Journal articles on the topic 'Photoplethysmography (PPG) signals'
To see the other types of publications on this topic, follow the link: Photoplethysmography (PPG) signals.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Photoplethysmography (PPG) signals.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Tang, Ya Wen, and Yue Der Lin. "L2-EMD Filter Design for Photoplethysmography Signal." Applied Mechanics and Materials 479-480 (December 2013): 486–90. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.486.
Full textAbstract:
Photoplethysmography (PPG) is a noninvasive bio-signal measurement technique used to monitor peripheral hemal circulatory circumstance. Generally, PPG signal is treated as a morphologically equivalent signal of pulse signals. To prevent the affection of environment noise or motion artifact, an empirical mode decomposition (EMD) base filter with L2 norm similarity selector is proposed in this article. In this experiment, PPG signal of twelve healthy subjects is acquired with a stable state. The acquired PPG signals are mixed with different level white noise to exam the filter capability. 30dB SNR and 60dB SNR noisy PPG signals were adopted and applied with empirical mode decomposition to extract the imply mode of the input signal. After that, a l2-norm calculation is used to evaluate the similarity between each extracted intrinsic mode function (IMF) and the input signal. The high similarity IMFs are collected and used to reconstruct the filtered signal. Although the reconstructed signals may suffer a serious boundary effect as EMD faced, the results show effective noise elimination and prove the l2-EMD filter capability of PPG signals.
2
Ju, Bin, Yun Tao Qian, and Huo Jie Ye. "Wavelet Based Measurement on Photoplethysmography by Smartphone Imaging." Applied Mechanics and Materials 380-384 (August 2013): 773–77. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.773.
Full textAbstract:
[Purpose] Smartphones video cameras can be used to detect the photoplethysmograph (PPG) signal.The pulse wave signal detected by smartphone always mixed mass noise because of finger moving, unevenness of pressure and outer light interference. Previous studies limit to the filtering algorithm that denoising signals, without considering characteristics information of pulse wave itself. [Method] In this paper, we propose an algorithm based on wavelet to detect qualified PPG, which captures three critical characteristic quantities through wavelet high frequency coefficient. [Results] Experiment illustrates that the detected PPG signal contain dicrotic wave, and whats more, further experiment on artery elasticity indexes indicates good robust of the algorithm. [Conclusions] Wavelet Based Measurement on Photoplethysmography by Smartphone Imaging can be used for the calculation of cardiovascular parameter such as angiosclerosis, arrhythmia, and vascular resistance.
3
Alkhoury, Ludvik, JiWon Choi, Vishnu D. Chandran, Gabriela B. De Carvalho, Saikat Pal, and Moshe Kam. "Dual Wavelength Photoplethysmography Framework for Heart Rate Calculation." Sensors 22, no. 24 (December 17, 2022): 9955. http://dx.doi.org/10.3390/s22249955.
Full textAbstract:
The quality of heart rate (HR) measurements extracted from human photoplethysmography (PPG) signals are known to deteriorate under appreciable human motion. Auxiliary signals, such as accelerometer readings, are usually employed to detect and suppress motion artifacts. A 2019 study by Yifan Zhang and his coinvestigatorsused the noise components extracted from an infrared PPG signal to denoise a green PPG signal from which HR was extracted. Until now, this approach was only tested on “micro-motion” such as finger tapping. In this study, we extend this technique to allow accurate calculation of HR under high-intensity full-body repetitive “macro-motion”. Our Dual Wavelength (DWL) framework was tested on PPG data collected from 14 human participants while running on a treadmill. The DWL method showed the following attributes: (1) it performed well under high-intensity full-body repetitive “macro-motion”, exhibiting high accuracy in the presence of motion artifacts (as compared to the leading accelerometer-dependent HR calculation techniques TROIKA and JOSS); (2) it used only PPG signals; auxiliary signals such as accelerometer signals were not needed; and (3) it was computationally efficient, hence implementable in wearable devices. DWL yielded a Mean Absolute Error (MAE) of 1.22|0.57 BPM, Mean Absolute Error Percentage (MAEP) of 0.95|0.38%, and performance index (PI) (which is the frequency, in percent, of obtaining an HR estimate that is within ±5 BPM of the HR ground truth) of 95.88|4.9%. Moreover, DWL yielded a short computation period of 3.0|0.3 s to process a 360-second-long run.
4
Chang, Cheng-Chun, Chien-Ta Wu, Byung Il Choi, and Tong-Jing Fang. "MW-PPG Sensor: An on-Chip Spectrometer Approach." Sensors 19, no. 17 (August 26, 2019): 3698. http://dx.doi.org/10.3390/s19173698.
Full textAbstract:
Multi-wavelength photoplethysmography (MW-PPG) sensing technology has been known to be superior to signal-wavelength photoplethysmography (SW-PPG) sensing technology. However, limited by the availability of sensing detectors, many prior studies can only use conventional bulky and pricy spectrometers as the detectors, and hence cannot bring the MW-PPG technology to daily-life applications. In this study we developed a chip-scale MW-PPG sensor using innovative on-chip spectrometers, aimed at wearable applications. Also in this paper we present signal processing methods for robustly extracting the PPG signals, in which an increase of up to 50% in the signal-to-noise ratio (S/N) was observed. Example measurements of saturation of peripheral blood oxygen (SpO2) and blood pressure were conducted.
5
Li, Suyi, Lijia Liu, Jiang Wu, Bingyi Tang, and Dongsheng Li. "Comparison and Noise Suppression of the Transmitted and Reflected Photoplethysmography Signals." BioMed Research International 2018 (September 26, 2018): 1–9. http://dx.doi.org/10.1155/2018/4523593.
Full textAbstract:
The photoplethysmography (PPG) is inevitably corrupted by many kinds of noise no matter whether its acquisition mode is transmittance or reflectance. To enhance the quality of PPG signals, many studies have made great progress in PPG denoising by adding extra sensors and developing complex algorithms. Considering the reasonable cost, compact size, and real-time and easy implementation, this study proposed a simple real-time denoising method based on double median filters which can be integrated in microcontroller of commercial or portable pulse oximeters without adding extra hardware. First, we used the boundary extension to preserve the signal boundary distortion and designed a first median filter with the time window at approximately 78 ms to eliminate the high-frequency components of the signal. Then, through the second median filter with a time window which was about 780 ms, we estimated the low-frequency components. Finally, we removed the estimated low-frequency components from the signal to obtain the denoised signal. Through comparing the multiple sets of signals under calmly sitting and slightly moving postures, the PPG signals contained noises no matter whether collected by the transmittance-mode or the reflectance-mode. To evaluate the proposed method, we conducted measured, simulated experiments and a strong noisy environment experiment. Through comparing the morphology distortions, frequency spectra, and the signal-to-noise ratios (SNRs), the results showed that the proposed method can suppress noise effectively and preserve the essential morphological features from PPG signals. As a result, the proposed method can enhance the quality of PPG signals and, thus, can contribute to the improvement of the calculation accuracy of the subsequent physiological parameters. In addition, the proposed method could be a good choice to address the real-time noise reduction of portable PPG measuring instruments.
