Wrist Pulse Diagnosis Research Articles

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

Background and objectiveAs 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. MethodsThe 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). ResultsCompared 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. ConclusionAbnormal 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.

Graph Based Multichannel Feature Fusion for Wrist Pulse Diagnosis.

It is well known in Traditional Chinese Medicine (TCM) that a person's wrist pulse signal can reflect their health condition. Recently, many computerized wrist pulse AI systems have been proposed to simulate a practitioner's three fingers in order to acquire the wrist pulse signals (three positions/channels) from a candidate's wrist dynamically, before evaluating their health status based on the various feature extraction and detection methods. However, few works have investigated the correlation of the extracted features from the three wrist channels and comprehensively fused the various features together, which can improve the performance of wrist pulse diagnosis. In this paper, we propose a graph based multichannel feature fusion (GBMFF) method to utilize the multichannel features of the wrist pulse signals effectively. In detail, two different sensors, i.e., pressure and photoelectricity are used to capture the three channels of the wrist pulse signals. These are used to generate two different features by applying the stacked sparse autoencoder and wavelet scattering. Each feature of one wrist pulse sample is regarded as a node associated with its corresponding feature vector, and used to construct a graph for one candidate. A novel algorithm is implemented to construct different graphs for different candidates, which are used for wrist pulse diagnosis by developing graph convolutional networks. Experimental results indicate that our proposed AI-based method can obtain superior performances compared to other state-of-the-art approaches.

Validation of Noninvasive MOEMS-Assisted Measurement System Based on CCD Sensor for Radial Pulse Analysis

Examination of wrist radial pulse is a noninvasive diagnostic method, which occupies a very important position in Traditional Chinese Medicine. It is based on manual palpation and therefore relies largely on the practitioner′s subjective technical skills and judgment. Consequently, it lacks reliability and consistency, which limits practical applications in clinical medicine. Thus, quantifiable characterization of the wrist pulse diagnosis method is a prerequisite for its further development and widespread use. This paper reports application of a noninvasive CCD sensor-based hybrid measurement system for radial pulse signal analysis. First, artery wall deformations caused by the blood flow are calibrated with a laser triangulation displacement sensor, following by the measurement of the deformations with projection moiré method. Different input pressures and fluids of various viscosities are used in the assembled artificial blood flow system in order to test the performance of laser triangulation technique with detection sensitivity enhancement through microfabricated retroreflective optical element placed on a synthetic vascular graft. Subsequently, the applicability of double-exposure whole-field projection moiré technique for registration of blood flow pulses is considered: a computational model and representative example are provided, followed by in vitro experiment performed on a vascular graft with artificial skin atop, which validates the suitability of the technique for characterization of skin surface deformations caused by the radial pulsation.

NONINVASIVE POWER SPECTRUM ANALYSIS OF RADIAL PRESSURE WAVEFORM FOR ASSESSMENT OF VASCULAR SYSTEM

Wrist pulse diagnosis has been used in traditional Chinese medicine (TCM) for thousands of years, because pulse pressure signal contains a large number of physiological and pathological information of people. In this research, a systematic approach was proposed to analyze the computerized radial pressure waveform, with the focus placed on the power spectrum. We gained the power spectrum by using a modified fast Fourier transform, and the power-spectral characteristics were analyzed and compared. The analyzing program calculated the first peak frequency (F1) and the second peak (F2) automatically, and gained the time of phase shift between two frequencies. They could provide a simple noninvasive means for studying changes in the elastic properties of the vascular system depending on the age and the disease. Namely, the frequency analysis of radial pressure waveform gives new insight into the dynamics of cardiovascular system.

Wrist pulse signal diagnosis using modified Gaussian models and Fuzzy C-Means classification

Wrist pulse signal contains important information about the health status of a person and pulse signal diagnosis has been employed in oriental medicine for thousands of years. In this research, a systematic approach is proposed to analyze the computerized wrist pulse signals, with the focus placed on the feature extraction and pattern classification. The wrist pulse signals are first collected and pre-processed. Considering that a typical pulse signal is composed of periodically systolic and diastolic waves, a modified Gaussian model is adopted to fit the pulse signal and the modeling parameters are then taken as features. Consequently, a feature selection scheme is proposed to eliminate the tightly correlated features and select the disease-sensitive ones. Finally, the selected features are fed to a Fuzzy C-Means (FCM) classifier for pattern classification. The proposed approach is tested on a dataset which includes pulse signals from 100 healthy persons and 88 patients. The results demonstrate the effectiveness of the proposed approach in computerized wrist pulse diagnosis.