Pulse Signal Analysis Research Articles

An accurate semi-empirical model for PMT pulse signal analysis.

The energy information of pulse signals is significantly important for applications such as computed tomography (CT), positron emission tomography (PET), and research on defects in condensed matter. Time-over-threshold (TOT) and multi-voltage threshold (MVT) are commonly used digitization methods in sampling pulse signal. However, both approaches rely on a mathematical model of the pulse signal to derive energy information. This study proposes a semi-empirical mathematical model for pulse signals formation process in scintillation crystal-coupled photomultiplier tube(PMT) probes, by utilizing the CR-RC shaping method. This mathematical model accurately describes output of the PMT pulse signals. This study analyzes a substantial dataset of pulse signals, comparing the performance of the newly designed mathematical model with that of the double exponential function in terms of their ability to fit pulse signals. The results indicate that the mathematical model developed herein achieves an average R2 of 0.9255, significantly surpassing the 0.9155 of the double exponential function, thereby demonstrating its superior fitting efficacy.

Single-Electrode Triboelectric Nanogenerator Device Framework for Wrist Pulse Signal (Nadi) Analysis and Disease Detection

Single-Electrode Triboelectric Nanogenerator Device Framework for Wrist Pulse Signal (Nadi) Analysis and Disease Detection

A wearable device based on the ionic liquid decorated sponge-like ultraviolet-curable resin for recognizing human mental health conditions

Developing a wearable device that can reliably and continuously detect human pulse signals and assess heart rate variability (HRV) parameters is essential for discriminating healthy subjects from patients with cardiovascular diseases and mental health disorders. Here, by combining a template-assisted ultraviolet-curing method with a subsequent ionic liquid modification, we propose a sponge-like dielectric layer and adopt it to construct a wearable device for decoding human mental health conditions. Benefiting from the coexisting sponge-like structures and an ionic liquid, the proposed wearable device presents a robust capacitance response, and could continuously monitor human pulse signals as well as analyze inter-beat intervals (IBIs) and HRV parameters of volunteers under different emotional states. Lastly, IBIs distributions and heart rates extracted from the pulse signals of the volunteers with different emotional conditions are employed as input features to train a machine-learning model, which is performed to further demonstrate the possibility of recognizing human emotions via pulse signal analysis. In conclusion, by incorporating the proposed wearable device with a machine-learning model, it is expected to achieve the emotional recognition of the volunteers with a piece of the pulse signal, showing promising potential in future human-machine interaction personalized with medical services, communications, and entertainment.

A computationally efficient learning model to classify audio signal attributes

<p>The era of machine learning has opened up groundbreaking realities and opportunities in the field of medical diagnosis. However, it is also observed that faster and proper diagnosis of any diseases/medical conditions require proper analysis and classification of digital signal data. It indicates the proper identification of tumors in the brain. Brain magnetic resonance imaging (MRI) data has to be appropriately classified, and similarly, pulse signal analysis is required to evaluate the human heart operating condition. Several studies have used machine learning (ML) modeling to classify speech signals, but very few studies have explored the classification of audio signal attributes in the context of intelligent healthcare monitoring. The study thereby aims to introduce novel mathematical modeling to analyze and classify synthetic pulse audio signal attributes with cost-effective computation. The numerical modeling is composed of several functional blocks where deep neural network-based learning (DNNL) plays a crucial role during the training phase, and also it is further combined with a recurrent structure of long-short term memory (R-LSTM) feedback connections (FCs). The design approaches further experiment in a numerical computing environment in terms of accuracy and computational aspects. The classification outcome of the proposed approach shows that it attains approximately 85% accuracy, which is comparable to the baseline approaches and execution time.</p>

Pulse Signal Analysis Based on Deep Learning Network.

Pulse signal is one of the most important physiological features of human body, which is caused by the cyclical contraction and diastole. It has great research value and broad application prospect in the detection of physiological parameters, the development of medical equipment, and the study of cardiovascular diseases and pulse diagnosis objective. In recent years, with the development of the sensor, measuring and saving of pulse signal has become very convenient. Now the pulse signal feature analysis is a hotspot and difficulty in the signal processing field. Therefore, to realize pulse signal automatic analysis and recognition is vital significance in the aspects of the noninvasive diagnosis and remote monitoring, etc. In this article, we combined the pulse signal feature extraction in time and frequency domain and convolution neural network to analyze the pulse signal. Firstly, a theory of wavelet transform and the ensemble empirical mode decomposition (EEMD) which is gradually developed in recent years have been used to remove the noises in the pulse signal. Moreover, a method of feature point detection based on differential threshold method is proposed which realized the accurate positioning and extraction time-domain values. Finally, a deep learning method based on one-dimensional CNN has been utilized to make the classification of multiple pulse signals in the article. In conclusion, a deep learning method is proposed for the pulse signal classification combined with the feature extraction in time and frequency domain in this article.