6
Liang, Yongbo, Zhencheng Chen, Rabab Ward, and Mohamed Elgendi. "Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification." Biosensors 8, no. 4 (October 26, 2018): 101. http://dx.doi.org/10.3390/bios8040101.
Full textAbstract:
Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper, we investigate whether or not deep learning can provide better results for hypertension risk stratification when compared to the classical signal processing and feature extraction methods. We tested a deep learning method for the classification and evaluation of hypertension using photoplethysmography (PPG) signals based on the continuous wavelet transform (using Morse) and pretrained convolutional neural network (using GoogLeNet). We collected 121 data recordings from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database, each containing arterial blood pressure (ABP) and photoplethysmography (PPG) signals. The ABP signals were utilized to extract blood pressure category labels, and the PPG signals were used to train and test the model. According to the seventh report of the Joint National Committee, blood pressure levels are categorized as normotension (NT), prehypertension (PHT), and hypertension (HT). For the early diagnosis and assessment of HT, the timely detection of PHT and the accurate diagnosis of HT are significant. Therefore, three HT classification trials were set: NT vs. PHT, NT vs. HT, and (NT + PHT) vs. HT. The F-scores of these three classification trials were 80.52%, 92.55%, and 82.95%, respectively. The tested deep method achieved higher accuracy for hypertension risk stratification when compared to the classical signal processing and feature extraction method. Additionally, the method achieved comparable results to another approach that requires electrocardiogram and PPG signals.
7
Cheshmedzhiev, Krasimir. "A Photoplethysmography Signals Registering Device." Innovative STEM Education 2, no. 1 (August 10, 2020): 13–20. http://dx.doi.org/10.55630/stem.2020.0202.
Full textAbstract:
Monitoring a heart rate provides an essential information about health status of a subjects. Photoplethysmography is a low-cost optical technique to monitor blood volume changes in human body. In this article is presented a portable microcontroller system to register PPG signals from two types of sensors, convert them and store data on internal storage or send it to personal computer for next processing.
8
Yen, Chih-Ta, Sheng-Nan Chang, and Cheng-Hong Liao. "Deep learning algorithm evaluation of hypertension classification in less photoplethysmography signals conditions." Measurement and Control 54, no. 3-4 (March 2021): 439–45. http://dx.doi.org/10.1177/00202940211001904.
Full textAbstract:
This study used photoplethysmography signals to classify hypertensive into no hypertension, prehypertension, stage I hypertension, and stage II hypertension. There are four deep learning models are compared in the study. The difficulties in the study are how to find the optimal parameters such as kernel, kernel size, and layers in less photoplethysmographyt (PPG) training data condition. PPG signals were used to train deep residual network convolutional neural network (ResNetCNN) and bidirectional long short-term memory (BILSTM) to determine the optimal operating parameters when each dataset consisted of 2100 data points. During the experiment, the proportion of training and testing datasets was 8:2. The model demonstrated an optimal classification accuracy of 76% when the testing dataset was used.
9
Yu, Su-Gyeong, So-Eui Kim, Na Hye Kim, Kun Ha Suh, and Eui Chul Lee. "Pulse Rate Variability Analysis Using Remote Photoplethysmography Signals." Sensors 21, no. 18 (September 17, 2021): 6241. http://dx.doi.org/10.3390/s21186241.
Full textAbstract:
Pulse rate variability (PRV) refers to the change in the interval between pulses in the blood volume pulse (BVP) signal acquired using photoplethysmography (PPG). PRV is an indicator of the health status of an individual’s autonomic nervous system. A representative method for measuring BVP is contact PPG (CPPG). CPPG may cause discomfort to a user, because the sensor is attached to the finger for measurements. In contrast, noncontact remote PPG (RPPG) extracts BVP signals from face data using a camera without the need for a sensor. However, because the existing RPPG is a technology that extracts a single pulse rate rather than a continuous BVP signal, it is difficult to extract additional health status indicators. Therefore, in this study, PRV analysis is performed using lab-based RPPG technology that can yield continuous BVP signals. In addition, we intended to confirm that the analysis of PRV via RPPG can be performed with the same quality as analysis via CPPG. The experimental results confirmed that the temporal and frequency parameters of PRV extracted from RPPG and CPPG were similar. In terms of correlation, the PRVs of RPPG and CPPG yielded correlation coefficients between 0.98 and 1.0.
10
Charlton, Peter H., Panicos Kyriacou, Jonathan Mant, and Jordi Alastruey. "Acquiring Wearable Photoplethysmography Data in Daily Life: The PPG Diary Pilot Study." Engineering Proceedings 2, no. 1 (November 14, 2020): 80. http://dx.doi.org/10.3390/ecsa-7-08233.
Full textAbstract:
The photoplethysmogram (PPG) signal is widely measured by smart watches and fitness bands for heart rate monitoring. New applications of the PPG are also emerging, such as to detect irregular heart rhythms, track infectious diseases, and monitor blood pressure. Consequently, datasets of PPG signals acquired in daily life are valuable for algorithm development. The aim of this pilot study was to assess the feasibility of acquiring PPG data in daily life. A single subject was asked to wear a wrist-worn PPG sensor six days a week for four weeks, and to keep a diary of daily activities. The sensor was worn for 75.0% of the time, signals were acquired for 60.6% of the time, and signal quality was high for 30.5% of the time. This small pilot study demonstrated the feasibility of acquiring PPG data during daily living. Key lessons were learnt for future studies: (i) devices which are waterproof and require charging less frequently may provide signals for a greater proportion of the time; (ii) data should either be stored on the device or streamed via a reliable connection to a second device for storage; (iii) it may be beneficial to acquire signals during the night or during periods of low activity to achieve high signal quality; and (iv) there are several promising areas for PPG algorithm development including the design of pulse wave analysis techniques to track changes in cardiovascular properties in daily life. The dataset and code are publicly available at DOI: 10.5281/zenodo.3268500.
11
Neshitov, Alexander, Konstantin Tyapochkin, Evgeniya Smorodnikova, and Pavel Pravdin. "Wavelet Analysis and Self-Similarity of Photoplethysmography Signals for HRV Estimation and Quality Assessment." Sensors 21, no. 20 (October 13, 2021): 6798. http://dx.doi.org/10.3390/s21206798.
