PPG Signal Processing Research Articles

Heart Pulse Transmission Parameters of Multi-Channel PPG Signals for Cuffless Estimation of Arterial Blood Pressure: Preliminary Study

The paper describes a method developed for the indirect cuffless estimation of arterial blood pressure (ABP) from two/three-channel photoplethysmography (PPG) signals. It is important when the actual ABPs cannot be measured, e.g., during scanning inside a magnetic resonance imager. The proposed procedure uses heart pulse transmission parameters (HPTPs) extracted from the second derivative PPG signals. The linear regression method was used to calculate the relation between the determined HPTPs and the ABPs measured in parallel by a blood pressure monitor. The ABP values were estimated by the inverse conversion characteristic calculated from these linear relations. Three auxiliary investigations were performed first to find appropriate settings for PPG signal processing. We tested the accuracy of ABP estimation using two small corpora of multi-channel PPG records sensed during our previous experiments. We also analyzed the distribution of the determined HPTP values depending on the hand and gender for the mapping of a mutual relationship of HPTPs and measured ABPs. The final estimation errors were evaluated graphically (by correlation scatter plots and Bland–Altman plots) and numerically (by a correlation coefficient between the measured and estimated ABPs and by enumeration of the relative estimation error). The obtained results achieve acceptable mean values of −2.6/−3.5 mm Hg for systolic/diastolic ABPs.

Implementation of Scrum in the manufacture of non-invasive blood sugar detection devices using PPG signals

This study presents the effective integration of Scrum methodology in the production process of non-invasive blood sugar testing devices using Photoplethysmography (PPG) signals. During three months, a team consisting of a Product Owner, Scrum Master, and Developer Team successfully utilized Scrum's agile structure to manage the challenges of PPG signal processing, hardware integration, and software development. The repeated sprint cycles enabled swift adjustment to new obstacles and stakeholder input, guaranteeing both effectiveness and agility in the development process. The dynamic approach facilitated both the punctual delivery of complex medical equipment and the cultivation of a culture focused on ongoing enhancement, establishing a model for the future use of agile approaches in healthcare technology. The successful implementation highlights the effectiveness of Scrum in managing the complexities of medical device development. It provides a model for improving non-invasive blood sugar detection devices and establishes agile methodologies as a key driver of innovation in healthcare technology.

Combining Local PWV and Quantified Arterial Changes for Calibration-Free Cuffless Blood Pressure Estimation: A Clinical Validation

This study proposes a novel calibration-free cuffless blood pressure monitor (BPM). Unlike the cuffless BPM that requires timing correction with the traditional BPM, the calibration-free cuffless BPM can directly estimate the arterial blood pressure (BP) based on the hemodynamics mathematical model. Our proposed device integrates a pair of piezoelectric ceramics as pressure sensors to sense the pressure wave in the radial artery. It calculates the local pulse wave velocity (PWV) with our waveform detection algorithm of our proposed one. A photoplethysmogram (PPG) probe is set between the pair of pressure wave sensors. This PPG probe is used to monitor the radial artery’s PPG intensity ratio (PIR). We design PPG signal processing algorithms to quantify the PIR. We recruited 129 participants for the BP monitoring experiment. Compared with the reference sphygmomanometer, the error mean ± standard deviations (STDs) systolic BP (SBP) was 2.1 ± 3.4, and the correlation r-value was 0.97. The diastolic BP (DBP) was 0.8 ± 4.2, the correlation r-value was 0.90, and p < 0.05 is taken as statistically significant. A new type of wearable continuous calibration-free BPM can replace the situation that requires the use of traditional ambulatory BPM and reduce patient discomfort. Our proposed BP measurement passed all ANSI/AAMI/ISO 81060-2:20181_5.2.4.1.2 data analysis criterion 1 and 2 standard requirements.