A non-invasive harmonic analysis to assess risk of retinopathy in type 2 diabetes mellitus

AimsAssessing the hemodynamic changes of diabetic retinopathy (DR) using harmonic analysis of both non-invasively measured radial pulse and photoplethysmography (PPG) signals to propose a DR risk indicator. MethodsA total of 1879 patients with diabetes were followed on average of 3.5 years. The radial pulse and PPG signals were measured at the beginning of the trial. Kaplan-Meier curves of the DR risk indicator was analyzed. In addition, the correlation between the measurements of the radial pulse and PPG was evaluated. ResultsIn comparison of the patients' clinical characteristics, years of diabetes, systolic blood pressure, HbA1C, ACR, urinary albumin and fourth harmonic (C4) were higher in the DR group, and eGFR and third harmonic (C3) were lower. Patients in the high-DR risk group had a 1.8-fold higher risk of developing retinopathy than those in the low-risk group (log-rank test, p < 0.001). The correlation coefficient between radial pulse and PPG measurements for C3 and C4 were 0.727 and 0.628, respectively. ConclusionsThe harmonic analysis of radial pulse and PPG signals may be used to reflect the effect of DR in hemodynamics and the derived harmonic components may predict the risk of DR of patients with type 2 diabetes.

Automated Segmentation of the Systolic and Diastolic Phases in Wrist Pulse Signal Using Long Short-Term Memory Network.

Purpose Single-period segmentation is one of the important steps in time-domain analysis of pulse signals, which is the basis of time-domain feature extraction. The existing single-period segmentation methods have the disadvantages of generalization, reliability, and robustness. Method This paper proposed a period segmentation method of pulse signals based on long short-term memory (LSTM) network. The preprocessing was performed to remove noises and baseline drift of pulse signals. Thus, LabelMe was used to label each period of the pulse signals into two parts according to the location of the starting point of main wave and the dicrotic notch, and the dataset of the pulse signal period segmentation was established. Consequently, the labeled dataset was input into the LSTM for training and testing, and the results were compared with sum slope function method. Result The remarkable result with the whole period segmentation accuracy of 92.8% was achieved for the segmentation of seven types of pulse signals. And the segmentation accuracies of the systolic phase, diastolic phase, and whole period using this method were higher than those of the sum slope function method. Conclusion LSTM-based pulse signal segmentation method can achieve outstanding, robust, and reliable segmentation effects of the systolic phase, diastolic phase, and whole period of pulse signals. The research provides a new idea and method for the segmentation of pulse signals.

Pressure wrist pulse signal analysis by sparse decomposition using improved Gabor function

Background and objective: In traditional Chinese medicine and Ayurvedic medicine, wrist pulse wave fluctuations are an important indicator for distinguishing different health states. Owing to the development of modern sensing technology, computational methods have been used in the analysis of pulse wave signals. The description and quantification of the peaks in the pulse wave is significant for the identification of health status.Methods: In this study, we decomposed the pressure pulse waveform of the radial artery into several components by sparse decomposition with an improved Gabor function. To better represent the position, shape, and relationship of the peaks, we designed an improved Gabor function structure based on the characteristics of the pulse waveform to generate a time-frequency dictionary. Compared with conventional representation methods, the shape of the Gabor function is more variable. In addition, owing to the limitation of windowing, the Gabor function can reduce the influence on other positions when it represents a specific position. Feature vectors consisting of decomposed components can be used for computerized pulse signal analysis and disease diagnosis.Results: In the binary classification of healthy and diseased pulse signals, the proposed method achieved the best results for health/diabetes, health/cardiac disease, health/hypertension, and health/nephropathy with accuracies of 93.54%, 73.42%, 88.42%, and 82.28%, respectively. The multi-classification performance of the different types of features was evaluated by six classifiers, and the proposed method obtained the highest classification performance with support vector machine-radial basis function for both balanced and imbalanced data.Conclusions: The results indicated that the proposed method enabled to obtain a smaller representation error and exhibited superior performance in distinguishing between the signals collected from patients and healthy individuals. Moreover, for the multi-classification of the pulse signals, the proposed method performed better than the state-of-the-art methods.