Full textAbstract:
Peak-to-peak intervals in Photoplethysmography (PPG) can be used for heart rate variability (HRV) estimation if the PPG is collected from a healthy person at rest. Many factors, such as a person’s movements or hardware issues, can affect the signal quality and make some parts of the PPG signal unsuitable for reliable peak detection. Therefore, a robust HRV estimation algorithm should not only detect peaks, but also identify corrupted signal parts. We introduce such an algorithm in this paper. It uses continuous wavelet transform (CWT) for peak detection and a combination of features derived from CWT and metrics based on PPG signals’ self-similarity to identify corrupted parts. We tested the algorithm on three different datasets: a newly introduced Welltory-PPG-dataset containing PPG signals collected with smartphones using the Welltory app, and two publicly available PPG datasets: TROIKAand PPG-DaLiA. The algorithm demonstrated good accuracy in peak-to-peak intervals detection and HRV metric estimation.
12
Wu, Meng-Ting, I.-Fan Liu, Yun-Hsuan Tzeng, and Lei Wang. "Modified photoplethysmography signal processing and analysis procedure for obtaining reliable stiffness index reflecting arteriosclerosis severity." Physiological Measurement 43, no. 8 (August 3, 2022): 085001. http://dx.doi.org/10.1088/1361-6579/ac7d91.
Full textAbstract:
Abstract Objective. This study aimed to describe a modified photoplethysmography (PPG) signal processing and analysis procedure to obtain a more reliable arterial stiffness index (SI). Approach. Three parameters were used to assess the PPG signal quality without prominent diastolic waves, which are similar to a sinusoidal waveform shape. The first parameter, sinusoidal ratio (S-value), was based on frequency-domain analysis: a higher S-value indicated the presence of PPG pulse wave with unapparent diastolic peak. The second parameter was the time difference between systolic peak-to-diastolic peak and the systolic peak-to-dicrotic notch. The third parameter was the percentage of sin-like waveform in the PPG signals. The applicability of these parameters was demonstrated in 40 participants, including 11 with apparent diastolic peaks in the PPG signals and 29 with unapparent diastolic peaks. Main results. An S-value of >3.5 indicated apparent diastolic peaks in the PPG signals. In addition, a systolic peak-to-diastolic peak time difference >80% and a sin-like waveform >55% may be associated with severity of vascular aging. Significance. These parameters successfully detected low-quality PPG signals with unapparent diastolic waveform before SI calculation, thereby ensuring the accuracy of subsequent evaluation of cardiovascular-related disease and clinical risk stratification.
13
Kim, Seamin, Xiao Xiao, and Jun Chen. "Advances in Photoplethysmography for Personalized Cardiovascular Monitoring." Biosensors 12, no. 10 (October 12, 2022): 863. http://dx.doi.org/10.3390/bios12100863.
Full textAbstract:
Photoplethysmography (PPG) is garnering substantial interest due to low cost, noninvasiveness, and its potential for diagnosing cardiovascular diseases, such as cardiomyopathy, heart failure, and arrhythmia. The signals obtained through PPG can yield information based on simple analyses, such as heart rate. In contrast, when accompanied by the complex analysis of sophisticated signals, valuable information, such as blood pressure, sympathetic nervous system activity, and heart rate variability, can be obtained. For a complex analysis, a better understanding of the sources of noise, which create limitations in the application of PPG, is needed to get reliable information to assess cardiovascular health. Therefore, this Special Issue handles literature about noises and how they affect the waveform of the PPG caused by individual variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external factors (e.g., motion artifact, ambient light, and applied pressure to the skin). It also covers the issues that still need to be considered in each situation.
14
Abdelaziz, Abdulrahman B., Mohammad A. Rahimi, Muhammad R. Alrabeiah, Ahmed B. Ibrahim, Ahmed S. Almaiman, Amr M. Ragheb, and Saleh A. Alshebeili. "Photoplethysmography Data Reduction Using Truncated Singular Value Decomposition and Internet of Things Computing." Electronics 12, no. 1 (January 2, 2023): 220. http://dx.doi.org/10.3390/electronics12010220.
Full textAbstract:
Biometric-based identity authentication is integral to modern-day technologies. From smart phones, personal computers, and tablets to security checkpoints, they all utilize a form of identity check based on methods such as face recognition and fingerprint-verification. Photoplethysmography (PPG) is another form of biometric-based authentication that has recently been gaining momentum, because it is effective and easy to implement. This paper considers a cloud-based system model for PPG-authentication, where the PPG signals of various individuals are collected with distributed sensors and communicated to the cloud for authentication. Such a model incursarge signal traffic, especially in crowded places such as airport security checkpoints. This motivates the need for a compression–decompression scheme (or a Codec for short). The Codec is required to reduce the data traffic by compressing each PPG signal before it is communicated, i.e., encoding the signal right after it comes off the sensor and before it is sent to the cloud to be reconstructed (i.e., decoded). Therefore, the Codec has two system requirements to meet: (i) produce high-fidelity signal reconstruction; and (ii) have a computationallyightweight encoder. Both requirements are met by the Codec proposed in this paper, which is designed using truncated singular value decomposition (T-SVD). The proposed Codec is developed and tested using a publicly available dataset of PPG signals collected from multiple individuals, namely the CapnoBase dataset. It is shown to achieve a 95% compression ratio and a 99% coefficient of determination. This means that the Codec is capable of delivering on the first requirement, high-fidelity reconstruction, while producing highly compressed signals. Those compressed signals do not require heavy computations to be produced as well. An implementation on a single-board computer is attempted for the encoder, showing that the encoder can average 300 milliseconds per signal on a Raspberry Pi 3. This is enough time to encode a PPG signal prior to transmission to the cloud.
15
Liu, Shing-Hong, Jia-Jung Wang, Chun-Hung Su, and Da-Chuan Cheng. "Improvement of Left Ventricular Ejection Time Measurement in the Impedance Cardiography Combined with the Reflection Photoplethysmography." Sensors 18, no. 9 (September 11, 2018): 3036. http://dx.doi.org/10.3390/s18093036.
Full textAbstract:
Cardiac stroke volume (SV) is an essential hemodynamic indicator that can be used to assess whether the pump function of the heart is normal. Non-invasive SV measurement is currently performed using the impedance cardiography (ICG). In this technology, left ventricular ejection time (LVET) is an important parameter which can be determined from the ICG signals. However, the ICG signals are inherently susceptible to artificial noise interference, which leads to an inaccurate LVET measurement and then yields an error in the calculation of SV. Therefore, the goal of the study was to measure LVETs using both the transmission and reflection photoplethysmography (PPG), and to assess whether the measured LVET was more accurate by the PPG signal than the ICG signal. The LVET measured by the phonocardiography (PCG) was used as the standard for comparing with those by the ICG and PPG. The study recruited ten subjects whose LVETs were simultaneously measured by the ICG using four electrodes, the reflection PPG using neck sensors (PPGneck) and the transmission PPG using finger sensors (PPGfinger). In each subject, ten LVETs were obtained from ten heartbeats selected properly from one-minute recording. The differences of the measured LVETs between the PCG and one of the ICG, PPGneck and PPGfinger were −68.2 ± 148.6 ms, 4.8 ± 86.5 ms and −7.0 ± 107.5 ms, respectively. As compared with the PCG, both the ICG and PPGfinger underestimated but the PPGneck overestimated the LVETs. Furthermore, the measured LVET by the PPGneck was the closest to that by the PCG. Therefore, the PPGneck may be employed to improve the LVET measurement in applying the ICG for continuous monitoring of SV in clinical settings.
