waveforms during rest and nitroglycerin infusion () for the estimation of mean and beat-to-beat PTT. The results of the ANN models were compared to a multiple linear regression (LR) model when the features of radial arterial pressure waveform in time and frequency domains were used as the predictors of the models.

RESULTS

For the estimation of mean PTT and beat-to-beat PTT by ANN ( ), the correlation coefficient between the and the measured PTT () (mean: ; beat-to-beat: ) is higher than that between the PTT estimated by LR ( ) and (mean: ; beat-to-beat: ). The standard deviation (SD) of the difference between the and ( ; beat-to-beat: ) is significantly less than that between the and (; beat-to-beat: 10 ms), but no significant difference exists between their mean ( ). The lack of frequency features of radial pressure waveform caused obvious reduction in the correlation coefficient and SD of the difference between the and . The performance of the ANN was improved by increasing the sample number but not by increasing the neuron number.

CONCLUSION

ANN is a potential method of PTT estimation from a single pressure measurement at radial artery.

A feasibility study on age-related factors of wrist pulse using principal component analysis

Various analysis methods for examining wrist pulse characteristics are needed for accurate pulse diagnosis. In this feasibility study, principal component analysis (PCA) was performed to observe age-related factors of wrist pulse from various analysis parameters. Forty subjects in the age group of 20s and 40s were participated, and their wrist pulse signal and respiration signal were acquired with the pulse tonometric device. After pre-processing of the signals, twenty analysis parameters which have been regarded as values reflecting pulse characteristics were calculated and PCA was performed. As a results, we could reduce complex parameters to lower dimension and age-related factors of wrist pulse were observed by combining-new analysis parameter derived from PCA. These results demonstrate that PCA can be useful tool for analyzing wrist pulse signal.

Study on the Depth, Rate, Shape, and Strength of Pulse with Cardiovascular Simulator

Pulse diagnosis is important in oriental medicine. The purpose of this study is explaining the mechanisms of pulse with a cardiovascular simulator. The simulator is comprised of the pulse generating part, the vessel part, and the measurement part. The pulse generating part was composed of motor, slider-crank mechanism, and piston pump. The vessel part, which was composed with the aorta and a radial artery, was fabricated with silicon to implement pulse wave propagation. The pulse parameters, such as the depth, rate, shape, and strength, were simulated. With changing the mean pressure, the floating pulse and the sunken pulse were generated. The change of heart rate generated the slow pulse and the rapid pulse. The control of the superposition time of the reflected wave generated the string-like pulse and the slippery pulse. With changing the pulse pressure, the vacuous pulse and the replete pulse were generated. The generated pulses showed good agreements with the typical pulses.

A Distinguishing Arterial Pulse Waves Approach by Using Image Processing and Feature Extraction Technique

Traditional Chinese Medicine (TCM) is based on five main types of diagnoses methods consisting of inspection, auscultation, olfaction, inquiry, and palpation. The most important one is palpation also called pulse diagnosis which is to measure wrist artery pulse by doctor's fingers for detecting patient's health state. In this paper, it is carried out by using a specialized pulse measuring instrument to classify one's pulse type. The measured pulse waves (MPWs) were segmented into the arterial pulse wave curve (APWC) by image proposing method. The slopes and periods among four specific points on the APWC were taken to be the pulse features. Three algorithms are proposed in this paper, which could extract these features from the APWCs and compared their differences between each of them to the average feature matrix, individually. These results show that the method proposed in this study is superior and more accurate than the previous studies. The proposed method could significantly save doctors a large amount of time, increase accuracy and decrease data volume.

Variation of radial pulse wave contour influenced by contact pressure

In this paper, the radial pulse waveforms of the same subjects under various contact pressures were measured. Then, the feature points of the pulse wave contours were extracted and the physical parameters were calculated corresponding to different contact pressures. The various trends of parameters, including peripheral augmentation index, peripheral subendocardial viability ratio, and peripheral resistance, influenced by contact pressures were analyzed. By comparing the variation trend between different subject groups, it is notable that there exists a significant difference between the parameters of young healthy people and elder patients (P<0.01). Moreover, the peripheral augmentation index, SEVR and the peripheral resistance descend with increased contact pressure, because of the variation of pulse wave contour.

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.