Refined Multiscale Entropy Analysis of Wrist Pulse for Gender Difference in Traditional Chinese Medicine among Young Individuals.

Pulse signal analysis plays an important role in promoting the objectification of traditional Chinese medicine (TCM). Like electrocardiogram (ECG) signals, wrist pulse signals are mainly caused by cardiac activities and are valuable in analyzing cardiac diseases. A large number of studies have reported ECG signals can distinguish gender characteristics of normal healthy subjects using entropy complexity measures, consistently showing more complexity in females than males. No research up to date, however, has been found on examining gender differences with wrist pulse signals of healthy subjects on entropy complexity measures. This paper is aimed to fill in the research gap, which could, in turn, provide a deeper insight into the pulse signal and might identify potential differences between ECG signals and pulse signals. In particular, several complementary entropy measures with corresponding refined composite multiscale versions are established to perform the analysis for the filtered TCM pulse signals. Experimental results reveal that regardless of entropy measures used, there is no statistically significant gender difference in terms of entropy complexity, indicating that the pulse signal holds less gender characteristics than the ECG signal. In view of these findings, wrist pulse signals could be likely to provide different pieces of information to ECG signals. The present study is the first to quantitatively evaluate gender differences in healthy pulse signals with measures of entropy complexity and more importantly; we expect this study could make contribution to the ongoing pulse intelligent diagnosis and objective analysis, further facilitating the modernization of TCM pulse diagnosis.

NA-MEMD Is Actually What You Need for Computational Pulse Analysis

Time-frequency analysis is of necessity for wrist pulse signal due to its complexity, among which, empirical mode decomposition (EMD) algorithm and its improved noise-assisted versions (such as ensemble EMD, noise-assisted multivariate EMD (NA-MEMD) and very recently median EMD) are deemed to be the most representative ones. In this study, we provide an in-depth evaluation of these well-established noise-assisted EMD algorithms in computational pulse analysis for the first time. In particular, we compare the performance of the different algorithms systematically and quantitatively based on objective quantitative criteria: number and central frequency of intrinsic mode function (IMF) components, total orthogonality index and mode mixing. Rather than using synthetic signals with visual inspection in most existing literature, the wrist pulse signals used in the evaluation are real recorded samples acquired from both healthy and patient subjects. Through extensive experiments, we found that: 1) Advanced EMD algorithm that has the best performance in other areas may not be the most suitable method for pulse signal analysis, which indicates its high dependence on the type of signal to be analyzed; 2) Adding noise can significantly improve algorithm performance, but tends to produce physiologically not relevant components, which however are usually neglected throughout the intelligent pulse diagnosis literature. Therefore, excluding redundant components and then extracting features is expected to improve performance further. Together, currently NA-MEMD achieves a better performance consistently, potential to become a powerful tool for computational pulse analysis, which in itself have not been applied in wrist pulse analysis before. We believe our works can bring up a new perspective to application of EMD-like algorithms in computational pulse analysis/diagnosis with effective information and guidance. Additionally, considering the similarity between physiological signals, especially such as photoplethysmogram/electrocardiogram, our research can be extended to wearable health monitoring technologies, including smart watches and fitness trackers, and their potential future applications, such as in heart rate estimation and evaluate various cardiovascular-related diseases. The present study underscored the necessity of evaluation noise-assisted EMDs or other adaptive decomposition algorithms based on real recorded signals with more objective measures. Especially, caution the possible redundant components that are introduced.

Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study.

BackgroundIn pulse signal analysis and identification, time domain and time frequency domain analysis methods can obtain interpretable structured data and build classification models using traditional machine learning methods. Unstructured data, such as pulse signals, contain rich information about the state of the cardiovascular system, and local features of unstructured data can be extracted and classified using deep learning.ObjectiveThe objective of this paper was to comprehensively use machine learning and deep learning classification methods to fully exploit the information about pulse signals.MethodsStructured data were obtained by using time domain and time frequency domain analysis methods. A classification model was built using a support vector machine (SVM), a deep convolutional neural network (DCNN) kernel was used to extract local features of the unstructured data, and the stacking method was used to fuse the above classification results for decision making.ResultsThe highest average accuracy of 0.7914 was obtained using only a single classifier, while the average accuracy obtained using the ensemble learning approach was 0.8330.ConclusionsEnsemble learning can effectively use information from structured and unstructured data to improve classification accuracy through decision-level fusion. This study provides a new idea and method for pulse signal classification, which is of practical value for pulse diagnosis objectification.