16
Prabhakar, Sunil Kumar, Harikumar Rajaguru, and Sun-Hee Kim. "Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders." Diagnostics 10, no. 10 (September 28, 2020): 763. http://dx.doi.org/10.3390/diagnostics10100763.
Full textAbstract:
The main aim of this paper is to optimize the output of diagnosis of Cardiovascular Disorders (CVD) in Photoplethysmography (PPG) signals by utilizing a fuzzy-based approach with classification. The extracted parameters such as Energy, Variance, Approximate Entropy (ApEn), Mean, Standard Deviation (STD), Skewness, Kurtosis, and Peak Maximum are obtained initially from the PPG signals, and based on these extracted parameters, the fuzzy techniques are incorporated to model the Cardiovascular Disorder(CVD) risk levels from PPG signals. Optimization algorithms such as Differential Search (DS), Shuffled Frog Leaping Algorithm (SFLA), Wolf Search (WS), and Animal Migration Optimization (AMO) are implemented to the fuzzy modeled levels to optimize them further so that the PPG cardiovascular classification can be characterized well. This kind of approach is totally new in PPG signal classification, and the results show that when fuzzy-inspired modeling is implemented with WS optimization and classified with the Radial Basis Function (RBF) classifier, a classification accuracy of 94.79% is obtained for normal cases. When fuzzy-inspired modeling is implemented with AMO and classified with the Support Vector Machine–Radial Basis Function (SVM–RBF) classifier, a classification accuracy of 95.05% is obtained for CVD cases.
17
Charlton, Peter H., Birutė Paliakaitė, Kristjan Pilt, Martin Bachler, Serena Zanelli, Dániel Kulin, John Allen, et al. "Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet." American Journal of Physiology-Heart and Circulatory Physiology 322, no. 4 (April 1, 2022): H493—H522. http://dx.doi.org/10.1152/ajpheart.00392.2021.
Full textAbstract:
The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age.
18
Lin, Chih-Hsueh, Zhi-Hao Wang, and Gwo-Jia Jong. "A research on relevance between photoplethysmography signal and perceptual stimulation." International Journal of Modern Physics B 34, no. 22n24 (August 18, 2020): 2040129. http://dx.doi.org/10.1142/s0217979220401293.
Full textAbstract:
In this paper, the Photoplethysmography (PPG) signal response of humans under different perceptual stimuli was mainly discussed. The autonomic nervous system (ANS) regulates the functioning of organs or tissues in the body to make the body adapt to the environment. When the human body is stimulated, it can obtain information about ANS from the analysis results of heart rate variability (HRV).The proposed method is used with PPG signals for time domain and frequency domain analysis to obtain HRV parameters and short-time Fourier transform (STFT) output heat map, respectively. Different environmental stimuli were given during the experiment and changes in PPG signals were observed. There were eight males and eight females with a total of 16 subjects with an average age of 24 years. Heart rate was observed to vary with stimulation in 16 subjects. The heat map clearly distinguishes whether the subject is affected by the stimulus and causes the ANS to regulate the human body. The data obtained under various stimuli are directly compared and measured from physiological signals. It can establish an objective patient’s current emotional judgment and can be used to explore the patient’s diagnosis status during the diagnosis process.
19
Li, Suyi, Shanqing Jiang, Shan Jiang, Jiang Wu, Wenji Xiong, and Shu Diao. "A Hybrid Wavelet-Based Method for the Peak Detection of Photoplethysmography Signals." Computational and Mathematical Methods in Medicine 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/9468503.
Full textAbstract:
The noninvasive peripheral oxygen saturation (SpO2) and the pulse rate can be extracted from photoplethysmography (PPG) signals. However, the accuracy of the extraction is directly affected by the quality of the signal obtained and the peak of the signal identified; therefore, a hybrid wavelet-based method is proposed in this study. Firstly, we suppressed the partial motion artifacts and corrected the baseline drift by using a wavelet method based on the principle of wavelet multiresolution. And then, we designed a quadratic spline wavelet modulus maximum algorithm to identify the PPG peaks automatically. To evaluate this hybrid method, a reflective pulse oximeter was used to acquire ten subjects’ PPG signals under sitting, raising hand, and gently walking postures, and the peak recognition results on the raw signal and on the corrected signal were compared, respectively. The results showed that the hybrid method not only corrected the morphologies of the signal well but also optimized the peaks identification quality, subsequently elevating the measurement accuracy of SpO2 and the pulse rate. As a result, our hybrid wavelet-based method profoundly optimized the evaluation of respiratory function and heart rate variability analysis.
20
Chang, Xiangmao, Gangkai Li, Guoliang Xing, Kun Zhu, and Linlin Tu. "DeepHeart." ACM Transactions on Sensor Networks 17, no. 2 (June 2021): 1–18. http://dx.doi.org/10.1145/3441626.
Full textAbstract:
Heart rate (HR) estimation based on photoplethysmography (PPG) signals has been widely adopted in wrist-worn devices. However, the motion artifacts caused by the user’s physical activities make it difficult to get the accurate HR estimation from contaminated PPG signals. Although many signal processing methods have been proposed to address this challenge, they are often highly optimized for specific scenarios, making them impractical in real-world settings where a user may perform a wide range of physical activities. In this article, we propose DeepHeart, a new HR estimation approach that features deep-learning-based denoising and spectrum-analysis-based calibration. DeepHeart generates clean PPG signals from electrocardiogram signals based on a training data set. Then a set of denoising convolutional neural networks (DCNNs) are trained with the contaminated PPG signals and their corresponding clean PPG signals. Contaminated PPG signals are then denoised by an ensemble of DCNNs and a spectrum-analysis-based calibration is performed to estimate the final HR. We evaluate DeepHeart on the IEEE Signal Processing Cup training data set with 12 records collected during various physical activities. DeepHeart achieves an average absolute error of 1.61 beats per minute (bpm), outperforming a state-of-the-art deep learning approach (4 bpm) and a classical signal processing approach (2.34 bpm).
21
Jeong, Youngwoo, Joungmin Park, Sun Beom Kwon, and Seung Eun Lee. "Photoplethysmography-Based Distance Estimation for True Wireless Stereo." Micromachines 14, no. 2 (January 19, 2023): 252. http://dx.doi.org/10.3390/mi14020252.