Implementation of Digital Filters for Real-Time PPG Signal Processing in VLC

Health care units depend on Wi-Fi or local area networks for biomedical data communication within their premises. The major hazard associated with biomedical data communication in healthcare units is radio frequency interference with medical devices. Visible light communication (VLC) is a developing technology that solves the issue of RF interference in healthcare units. Long-range communication is a great challenge in VLC as the transmitted data get corrupted by noise from ambient light and degrade with distance. Hence it is necessary to upgrade the signal condition and eradicate the noise due to ambient light interference. The proposed work addresses these issues and enables robust communication in healthcare units. The experimental validation of the proposed work has been executed by analyzing the VLC communication performance up to a distance of 5[Formula: see text]m. The proposed work focuses on implementation of digital filters like finite impulse response, infinite impulse response and least mean square adaptive filter for PPG signal processing. The least mean square filter showed a superior performance with a mean square error of 7.23e[Formula: see text] than the other filters, leading to an accurate diagnosis for patients without any RF interference inside healthcare units.

Principal component analysis of photoplethysmography signals for improved gesture recognition.

In recent years, researchers have begun to introduce photoplethysmography (PPG) signal into the field of gesture recognition to achieve human-computer interaction on wearable device. Unlike the signals used for traditional neural interface such as electromyography (EMG) and electroencephalograph (EEG), PPG signals are readily available in current commercial wearable devices, which makes it possible to realize practical gesture-based human-computer interaction applications. In the process of gesture execution, the signal collected by PPG sensor usually contains a lot of noise irrelevant to gesture pattern and not conducive to gesture recognition. Toward improving gesture recognition performance based on PPG signals, the main contribution of this study is that it explores the feasibility of using principal component analysis (PCA) decomposition algorithm to separate gesture pattern-related signals from noise, and then proposes a PPG signal processing scheme based on normalization and reconstruction of principal components. For 14 wrist and finger-related gestures, PPG data of three wavelengths of light (green, red, and infrared) are collected from 14 subjects in four motion states (sitting, walking, jogging, and running). The gesture recognition is carried out with Support Vector Machine (SVM) classifier and K-Nearest Neighbor (KNN) classifier. The experimental results verify that PCA decomposition can effectively separate gesture-pattern-related signals from irrelevant noise, and the proposed PCA-based PPG processing scheme can improve the average accuracies of gesture recognition by 2.35∼9.19%. In particular, the improvement is found to be more evident for finger-related (improved by 6.25∼12.13%) than wrist-related gestures (improved by 1.93∼5.25%). This study provides a novel idea for implementing high-precision PPG gesture recognition technology.

Photoplethysmogram Analysis and Applications: An Integrative Review.

Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

The Application of Deep Learning Algorithms for PPG Signal Processing and Classification

Photoplethysmography (PPG) is widely used in wearable devices due to its conveniency and cost-effective nature. From this signal, several biomarkers can be collected, such as heart and respiration rate. For the usual acquisition scenarios, PPG is an artefact-ridden signal, which mandates the need for the designated classification algorithms to be able to reduce the noise component effect on the classification. Within the selected classification algorithm, the hyperparameters’ adjustment is of utmost importance. This study aimed to develop a deep learning model for robust PPG wave detection, which includes finding each beat’s temporal limits, from which the peak can be determined. A study database consisting of 1100 records was created from experimental PPG measurements performed in 47 participants. Different deep learning models were implemented to classify the PPG: Long Short-Term Memory (LSTM), Bidirectional LSTM, and Convolutional Neural Network (CNN). The Bidirectional LSTM and the CNN-LSTM were investigated, using the PPG Synchrosqueezed Fourier Transform (SSFT) as the models’ input. Accuracy, precision, recall, and F1-score were evaluated for all models. The CNN-LSTM algorithm, with an SSFT input, was the best performing model with accuracy, precision, and recall of 0.894, 0.923, and 0.914, respectively. This model has shown to be competent in PPG detection and delineation tasks, under noise-corrupted signals, which justifies the use of this innovative approach.