Analysis of Wrist Pulse Signal: Emotions and Physical Pain

BackgroundPulse diagnosis (wrist pulse signal) is a well-known traditional technique used for a health examination. It has the potential to detect cardiac and non-cardiac diseases. ObjectiveA study was conducted to investigate human emotions using wrist pulse signal assessment. The aim was to categorize anxiety, boredom, physical pain, and reference state by processing and analysis of acquired signals. MethodA protocol was designed to induce emotions. Data were acquired from 24 healthy volunteers. Signals were processed and further analyzed using paired t-test and Analysis of Variance (ANOVA). Machine learning algorithms, Linear Discriminant Function (LDF), Quadratic Discriminant Function (QDF), and Support Vector Machine using kernel Gaussian radial basis function (RBF-SVM) were used to evaluate significant features and classify the emotions. ResultsComputing significant plus ranked features performed better over randomly selected features for pairwise emotion classification. Here, the QDF classifier outperforms LDF. Additionally, ANOVA validated the effectiveness of statistically prominent features to classify emotional states. Ratio_Pulse_Strength, total_power, Spectral Entropy, and meancd5 came out as the four most significant features to classify the emotion “Anxiety”, “Boredom”, “Pain”, and “Reference” with positive prediction rate of 100%, 73%, 100%, and 86% respectively using RBF-SVM in the user-independent model. ConclusionPreviously, WPS has been used mainly to detect physical abnormality in the human body. The results endorse the potential of user-independent human emotion detection using a wrist pulse signal. The present work was focusing on a few emotional states. Results are encouraging and may be well applied to many more states.

Measurement While Drilling Mud Pulse Signal Denoising and Extraction Approach Based on Particle-Swarm-Optimized Time-Varying Filtering Empirical Mode Decomposition

Summary At present, mud pulse transmission is widely used in underground wireless transmission. To extract more accurately the original drilling fluid pulse signals while drilling, in this paper, we developed an algorithm for optimal denoising shaping based on particle-swarm-optimized time-varying filtering empirical mode decomposition (TVFEMD). The performance of TVFEMD heavily depends on its parameters (i.e., B-spline order and bandwidth threshold). In the traditional TVFEMD method, the parameters are given in advance and may not be optimized, so it is difficult to achieve satisfactory decomposition results. To tackle this issue, the correlation coefficient was used as the objective function, and the particle-swarm-optimization algorithm was used to optimize the parameters of TVFEMD in this paper. First, the particle swarm optimization was used to search for the best combination of parameters. Then, the TVFEMD was applied to obtain a series of intrinsic mode functions (IMFs). Subsequently, the optimal denoising and shaping algorithm was used to determine the best reconstructed signal by low-pass filtering. Permutation entropy was taken as the evaluation index to obtain a reconstruction signal. Finally, the reconstructed signal was processed by square wave shaping to obtain accurate drilling fluid pulse signals. The approximation of the algorithm is 0.7581, and relevance is as high as 0.8535. The simulation signal and drilling fluid pulse signal analysis results showed that the proposed approach can extract the original pulse signal accurately.

Cyclic Point Processes with Limited Aftereffect for a Pulse Signal Analysis with Significant Pulse Rhythm Variability

In this paper, we present the results of applying the model of cyclic point processes with a limited aftereffect for the analysis of the rhythmic characteristics of pulsed signals. Being a generalization of recurrent and alternating point processes, we show that cyclic processes can describe event flows with more than two states. The latter circumstance significantly expands the scope of their application, in particular, to biomedical signals. Here, a complete (local) statistical description of the cyclic processes is derived, the asymptotics of their behavior are studied, and a simplified statistical description for the case of stationary modes is given. In the latter case, analytical expressions for the average and second mixed moments of the cyclic process are obtained based on the local statistics. In the most important particular case, the dependence of the features of their structure on the time scales of the signal dynamics and on the ratios between the scales was clarified.