Full textAbstract:
Recently, supplying healthcare services with wearable devices has been investigated. To realize this for true wireless stereo (TWS), which has limited resources (e.g., space, power consumption, and area), implementing multiple functions with one sensor simultaneously is required. The Photoplethysmography (PPG) sensor is a representative healthcare sensor that measures repeated data according to the heart rate. However, since the PPG data are biological, they are influenced by motion artifact and subject characteristics. Hence, noise reduction is needed for PPG data. In this paper, we propose the distance estimation algorithm for PPG signals of TWS. For distance estimation, we designed a waveform adjustment (WA) filter that minimizes noise while maintaining the relationship between before and after data, a lightweight deep learning model called MobileNet, and a PPG monitoring testbed. The number of criteria for distance estimation was set to three. In order to verify the proposed algorithm, we compared several metrics with other filters and AI models. The highest accuracy, precision, recall, and f1 score of the proposed algorithm were 92.5%, 92.6%, 92.8%, and 0.927, respectively, when the signal length was 15. Experimental results of other algorithms showed higher metrics than the proposed algorithm in some cases, but the proposed model showed the fastest inference time.
22
Zhao, Xiangfa, and Guobing Sun. "A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals." Entropy 23, no. 1 (January 18, 2021): 116. http://dx.doi.org/10.3390/e23010116.
Full textAbstract:
Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen’s kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.
23
Moscato, Serena, Stella Lo Giudice, Giulia Massaro, and Lorenzo Chiari. "Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis." Sensors 22, no. 15 (August 4, 2022): 5831. http://dx.doi.org/10.3390/s22155831.
Full textAbstract:
Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context.
24
Lu, Huan, Guangjie Yuan, Jin Zhang, and Guangyuan Liu. "Recognition of Impulse of Love at First Sight Based On Photoplethysmography Signal." Sensors 20, no. 22 (November 17, 2020): 6572. http://dx.doi.org/10.3390/s20226572.
Full textAbstract:
Love at first sight is a well-known and interesting phenomenon, and denotes the strong attraction to a person of the opposite sex when first meeting. As far as we know, there are no studies on the changes in physiological signals between the opposite sexes when this phenomenon occurs. Although privacy is involved, knowing how attractive a partner is may be beneficial to building a future relationship in an open society where both men and women accept each other. Therefore, this study adopts the photoplethysmography (PPG) signal acquisition method (already applied in wearable devices) to collect signals that are beneficial for utilizing the results of the analysis. In particular, this study proposes a love pulse signal recognition algorithm based on a PPG signal. First, given the high correlation between the impulse signals of love at first sight and those for physical attractiveness, photos of people with different levels of attractiveness are used to induce real emotions. Then, the PPG signal is analyzed in the time, frequency, and nonlinear domains, respectively, in order to extract its physiological characteristics. Finally, we propose the use of a variety of machine learning techniques (support vector machine (SVM), random forest (RF), linear discriminant analysis (LDA), and extreme gradient enhancement (XGBoost)) for identifying the impulsive states of love, with or without feature selection. The results show that the XGBoost classifier has the highest classification accuracy (71.09%) when using the feature selection.
25
Martínez, Gloria, Newton Howard, Derek Abbott, Kenneth Lim, Rabab Ward, and Mohamed Elgendi. "Can Photoplethysmography Replace Arterial Blood Pressure in the Assessment of Blood Pressure?" Journal of Clinical Medicine 7, no. 10 (September 30, 2018): 316. http://dx.doi.org/10.3390/jcm7100316.
Full textAbstract:
Arterial Blood Pressure (ABP) and photoplethysmography (PPG) are both useful techniques to monitor cardiovascular status. Though ABP monitoring is more widely employed, this procedure of signal acquisition whether done invasively or non-invasively may cause inconvenience and discomfort to the patients. PPG, however, is simple, noninvasive, and can be used for continuous measurement. This paper focuses on analyzing the similarities in time and frequency domains between ABP and PPG signals for normotensive, prehypertensive and hypertensive subjects and the feasibility of the classification of subjects considering the results of the analysis performed. From a database with 120 records of ABP and PPG, each 120 s in length, the records where separated into epochs taking into account 10 heartbeats, and the following statistical measures were performed: Correlation (r), Coherence (COH), Partial Coherence (pCOH), Partial Directed Coherence (PDC), Directed Transfer Function (DTF), Full Frequency Directed Transfer Function (ffDTF) and Direct Directed Transfer Function (dDTF). The correlation coefficient was r > 0.9 on average for all groups, indicating a strong morphology similarity. For COH and pCOH, coherence (linear correlation in frequency domain) was found with significance (p < 0.01) in differentiating between normotensive and hypertensive subjects using PPG signals. For the dataset at hand, only two synchrony measures are able to convincingly distinguish hypertensive subjects from normotensive control subjects, i.e., ffDTF and dDTF. From PDC, DTF, ffDTF, and dDTF, a consistent, a strong significant causality from ABP→PPG was found. When all synchrony measures were combined, an 87.5 % accuracy was achieved to detect hypertension using a Neural Network classifier, suggesting that PPG holds most informative features that exist in ABP.
26
Ysehak Abay, Tomas, Kamran Shafqat, and Panayiotis A. Kyriacou. "Perfusion Changes at the Forehead Measured by Photoplethysmography during a Head-Down Tilt Protocol." Biosensors 9, no. 2 (May 27, 2019): 71. http://dx.doi.org/10.3390/bios9020071.
Full textAbstract:
Photoplethysmography (PPG) signals from the forehead can be used in pulse oximetry as they are less affected by vasoconstriction compared to fingers. However, the increase in venous blood caused by the positioning of the patient can deteriorate the signals and cause erroneous estimations of the arterial oxygen saturation. To date, there is no method to measure this venous presence under the PPG sensor. This study investigates the feasibility of using PPG signals from the forehead in an effort to estimate relative changes in haemoglobin concentrations that could reveal these posture-induced changes. Two identical reflectance PPG sensors were placed on two different positions on the forehead (above the eyebrow and on top of a large vein) in 16 healthy volunteers during a head-down tilt protocol. Relative changes in oxygenated ( Δ HbO 2 ), reduced ( Δ HHb) and total ( Δ tHb) haemoglobin were estimated from the PPG signals and the trends were compared with reference Near Infrared Spectroscopy (NIRS) measurements. Also, the signals from the two PPG sensors were analysed in order to reveal any difference due to the positioning of the sensor. Δ HbO 2 , Δ HHb and Δ tHb estimated from the forehead PPGs trended well with the same parameters from the reference NIRS. However, placing the sensor over a large vasculature reduces trending against NIRS, introduces biases as well as increases the variability of the changes in Δ HHb. Forehead PPG signals can be used to measure perfusion changes to reveal venous pooling induced by the positioning of the subject. Placing the sensor above the eyebrow and away from large vasculature avoids biases and large variability in the measurements.