A non-contact pulse automatic positioning measurement system for traditional Chinese medicine

This study is to construct a non-contact pulse automatic positioning measurement system for Traditional Chinese Medicine (TCM) using optical triangulation measurements. The system consists of a linear laser, a CMOS image sensor and image analysis software. The linear laser is projected on the pulse beat location on the wrists; the CMOS image sensor records the process and the software analyzes the images. The program mainly uses the optical centroid and fast Fourier transform (FFT) principles to calculate centroid changes (pulse amplitude changes) from the images taken by the CMOS image sensor. It returns the positions of cun, guan and chi pulses automatically in terms of the amplitudes and the signals are then transformed from the time domain (time-amplitude) into the frequency domain (frequency-amplitude) via FFT to obtain the waveforms and frequencies of the cun, guan and chi pulses. It successfully extracts the data from the TCM pulse reading and can be a medical aid system for TCM. Combining the advantages of optical measurement and computer automation, this system provides a non-contact, easy to operate, fast in detection and low-cost equipment design.

A study of new pulse auscultation system

This study presents a new type of pulse auscultation system, which uses a condenser microphone to measure pulse sound waves on the wrist, captures the microphone signal for filtering, amplifies the useful signal and outputs it to an oscilloscope in analog form for waveform display and storage and delivers it to a computer to perform a Fast Fourier Transform (FFT) and convert the pulse sound waveform into a heartbeat frequency. Furthermore, it also uses an audio signal amplifier to deliver the pulse sound by speaker. The study observed the principles of Traditional Chinese Medicine's pulsing techniques, where pulse signals at places called "cun", "guan" and "chi" of the left hand were measured during lifting (100 g), searching (125 g) and pressing (150 g) actions. Because the system collects the vibration sound caused by the pulse, the sensor itself is not affected by the applied pressure, unlike current pulse piezoelectric sensing instruments, therefore, under any kind of pulsing pressure, it displays pulse changes and waveforms with the same accuracy. We provide an acquired pulse and waveform signal suitable for Chinese Medicine practitioners' objective pulse diagnosis, thus providing a scientific basis for this Traditional Chinese Medicine practice. This study also presents a novel circuit design using an active filtering method. An operational amplifier with its differential features eliminates the interference from external signals, including the instant high-frequency noise. In addition, the system has the advantages of simple circuitry, cheap cost and high precision.

Shape-preserving preprocessing for human pulse signals based on adaptive parameter determination

The use of the human pulse signal for medical diagnosis is a mainstay in the practice of traditional Chinese medicine. Computer processing of this signal may be used to automate diagnostic procedures and to reveal sources of information in the waveform that have been used by both eastern and western physicians for more than two millennia. A new method for preprocessing of the human pulse signal significantly improves feature extraction and classification of the waveform. Baseline distortion is first removed using the dual-tree complex wavelet transform (DT-CWT) and cubic spline interpolation, then a novel filtering method removes the residual background noise. Filtering is implemented in two stages. In the initial pass, a majority of the noise is eliminated by an adaptive mean filter whose sliding window duration is selected automatically based on a chain code and the DT-CWT. In the second pass, residual high frequency noise is removed using the DT-CWT with a new threshold determination. Experimental results demonstrate effective removal of background disturbances with excellent preservation of pulse peak information essential for proper parametric representation and classification of the waveform.

New assessment model of pulse depth based on sensor displacement in pulse diagnostic devices

An accurate assessment of the pulse depth in pulse diagnosis is vital to determine the floating and sunken pulse qualities (PQs), which are two of the four most basic PQs. In this work, we proposed a novel model of assessing the pulse depth based on sensor displacement (SD) normal to the skin surface and compared this model with two previous models which assessed the pulse depth using contact pressure (CP). In contrast to conventional stepwise CP variation tonometry, we applied a continuously evolving tonometric mechanism at a constant velocity and defined the pulse depth index as the optimal SD where the largest pulse amplitude was observed. By calculating the pulse depth index for 18 volunteers, we showed that the pulse was deepest at Cheok (significance level: P < 0.01), while no significant difference was found between Chon and Gwan. In contrast, the two CP-based models estimated that the pulse was shallowest at Gwan (P < 0.05). For the repeated measures, the new SD-based model showed a smaller coefficient of variation (CV ≈ 7.6%) than the two CP-based models (CV ≈ 13.5% and 12.3%, resp.). The SD-based pulse depth assessment is not sensitive to the complex geometry around the palpation locations and temperature variation of contact sensors, which allows cost-effective sensor technology.