Research on adaptive pulse signal extraction algorithm based on fingertip video image

In order to solve the saturation distortion phenomenon of R component in fingertip video image, this paper proposes an iterative threshold segmentation algorithm, which adaptively generates the region to be detected for the R component, and extracts the human pulse signal by calculating the gray mean value of the region to be detected. The original pulse signal has baseline drift and high frequency noise. Combining with the characteristics of pulse signal, a zero phase digital filter is designed to filter out noise interference. Fingertip video images are collected on different smartphones, and the region to be detected is extracted by the algorithm proposed in this paper. Considering that the fingertip's pressure will be different during each measurement, this paper makes a comparative analysis of pulse signals extracted under different pressures. In order to verify the accuracy of the algorithm proposed in this paper in heart rate detection, a comparative experiment of heart rate detection was conducted. The results show that the algorithm proposed in this paper can accurately extract human heart rate information and has certain portability, which provides certain theoretical help for further development of physiological monitoring application on smartphone platform.

Study on the Classification of Pulse Signal Based on the BP Neural Network

The objectification of the pulse signal analysis is a practical problem. The classification of the pulse signal is studied based on the BP neural network. It is first analyzed how to select the characteristic factors of the pulse signal. Then the method of nondimensionalization/normalization on the pulse signal is presented to preprocess the characteristic factors. The classification of the pulse signal and the effects of the selection of characteristic factors are studied by using the normalized data and BP neural network. It is shown that nondimensionalization/normalization of the data is in favor of the training and forecasting of the network. The selection of characteristic factors affects the accuracy of forecasting obviously. The results of forecasting by selection of 8, 6 and 4 factors respectively show that the less the factors are, the worse the effects are.

Evaluation of methods for determining abnormalities in cardiovascular system by pulse signal under psycho-emotional stress in dental practice

The purpose of this paper is to compare the existing methods of pulse signal analysis in order to select the best methods for detecting psycho-emotional stress in dental practice. The carried out analysis showed that analytical methods are the most promising for the creation of new and improvement of existing diagnostic equipment, as they contain clear algorithms and have high reproducibility of calculation results.

Features fusion of multichannel wrist pulse signal based on KL-MGDCCA and decision level combination

To sense the pulse in the representative positions of wrist is the basis of traditional Chinese pulse diagnosis. The pulse diagnosis has been obtaining more and more attentions for its non-invasive character and its convenience in analysis of health status. For objective analysis, various types of pulse features have been extracted from the pulse signal with the development of computerized pulse analysis. The effective utilization of the features of multichannel is the increasing and urgent need for the pulse analysis. A novel features fusion frame is proposed to reduce the redundant information for the homogeneous features, and eliminate the interference of heterogeneous features. For the same type of features extracted from the different channels, the proposed method uses Karhunen–Loeve multiple generalized discriminative canonical correlation analysis (KL-MGDCCA) to fuse them into one feature vector. A support vector machine (SVM) classifier is trained for each type of fused features. Then, the frame adopts decision level fusion approach to combine these classifiers for pulse signal classification to solve the problem of heterogeneous features fusion. Extensive experiments show that the proposed fusion frame can achieve the best performance on most indicators for multichannel pulse signal analysis. For the classification of Diabetes/Health, Nephropathy/Health and Diabetes/Nephropathy, the proposed method achieves the best F-score with the value of 75.25%, 79.02% and 56.54%, which outperforms state-of-the-art methods being compared.

Analysis of Pulse Signals Based on Array Pulse Volume.

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. 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. 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%). 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.

High-Precision Measurement of Sine and Pulse Reference Signals Using Software-Defined Radio

This paper addresses simultaneous, high-precision measurement and analysis of generic reference signals by using inexpensive commercial off-the-shelf Software Defined Radio hardware. Sine reference signals are digitally down-converted to baseband for the analysis of phase deviations. Hereby, we compare the precision of the fixed-point hardware Digital Signal Processing chain with a custom Single Instruction Multiple Data (SIMD) x86 floating-point implementation. Pulse reference signals are analyzed by a software trigger that precisely locates the time where the slope passes a certain threshold. The measurement system is implemented and verified using the Universal Software Radio Peripheral (USRP) N210 by Ettus Research LLC. Applying standard 10 MHz and 1 PPS reference signals for testing, a measurement precision (standard deviation) of 0.36 ps and 16.6 ps is obtained, respectively. In connection with standard PC hardware, the system allows long-term acquisition and storage of measurement data over several weeks. A comparison is given to the Dual Mixer Time Difference (DMTD) and Time Interval Counter (TIC), which are state-of-the-art measurement methods for sine and pulse signal analysis, respectively. Furthermore, we show that our proposed USRP-based approach outperforms measurements with a high-grade Digital Sampling Oscilloscope.