27
Lee, Jongshill, Minseong Kim, Hoon-Ki Park, and In Young Kim. "Motion Artifact Reduction in Wearable Photoplethysmography Based on Multi-Channel Sensors with Multiple Wavelengths." Sensors 20, no. 5 (March 9, 2020): 1493. http://dx.doi.org/10.3390/s20051493.
Full textAbstract:
Photoplethysmography (PPG) is an easy and convenient method by which to measure heart rate (HR). However, PPG signals that optically measure volumetric changes in blood are not robust to motion artifacts. In this paper, we develop a PPG measuring system based on multi-channel sensors with multiple wavelengths and propose a motion artifact reduction algorithm using independent component analysis (ICA). We also propose a truncated singular value decomposition for 12-channel PPG signals, which contain direction and depth information measured using the developed multi-channel PPG measurement system. The performance of the proposed method is evaluated against the R-peaks of an electrocardiogram in terms of sensitivity (Se), positive predictive value (PPV), and failed detection rate (FDR). The experimental results show that Se, PPV, and FDR were 99%, 99.55%, and 0.45% for walking, 96.28%, 99.24%, and 0.77% for fast walking, and 82.49%, 99.83%, and 0.17% for running, respectively. The evaluation shows that the proposed method is effective in reducing errors in HR estimation from PPG signals with motion artifacts in intensive motion situations such as fast walking and running.
28
Li, Zheming, and Wei He. "A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model." Sensors 21, no. 21 (October 29, 2021): 7207. http://dx.doi.org/10.3390/s21217207.
Full textAbstract:
Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.
29
Peláez-Coca, M. D., M. Orini, J. Lázaro, R. Bailón, and E. Gil. "Cross Time-Frequency Analysis for Combining Information of Several Sources: Application to Estimation of Spontaneous Respiratory Rate from Photoplethysmography." Computational and Mathematical Methods in Medicine 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/631978.
Full textAbstract:
A methodology that combines information from several nonstationary biological signals is presented. This methodology is based on time-frequency coherence, that quantifies the similarity of two signals in the time-frequency domain. A cross time-frequency analysis method, based on quadratic time-frequency distribution, has been used for combining information of several nonstationary biomedical signals. In order to evaluate this methodology, the respiratory rate from the photoplethysmographic (PPG) signal is estimated. The respiration provokes simultaneous changes in the pulse interval, amplitude, and width of the PPG signal. This suggests that the combination of information from these sources will improve the accuracy of the estimation of the respiratory rate. Another target of this paper is to implement an algorithm which provides a robust estimation. Therefore, respiratory rate was estimated only in those intervals where the features extracted from the PPG signals are linearly coupled. In 38 spontaneous breathing subjects, among which 7 were characterized by a respiratory rate lower than 0.15 Hz, this methodology provided accurate estimates, with the median error{0.00; 0.98} mHz ({0.00; 0.31}%) and the interquartile range error{4.88; 6.59} mHz ({1.60; 1.92}%). The estimation error of the presented methodology was largely lower than the estimation error obtained without combining different PPG features related to respiration.
30
Chen, Shao-Hao, Yung-Chi Chuang, and Cheng-Chun Chang. "Development of a Portable All-Wavelength PPG Sensing Device for Robust Adaptive-Depth Measurement: A Spectrometer Approach with a Hydrostatic Measurement Example." Sensors 20, no. 22 (November 17, 2020): 6556. http://dx.doi.org/10.3390/s20226556.
Full textAbstract:
Photoplethysmography (PPG), a noninvasive optical sensing technology, has been widely used to measure various physiological indices. Over-the-counter PPG devices are typically composed of a single-wavelength light source, namely, single-wavelength PPG (SW-PPG). It is known that signals of SW-PPG are easily contaminated or distorted by measurement conditions such as motion artifacts, wearing pressure, and skin type. Since lights of different wavelengths can penetrate skin tissues at different depths, how to effectively construct a multiwavelength PPG (MW-PPG) device or even an all-wavelength PPG (AW-PPG) device has attracted great attention. There is also a very interesting question, that is, what could be the potential benefits of using MW-PPG or AW-PPG devices? This paper demonstrates the construction of an AW-PPG portable device and conducts a preliminary evaluation. The presented device consists of four light-emitting diodes, a chip-scale spectrometer, a microcontroller, a Bluetooth Low Energy transceiver, and a phone app. The maximum ratio combining algorithm (MRC) is used to combine the PPG signals derived from different wavelengths to achieve a better signal-to-noise ratio (S/N). The PPG signals from the developed MRC-AW-PPG device versus those from the conventional SW-PPG device are compared in terms of different hydrostatic pressure conditions. It has been observed that the MRC-AW-PPG device can provide more stable PPG signals than that of a conventional PPG device. The results shine a light on the potential benefits of using multiple wavelengths for the next generation of noninvasive PPG sensing.
31
Qin, Caijie, Xiaohua Wang, Guangjun Xu, and Xibo Ma. "Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals." BioMed Research International 2022 (October 1, 2022): 1–16. http://dx.doi.org/10.1155/2022/8094351.
Full textAbstract:
Objective. To review the progress of research on photoplethysmography- (PPG-) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless continuous blood pressure studies using PPG signals in the recent five years were searched. Results. Based on the retrieved literature, this paper describes the available open datasets, commonly used signal preprocessing methods, and model evaluation criteria. Early researches employed multisite PPG signals to calculate pulse wave velocity or time and predicted blood pressure by a simple linear equation. Later, extensive researches were dedicated to mine the features of PPG signals related to blood pressure and regressed blood pressure by machine learning models. Most recently, many researches have emerged to experiment with complex deep learning models for blood pressure prediction with the raw PPG signal as input. Conclusion. This paper summarized the methods in the retrieved literature, provided insight into the artificial intelligence algorithms employed in the literature, and concluded with a discussion of the challenges and opportunities for the development of cuffless continuous blood pressure monitoring technologies.
32
Lee, Inho, Nakkyun Park, Hanbee Lee, Chuljin Hwang, Joo Hee Kim, and Sungjun Park. "Systematic Review on Human Skin-Compatible Wearable Photoplethysmography Sensors." Applied Sciences 11, no. 5 (March 5, 2021): 2313. http://dx.doi.org/10.3390/app11052313.
Full textAbstract:
The rapid advances in human-friendly and wearable photoplethysmography (PPG) sensors have facilitated the continuous and real-time monitoring of physiological conditions, enabling self-health care without being restricted by location. In this paper, we focus on state-of-the-art skin-compatible PPG sensors and strategies to obtain accurate and stable sensing of biological signals adhered to human skin along with light-absorbing semiconducting materials that are classified as silicone, inorganic, and organic absorbers. The challenges of skin-compatible PPG-based monitoring technologies and their further improvements are also discussed. We expect that such technological developments will accelerate accurate diagnostic evaluation with the aid of the biomedical electronic devices.