Filtering-induced time shifts in photoplethysmography pulse features measured at different body sites: the importance of filter definition and standardization

Objective. The waveform of a photoplethysmography (PPG) signal depends on the measurement site and individual physiological conditions. Filtering can distort the morphology of the original PPG signal waveform and change the timing of pulse feature points on PPG signals. We aim to quantitatively investigate the effect of PPG signal morphology (related to measurement site) and type of pulse feature on the filtering-induced time shift (TS). Approach. 60 s PPG signals were measured from six body sites (finger, wrist under (volar), wrist upper (dorsal), earlobe, and forehead) of 36 healthy adults. Using infinite impulse response digital filters which are common in PPG signal processing, PPG signals were prefiltered (band-pass, pass and stop bands: >0.5 Hz and <0.2 Hz for high-pass filter, <20 Hz and >30 Hz for low-pass filter) and then filtered (low-pass, pass and stop bands: <3 Hz and >5 Hz). Four pulse feature points were defined and extracted (peak, valley, maximal first derivative, and maximal second derivative). For each subject, overall TS and intra-subject TS variability in feature points were calculated as the mean and standard deviation of TS between prefiltered and filtered PPG signals in 50 cardiac cycles. Statistical testing was performed to investigate the effect of measurement site and type of pulse feature on overall TS and intra-subject TS variability. Main results. Measurement site, type of pulse feature, and their interaction had significant impacts on the overall TS and intra-subject TS variability (p < 0.001 for all). Valley and maximal second derivative showed higher overall TS than peak and maximal first derivative. Finger had higher overall TS and lower intra-subject TS variability than other measurement sites. Significance. Measurement site and type of pulse feature can significantly influence the timing of feature points on filtered PPG signals. Filtering parameters should be quoted to support the reproducibility of PPG-related studies.

Influence of digital filtering parameters on the pulse waveform in reflectance photoplethysmography

Photoplethysmography (PPG) is an optical method for recording pulse wave (PW) propagating in the tissue microvasculature. As a rule, filters with infinite impulse response (Butterworth, Bessel, etc.) often used in PPG signal processing introduce distortions in the PW signal. At the same time, the filtering parameters for a more accurate reproduction of PW have not yet been substantiated. The aim of this work is to study the influence of digital filtering parameters, such as bandwidth and filter order, on the pulse waveform. In the study, a digital bandpass Butterworth filter was used. The lower cutoff frequency of the filter varied from 0.1 to 1 Hz, the upper cutoff frequency varied from 2 to 10 Hz and the filter order – from 2nd to 6th. It was found that an increase in the lower cutoff frequency of the bandpass filtering leads to a decrease in the amplitude of the reflected diastolic wave and distortion of the front of the direct systolic wave. A decrease in the upper cutoff frequency leads to damping of the dicrotic notch and a phase shift of the PW. Increasing the filter order decreases the reflected wave amplitude. The minimal distortions of the PPG signal were observed at the lower cutoff frequency of 0.1 Hz, the upper one at 10 Hz and the filter order equal to 2. Thus, these parameters of a bandpass filtering can be recommended for processing PPG signals for a more accurate morphological analysis of PW. The obtained results make it possible to create devices for PW analysis with substantiated medical and technical requirements for filtration parameters.

A Noninvasive Blood Glucose Monitoring System Based on Smartphone PPG Signal Processing and Machine Learning

Blood glucose level needs to be monitored regularly to manage the health condition of hyperglycemic patients. The current glucose measurement approaches still rely on invasive techniques which are uncomfortable and raise the risk of infection. To facilitate daily care at home, in this article, we propose an intelligent, noninvasive blood glucose monitoring system which can differentiate a user's blood glucose level into normal, borderline, and warning based on smartphone photoplethysmography (PPG) signals. The main implementation processes of the proposed system include 1) a novel algorithm for acquiring PPG signals using only smartphone camera videos; 2) a fitting-based sliding window algorithm to remove varying degrees of baseline drifts and segment the signal into single periods; 3) extracting characteristic features from the Gaussian functions by comparing PPG signals at different blood glucose levels; 4) categorizing the valid samples into three glucose levels by applying machine learning algorithms. Our proposed system was evaluated on a data set of 80 subjects. Experimental results demonstrate that the system can separate valid signals from invalid ones at an accuracy of 97.54% and the overall accuracy of estimating the blood glucose levels reaches 81.49%. The proposed system provides a reference for the introduction of noninvasive blood glucose technology into daily or clinical applications. This article also indicates that smartphone-based PPG signals have great potential to assess an individual's blood glucose level.