Pulse waveform analysis as a bridge between pulse examination in Chinese medicine and cardiology

Pulse examination was probably the earliest attempt to distinguish between health and illnesses. Starting at the pre-Hippocratic era, Chinese medicine practitioners developed techniques for pulse examination and defined pulse images based on their perceptions of pulse waveforms at the radial artery. Pulse images were described using basic variables (frequency, rhythm, wideness, length, deepness, and qualities) developed under philosophical trends such as Taoism and Confucianism. Recent advances in biomedical instrumentation applied to cardiology opened possibilities to research on pulse examination based on ancient Chinese medical theories: the pulse wave analysis. Although strongly influenced by philosophy, some characteristics used to describe a pulse image are interpretable as parameters obtained by pulse waveform analysis such as pulse wave velocity and augmentation index. Those clinical parameters reflect concepts unique to Chinese medicine - such as yinyang - while are based on wave reflection and resonance theories of fluids mechanics. Major limitations for integration of Chinese and Western pulse examination are related to quantitative description of pulse images and pattern differentiation based on pulse examination. Recent evidence suggests that wave reflection and resonance phenomena may bridge Chinese medicine and cardiology to provide a more evidence-based medical practice.

Using the spring constant method to analyze arterial elasticity in type 2 diabetic patients

BACKGROUND

This study tests the validity of a newly-proposed spring constant method to analyze arterial elasticity in type 2 diabetic patients.

METHODS

The experimental group comprised 66 participants (36 men and 30 women) ranging between 46 and 86 years of age, all with diabetes mellitus. In the experimental group, 21 participants suffered from atherosclerosis. All were subjected to the measurements of both the carotid-femoral pulse wave velocity (cfPWV) and the spring constant method. The comparison (control) group comprised 66 normal participants (37 men and 29 women) with an age range of 40 to 80 years who did not have diabetes mellitus. All control group members were subjected to measurement by the spring constant method.

RESULTS

Statistical analysis of the experimental and control groups indicated a significant negative correlation between the spring constant and the cfPWV (P < .001; r = - 0.824 and - 0.71). Multivariate analysis similarly indicated a close relationship. The Student's t test was used to examine the difference in the spring constant parameter between the experimental and control groups. A P-value less than .05 confirmed that the difference between the 2 groups was statistically significant. In receiver operating characteristic curve (ROC), the Area Under Curve (AUC, = 0.85) indicates good discrimination. These findings imply that the spring constant method can effectively identify normal versus abnormal characteristics of elasticity in normal and diabetic participants.

CONCLUSIONS

This study verifies the use of the spring constant method to assess arterial elasticity, and found it to be efficient and simple to use. The spring constant method should prove useful not only for improving clinical diagnoses, but also for screening diabetic patients who display early evidence of vascular disease.

Temporal and spatial properties of arterial pulsation measurement using pressure sensor array

Conventionally, a pulse taking platform is based on a single sensor, which initiates a feasible method of quantitative pulse diagnosis. The aim of this paper is to implement a pulse taking platform with a tactile array sensor. Three-dimensional wrist pulse signals are constructed, and the length, width, ascending slope, and descending slope are defined following the surface of the wrist pulse. And the pressure waveform of the wrist pulse obtained through proposed pulse-taking platform has the same performance as the single sensor. Finally, the results of a paired samples t-test reveal that the repeatability of the proposal platform is consistent with clinical experience. On the other hand, the results of ANOVA indicate that differences exist among different pulse taking depths, and this result is consistent with clinical experience in traditional Chinese medicine pulse diagnosis (TCMPD). Hence, the proposed pulse taking platform with an array sensor is feasible for quantification in TCMPD.

A quantitative system for pulse diagnosis in Traditional Chinese Medicine

The pulse diagnosis is one of the most important examinations in Traditional Chinese Medicine (TCM). Due to the subjectivity and fuzziness of pulse diagnosis in TCM, quantitative systems or methods are needed to modernize pulse diagnosis. But up to now, the effective models that can classify pulse types according to pulse waves automatically have not been reported, which undoubtedly limits the practical applications of pulse diagnosis in clinical medicines. In this article, a novel quantitative system for pulse diagnosis was constructed based on Bayesian networks (BNs) to build the mapping relationships between pulse waves and pulse types. The results show that the system obtains relative reliable predictions of pulse types, and its predictive accuracy rate reach 84%, which testifies that the model used in our system is feasible and effective and can be expected to facilitate popular applications of TCM.

Pulse image recognition using fuzzy neural network

The automatic recognition of pulse images is the key in the research of computerized pulse diagnosis. In order to automatically differentiate the pulse patterns by using small samples, a fuzzy neural network to classify pulse images based on the knowledge of experts in traditional Chinese pulse diagnosis was designed. The designed classifier can make hard decision and soft decision for identifying 18 patterns of pulse images at the accuracy of 91%, which is better than the results that achieved by back-propagation neural network.

