[Real-time Detection Method for Motion Artifact of Photoplethysmography Signals Based on Decision Trees].

PPG (photoplethysmography) holds significant application value in wearable and intelligent health devices. However, during the acquisition process, PPG signals can generate motion artifacts due to inevitable coupling motion, which diminishes signal quality. In response to the challenge of real-time detection of motion artifacts in PPG signals, this study analyzed the generation and significant features of PPG signal interference.

Malignant arrhythmias detection using a synthesis-by-analysis modeling method of arterial blood pressure signal.

Extreme bradycardia, extreme tachycardia, ventricular flutter fib, and ventricular tachycardia are four malignant arrhythmias (MAs) that lead to sudden cardiac death. It is very important to detect them in daily life. The arterial blood pressure (ABP) signal contains abundant pathological information about four MAs and is easy to be recorded under domestic conditions.

Research on heart rate extraction method based on mobile phone video.

As an important indicator of human health, heart rate is related to the diagnosis of cardiovascular diseases. In recent years, extracting the heart rate from the mobile phone image has become a research hotspot. However, the illumination intensity of the background, frame rate of the video, and resolution of the image influence heart rate detection accuracy.

A fast permutation entropy for pulse rate variability online analysis with one-sample recursion.

Pulse rate variability (PRV) signals are extracted from pulsation signal can be effectively used for cardiovascular disease monitoring in wearable devices. Permutation entropy (PE) algorithm is an effective index for the analysis of PRV signals. However, PE is computationally intensive and impractical for online PRV processing on wearable devices.

A fast sample entropy for pulse rate variability analysis.

Sample entropy is an effective nonlinear index for analyzing pulse rate variability (PRV) signal, but it has problems with a large amount of calculation and time consumption. Therefore, this study proposes a fast sample entropy calculation method to analyze the PRV signal according to the microprocessor process of data updating and the principle of sample entropy. The simulated data and PRV signal are employed as experimental data to verify the accuracy and time consumption of the proposed method.

Corrigendum on: A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree.

[This corrects the article DOI: 10.3389/fphys.2022.

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree.

Extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT), and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmia recognition is proposed based on pulse rate variability (PRV). First, noise and interference are wiped out from the arterial blood pressure (ABP), and the PRV signal is extracted.

A recognition method for extreme bradycardia by arterial blood pressure signal modeling with curve fitting.

Objective: The aim of this study is to investigate the potential of arterial blood pressure (ABP) signal for the detection of the subjects with life-threatening extreme bradycardia (EBr).

Approach: The steps of the proposed method include ABP signal preprocessing, ABP wave segmentation, model parameter estimation, and EBr subject detection. First, the noise, interference and abnormal segments are eliminated in the pre-processing.

[Study on a quantitative analysis method for pulse signal by modelling its waveform in time and space domain].

In order to quantitatively analyze the morphology and period of pulse signals, a time-space analytical modeling and quantitative analysis method for pulse signals were proposed. Firstly, according to the production mechanism of the pulse signal, the pulse space-time analytical model was built after integrating the period and baseline of pulse signal into the analytical model, and the model mathematical expression and its 12 parameters were obtained for pulse wave quantification. Then, the model parameters estimation process based on the actual pulse signal was presented, and the optimization method, constraints and boundary conditions in parameter estimation were given.

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.

A Real-Time Analysis Method for Pulse Rate Variability Based on Improved Basic Scale Entropy.

Base scale entropy analysis (BSEA) is a nonlinear method to analyze heart rate variability (HRV) signal. However, the time consumption of BSEA is too long, and it is unknown whether the BSEA is suitable for analyzing pulse rate variability (PRV) signal. Therefore, we proposed a method named sliding window iterative base scale entropy analysis (SWIBSEA) by combining BSEA and sliding window iterative theory.

[Research on PPG Signal Reconstruction Based on Compressed Sensing].

In order to improve the storage and transmission efficiency of dynamic photoplethysmography (PPG) signals in the detection process and reduce the redundancy of signals, the modified adaptive matching pursuit (MAMP) algorithm was proposed according to the sparsity of the PPG signal. The proposed algorithm which is based on reconstruction method of sparse adaptive matching pursuit (SAMP), could improve the accuracy of the sparsity estimation of signals by using both variable step size and the double threshold conditions. After experiments on the simulated and the actual PPG signals, the results show that the modified algorithm could estimate the sparsity of signals accurately and quickly, and had good anti-noise performance.

[Dynamic Pulse Signal Processing and Analyzing in Mobile System].

In order to derive dynamic pulse rate variability (DPRV) signal from dynamic pulse signal in real time, a method for extracting DPRV signal was proposed and a portable mobile monitoring system was designed. The system consists of a front end for collecting and wireless sending pulse signal and a mobile terminal. The proposed method is employed to extract DPRV from dynamic pulse signal in mobile terminal, and the DPRV signal is analyzed both in the time domain and the frequency domain and also with non-linear method in real time.

[Interference Detection and Signal Quality Assessment of Pulse Signals].

Pulse signal contains a wealth of biological and pathological information. However, it is susceptible to the influence of various factors which results in poor signal quality, and causes the device to generate false alarms. First the pulse signals are processing into discrete symbols, and then compare the test signal with the pulse template by using Dynamic Time Warping (DTW) to get the threshold for which can be used to find the interference segment of the test signal.

[Pulse Signal Quality Estimation and Filtering Based on Cyclostationary Algorithm].

In order to reduce the impact of various noise in pulse signal, the quality estimation and filtering algorithms based on cyclostationarity are proposed to reprocess pulse signal. First, A quality evaluation index of pulse signal which named quality factor is defined by cyclic spectrum to describe the quality variation of the pulse signal affected by noise; Second, a cyclic correlation matched filter (CCMF) is designed to remove noise. The simulation of pulse signal is produced by ourselves and noise signal is provided by MIT-BIH physiological database are used to test the function of proposed method, and then the method is applied to the actual pulse signal.

[Dynamic pulse signal acquisition and processing].

In order to obtain and process pulse signal in real-time, the integer coefficients notch, low-pass filters and an envelope filtering method were designed in consideration of the characteristics of disturbances in pulse signal and then were verified by MATLAB. The pulse signal was processed on DSP in time domain and frequency domain after simplifying the programming. The pulse wave height and pulse rate were calculated in real-time, and the pulse signal's spectrum was illustrated by FFT.