33
May, James M., Elisa Mejía-Mejía, Michelle Nomoni, Karthik Budidha, Changmok Choi, and Panicos A. Kyriacou. "Effects of Contact Pressure in Reflectance Photoplethysmography in an In Vitro Tissue-Vessel Phantom." Sensors 21, no. 24 (December 16, 2021): 8421. http://dx.doi.org/10.3390/s21248421.
Full textAbstract:
With the continued development and rapid growth of wearable technologies, PPG has become increasingly common in everyday consumer devices such as smartphones and watches. There is, however, minimal knowledge on the effect of the contact pressure exerted by the sensor device on the PPG signal and how it might affect its morphology and the parameters being calculated. This study explores a controlled in vitro study to investigate the effect of continually applied contact pressure on PPG signals (signal-to-noise ratio (SNR) and 17 morphological PPG features) from an artificial tissue-vessel phantom across a range of simulated blood pressure values. This experiment confirmed that for reflectance PPG signal measurements for a given anatomical model, there exists an optimum sensor contact pressure (between 35.1 mmHg and 48.1 mmHg). Statistical analysis shows that temporal morphological features are less affected by contact pressure, lending credit to the hypothesis that for some physiological parameters, such as heart rate and respiration rate, the contact pressure of the sensor is of little significance, whereas the amplitude and geometric features can show significant change, and care must be taken when using morphological analysis for parameters such as SpO2 and assessing autonomic responses.
34
Askarian, Behnam, Kwanghee Jung, and Jo Woon Chong. "Monitoring of Heart Rate from Photoplethysmographic Signals Using a Samsung Galaxy Note8 in Underwater Environments." Sensors 19, no. 13 (June 26, 2019): 2846. http://dx.doi.org/10.3390/s19132846.
Full textAbstract:
Photoplethysmography (PPG) is a commonly used in determining heart rate and oxygen saturation (SpO2). However, PPG measurements and its accuracy are heavily affected by the measurement procedure and environmental factors such as light, temperature, and medium. In this paper, we analyzed the effects of different mediums (water vs. air) and temperature on the PPG signal quality and heart rate estimation. To evaluate the accuracy, we compared our measurement output with a gold-standard PPG device (NeXus-10 MKII). The experimental results show that the average PPG signal amplitude values of the underwater environment decreased considerably (22% decrease) compared to PPG signals of dry environments, and the heart rate measurement deviated 7% (5 beats per minute on average. The experimental results also show that the signal to noise ratio (SNR) and signal amplitude decrease as temperature decreases. Paired t-test which compares amplitude and heart rate values between the underwater and dry environments was performed and the test results show statistically significant differences for both amplitude and heart rate values (p < 0.05). Moreover, experimental results indicate that decreasing the temperature from 45 °C to 5 °C or changing the medium from air to water decreases PPG signal quality, (e.g., PPG signal amplitude decreases from 0.560 to 0.112). The heart rate is estimated within 5.06 bpm deviation at 18 °C in underwater environment, while estimation accuracy decreases as temperature goes down.
35
Medina, Angie, Nikolai Lopez, Jarelh Galdos, Elvis Supo, Jorge Rendulich, and Erasmo Sulla. "Continuous Blood Pressure Estimation in Wearable Devices Using Photoplethysmography: A Review." International Journal of Emerging Technology and Advanced Engineering 12, no. 10 (October 1, 2022): 104–13. http://dx.doi.org/10.46338/ijetae1022_12.
Full textAbstract:
Cardiovascular diseases (CVD) are among those with the highest mortality rates, and various wearable devices for continuous monitoring are emerging as a complement to medical procedures. Blood pressure (BP) monitoring in wearable devices, in order to be continuous, must be performed noninvasively, thus involving photoplethysmography (PPG), a technology that has been widely studied in recent years as a non-invasive solution for BP estimation. However, continuous data acquisition in a wearable system is still a challenge, one of the reasons being the noise caused by movement, the correct use of the PPG signal, and the estimation method to be used. This paper reviews the advances in blood pressure estimation based on photoplethysmography, focusing on the analysis of the preprocessing (ICA, FIR, adaptive filters) of the signals. Among the filters reviewed, the most suitable for dealing with Motion Artifacts (MA) of a wearable system are the adaptive filters, because conventional filters are limited to work only in the band for which they are designed, which does not always cover the spectrum of the MA. A review of the estimation methods is also carried out, among them machine learning stands out because it shows greater growth due to the new proposals that use more signals and obtain better results in terms of accuracy. The objective is to know and analyze the appropriate preprocessing filters and estimation methods from the perspective of wearable systems using PPG sensors affected by AM. Keywords— Blood Pressure Estimation, PAT, PTT, Machine Learning, Photoplethysmography, adaptive filtering.
36
Wu, Jiaze, Hao Liang, Changsong Ding, Xindi Huang, Jianhua Huang, and Qinghua Peng. "Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning." International Journal of Hypertension 2021 (August 5, 2021): 1–9. http://dx.doi.org/10.1155/2021/9938584.
Full textAbstract:
Background. Continuous wavelet transform (CWT) based scalogram can be used for photoplethysmography (PPG) signal transformation to classify blood pressure (BP) with deep learning. We aimed to investigate the determinants that can improve the accuracy of BP classification based on PPG and deep learning and establish a better algorithm for the prediction. Methods. The dataset from PhysioNet was accessed to extract raw PPG signals for testing and its corresponding BPs as category labels. The BP category of normal or abnormal followed the criteria of the 2017 American College of Cardiology/American Heart Association (ACC/AHA) Hypertension Guidelines. The PPG signals were transformed into 224 ∗ 224 ∗ 3-pixel scalogram via different CWTs and segment units. All of them are fed into different convolutional neural networks (CNN) for training and validation. The receiver-operating characteristic and loss and accuracy curves were used to evaluate and compare the performance of different methods. Results. Both wavelet type and segment length could affect the accuracy, and Cgau1 wavelet and segment-300 revealed the best performance (accuracy 90%) without obvious overfitting. This method performed better than previously reported MATLAB Morse wavelet transformed scalogram on both of our proposed CNN and CNN-GoogLeNet. Conclusions. We have established a new algorithm with high accuracy to predict BP classification from PPG via matching of CWT type and segment length, which is a promising solution for rapid prediction of BP classification from real-time processing of PPG signal on a wearable device.
37
Man, Ping-Kwan, Kit-Leong Cheung, Nawapon Sangsiri, Wilfred Jin Shek, Kwan-Long Wong, Jing-Wei Chin, Tsz-Tai Chan, and Richard Hau-Yue So. "Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring." Healthcare 10, no. 10 (October 21, 2022): 2113. http://dx.doi.org/10.3390/healthcare10102113.