SVR-EEMD: An Improved EEMD Method Based on Support Vector Regression Extension in PPG Signal Denoising.

Photoplethysmography (PPG) has been widely used in noninvasive blood volume and blood flow detection since its first appearance. However, its noninvasiveness also makes the PPG signals vulnerable to noise interference and thus exhibits nonlinear and nonstationary characteristics, which have brought difficulties for the denoising of PPG signals. Ensemble empirical mode decomposition known as EEMD, which has made great progress in noise processing, is a noise-assisted nonlinear and nonstationary time series analysis method based on empirical mode decomposition (EMD). The EEMD method solves the “mode mixing” problem in EMD effectively, but it can do nothing about the “end effect,” another problem in the decomposition process. In response to this problem, an improved EEMD method based on support vector regression extension (SVR-EEMD) is proposed and verified by simulated data and real-world PPG data. Experiments show that the SVR-EEMD method can solve the “end effect” efficiently to get a better decomposition performance than the traditional EEMD method and bring more benefits to the noise processing of PPG signals.

Development of a Low Cost Heart Rate Monitoring and Transmission System using PPG Signal Processing for Wearable Devices

Photoplethysmography (PPG) technique is used in most of the fitness tracking devices available now-a-days, due to its low cost and simplicity. These PPG signals are obtained from the variations in the blood flow with the help of pulse rate sensor. In this paper, PPG signal is acquired from two PPG sensors worn in the finger and the wrist to get the heart rate and its various parameters. The signal noise is removed with the help of MATLAB with various filters to get the smooth signal. The peak detection is done for heart rate (HR) and peak interval calculation, the spectral estimation is done, the first and second derivatives and the heart rate variability are obtained. The low cost Arduino nano and the Bluetooth module is used for the development and the transmission of HR value from the wearable device through an application developed for it, as well as the values are transmitted remotely with the help of the Global System for Communication (GSM) built in the mobile phone, which can display the value and can also transmit it to any particular person or remote physician who can monitor the person’s HR remotely. The complete system developed provides a low cost solution for heart rate detection and monitoring of a person from a distance.

An optimal filter for short photoplethysmogram signals

A photoplethysmogram (PPG) contains a wealth of cardiovascular system information, and with the development of wearable technology, it has become the basic technique for evaluating cardiovascular health and detecting diseases. However, due to the varying environments in which wearable devices are used and, consequently, their varying susceptibility to noise interference, effective processing of PPG signals is challenging. Thus, the aim of this study was to determine the optimal filter and filter order to be used for PPG signal processing to make the systolic and diastolic waves more salient in the filtered PPG signal using the skewness quality index. Nine types of filters with 10 different orders were used to filter 219 (2.1s) short PPG signals. The signals were divided into three categories by PPG experts according to their noise levels: excellent, acceptable, or unfit. Results show that the Chebyshev II filter can improve the PPG signal quality more effectively than other types of filters and that the optimal order for the Chebyshev II filter is the 4th order.

Real-time Heart Rate Measurement based on Photoplethysmography using Android Smartphone Camera

With the development of smartphone technologies enable photoplethysmogram (PPG) acquisition by camera and heart rate (HR) measurement. This papers presents improved algorithm to extract HR from PPG signal recorded by smartphone camera and to develop real-time PPG signal processing Android application. 400 video samples recorded by Samsung smartphone camera are imported as input data for further processing and evaluating algorithm on MATLAB. An optimized algorithm is developed and tested on Android platform with different kind of Samsung smartphones. To assess algorithm's performance, medical device Beurer BC08 is used as reference. According to related works, accuracy parameters includes 90% number of samples that has relative errors less than 5%, Person correlation (r) more than 0.9, and standard estimated error (SEE) less than 5 beats-per-minutes (bpm).