Nadi Tarangini: a pulse based diagnostic system

Ayurveda is a traditional medicine and natural healing system in India. Nadi-Nidan (pulse-based diagnosis) is a prominent method in Ayurveda, and is known to dictate all the salient features of a human body. In this paper, we provide details of our procedure for obtaining the complete spectrum of the nadi pulses as a time series. The system Nadi Tarangini1 contains a diaphragm element equipped with strain gauge, a transmitter cum amplifier, and a digitizer for quantifying analog signal. The system acquires the data with 16-bit accuracy with practically no external electronic or interfering noise. Prior systems for obtaining the nadi pulses have been few and far between, when compared to systems such as ECG. The waveforms obtained with our system have been compared with these other similar equipment developed earlier, and is shown to contain more details. The pulse waveform is also shown to have the desirable variations with respect to age of patients, and the pressure applied at the sensing element. The system is being evaluated by Ayurvedic practitioners as a computer-aided diagnostic tool.

A novel noninvasive measurement technique for analyzing the pressure pulse waveform of the radial artery

Previous noninvasive measurements of the pulse waveform of the radial artery have not employed standard positioning procedures. Here, we propose a new noninvasive measuring apparatus that has a two-axis mechanism and employs a standard positioning procedure for detecting the optimal site for accurately measuring the pressure pulse waveform (PPW). A modified sensor was designed to simultaneously measure the arterial diameter changed waveform (ADCW) and PPW. Considering the artery as a cylinder, the measured waveform would be distorted if the sensor were not at the middle of arterial width. Moreover, a blood vessel is elastic, and its compliance changes with the transmural pressure, being maximal when the transmural pressure is equal to zero. The sensor should detect the PPW with the lowest possible distortion and, hence, an analysis of the vascular geometry and an arterial model were used to design a standard positioning procedure based on the ADCW for the X and Z axes. In order to verify the resolution of the X axis scanning procedure, the echo method was used to measure the radial artery outer diameter in ten healthy subjects. The difference between the scanning width and the actual arterial diameter was 0.36 +/- 0.23 mm (mean +/- SD). Finally, the PPW as measured at the optimal position was used to diagnose myocardial ischemia symptoms in 60 elderly subjects whose chief complaint was chest pain, with the exercise electrocardiogram being used as a reference to compare between individuals with and without myocardial ischemia. The PPW analysis used the harmonic components in the frequency domain. We found that the fourth harmonic of the Fourier series differed significantly between the groups (p = 0.0039), which is consistent with previous studies. The results indicate that our noninvasive measurement apparatus is very suitable for analyzing the PPW of the radial artery.

Baseline wander correction in pulse waveforms using wavelet-based cascaded adaptive filter

Pulse diagnosis is a convenient, inexpensive, painless, and non-invasive diagnosis method. Quantifying pulse diagnosis is to acquire and record pulse waveforms by a set of sensor firstly, and then analyze these pulse waveforms. However, respiration and artifact motion during pulse waveform acquisition can introduce baseline wander. It is necessary, therefore, to remove the pulse waveform's baseline wander in order to perform accurate pulse waveform analysis. This paper presents a wavelet-based cascaded adaptive filter (CAF) to remove the baseline wander of pulse waveform. To evaluate the level of baseline wander, we introduce a criterion: energy ratio (ER) of pulse waveform to its baseline wander. If the ER is more than a given threshold, the baseline wander can be removed only by cubic spline estimation; otherwise it must be filtered by, in sequence, discrete Meyer wavelet filter and the cubic spline estimation. Compared with traditional methods such as cubic spline estimation, morphology filter and Linear-phase finite impulse response (FIR) least-squares-error digital filter, the experimental results on 50 simulated and 500 real pulse signals demonstrate the power of CAF filter both in removing baseline wander and in preserving the diagnostic information of pulse waveforms. This CAF filter also can be used to remove the baseline wander of other physiological signals, such as ECG and so on.

Lempel-ziv decomposition based arrhythmic pulses recognition

The computerization of traditional Chinese pulse diagnosis (TCPD) is relatively new in the field of automatic physiological signal analysis and diagnosis. The classification of pulse patterns according to their positions and shapes have been intensively investigated, but until now no research in identifying pulses by their rhythms has been found. This paper introduces a method to distinguish rhythmic and arrhythmic pulse patterns and further, applies the Lempel-Ziv decomposition to classify arrhythmic pulses. In the experiment on 140 clinic pulses, our approach was able to classify pulses by their rhythms with an accuracy of 90.7%.

Wavelet-Based Cascaded Adaptive Filter for Removing Baseline Drift in Pulse Waveforms

This paper presents an energy ratio-based method and a wavelet-based cascaded adaptive filter (CAF) for detecting and removing baseline drift from pulse waveforms. Experiments on 50 simulated and five hundred real pulse signals demonstrate that this CAF outperforms traditional filters both in removing baseline drift and in preserving the diagnostic information of pulse waveforms.