Full textAbstract:
Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.
38
Tang, Qunfeng, Zhencheng Chen, Rabab Ward, Carlo Menon, and Mohamed Elgendi. "Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram." Bioengineering 9, no. 8 (August 18, 2022): 402. http://dx.doi.org/10.3390/bioengineering9080402.
Full textAbstract:
The continuous prediction of arterial blood pressure (ABP) waveforms via non-invasive methods is of great significance for the prevention and treatment of cardiovascular disease. Photoplethysmography (PPG) can be used to reconstruct ABP signals due to having the same excitation source and high signal similarity. The existing methods of reconstructing ABP signals from PPG only focus on the similarities between systolic, diastolic, and mean arterial pressures without evaluating their global similarity. This paper proposes a deep learning model with a W-Net architecture to reconstruct ABP signals from PPG. The W-Net consists of two concatenated U-Net architectures, the first acting as an encoder and the second as a decoder to reconstruct ABP from PPG. Five hundred records of different lengths were used for training and testing. The experimental results yielded high values for the similarity measures between the reconstructed ABP signals and their reference ABP signals: the Pearson correlation, root mean square error, and normalized dynamic time warping distance were 0.995, 2.236 mmHg, and 0.612 mmHg on average, respectively. The mean absolute errors of the SBP and DBP were 2.602 mmHg and 1.450 mmHg on average, respectively. Therefore, the model can reconstruct ABP signals that are highly similar to the reference ABP signals.
39
Kwon, Tae-Ho, and Ki-Doo Kim. "Machine-Learning-Based Noninvasive In Vivo Estimation of HbA1c Using Photoplethysmography Signals." Sensors 22, no. 8 (April 12, 2022): 2963. http://dx.doi.org/10.3390/s22082963.
Full textAbstract:
Glycated hemoglobin (HbA1c) is an important factor in monitoring diabetes. Since the glycated hemoglobin value reflects the average blood glucose level over 3 months, it is not affected by exercise or food intake immediately prior to measurement. Thus, it is used as the most basic measure of evaluating blood-glucose control over a certain period and predicting the occurrence of long-term complications due to diabetes. However, as the existing measurement methods are invasive, there is a burden on the measurement subject who has to endure increased blood gathering and exposure to the risk of secondary infections. To overcome this problem, we propose a machine-learning-based noninvasive estimation method in this study using photoplethysmography (PPG) signals. First, the development of the device used to acquire the PPG signals is described in detail. Thereafter, discriminative and effective features are extracted from the acquired PPG signals using the device, and a machine-learning algorithm is used to estimate the glycated hemoglobin value from the extracted features. Finally, the performance of the proposed method is evaluated by comparison with existing model-based methods.
40
Annaheim, Simon, Fabian Braun, Leah Bernhard, Amarin Pfammatter, Martin Proença, Guillaume Bonnier, Damien Ferrario, Mathieu Lemay, and René M. Rossi. "Proof-of-Concept Study for Reflectance Pulse Oximetry Using Optical-Fibre-Based Sensors." Current Directions in Biomedical Engineering 8, no. 2 (August 1, 2022): 121–24. http://dx.doi.org/10.1515/cdbme-2022-1032.
Full textAbstract:
Abstract Optical fibres enable to design of new textile-based sensors for an unobtrusive, continuous, and long-term acquisition of photoplethysmography (PPG) signals. However, no research has been done about the accuracy of measuring blood oxygen saturation (SpO2) and the feasibility of PPG-based blood pressure measurement. This proof-ofconcept study examined these aspects for a woven sensing patch applied on the forehead in five healthy participants. During a controlled desaturation study, SpO2 estimation revealed an amplitude of the root-mean-squared error (ARMS) of 3.6%, and acceptable signal quality for blood pressure estimation was achieved for 40% of all data. These results indicate that textile-based sensors can reach the required PPG signal quality to simultaneously estimate multiple vital signs, including blood pressure.
41
He, Le. "Application of Biomedical Signal Acquisition Equipment in Human Sport Heart Rate Monitoring." Journal of Medical Imaging and Health Informatics 10, no. 4 (April 1, 2020): 877–83. http://dx.doi.org/10.1166/jmihi.2020.2948.
Full textAbstract:
Aiming at exploring biomedical signal acquisition equipment used in human motion heart rate monitoring, the research on the related hardware design and signal processing method was carried out. A biomedical signal acquisition device based on photoplethysmography (PPG) is designed, and the equipment was applied to acquire PPG signals and acceleration sensor signals under different motion states. The analysis of the experimental data showed that, the fusion method of the acceleration sensing information in the motion artifact removal method is perfected. The effectiveness of the baseline drift removal algorithm, motion artifact removal algorithm and dynamic heart rate monitoring algorithm was verified by reconstructing the signal quality evaluation index. To sum up, taking MINDRAY VS-800 as a reference device, it is compared with the adaptive filtering technology in terms of signal quality, BPM detection results and algorithm complexity, and better results are finally obtained.
42
Calamanti, Chiara, Sara Moccia, Lucia Migliorelli, Marina Paolanti, and Emanuele Frontoni. "Learning-Based Screening of Endothelial Dysfunction From Photoplethysmographic Signals." Electronics 8, no. 3 (March 1, 2019): 271. http://dx.doi.org/10.3390/electronics8030271.
Full textAbstract:
Endothelial-Dysfunction (ED) screening is of primary importance to early diagnosis cardiovascular diseases. Recently, approaches to ED screening are focusing more and more on photoplethysmography (PPG)-signal analysis, which is performed in a threshold-sensitive way and may not be suitable for tackling the high variability of PPG signals. The goal of this work was to present an innovative machine-learning (ML) approach to ED screening that could tackle such variability. Two research hypotheses guided this work: (H1) ML can support ED screening by classifying PPG features; and (H2) classification performance can be improved when including also anthropometric features. To investigate H1 and H2, a new dataset was built from 59 subject. The dataset is balanced in terms of subjects with and without ED. Support vector machine (SVM), random forest (RF) and k-nearest neighbors (KNN) classifiers were investigated for feature classification. With the leave-one-out evaluation protocol, the best classification results for H1 were obtained with SVM (accuracy = 71%, recall = 59%). When testing H2, the recall was further improved to 67%. Such results are a promising step for developing a novel and intelligent PPG device to assist clinicians in performing large scale and low cost ED screening.
43
Silva, Henrique, Hugo A. Ferreira, Clemente Rocha, and Luís Monteiro Rodrigues. "Texture Analysis is a Useful Tool to Assess the Complexity Profile of Microcirculatory Blood Flow." Applied Sciences 10, no. 3 (January 30, 2020): 911. http://dx.doi.org/10.3390/app10030911.
Full textAbstract:
The quantitative assessment of cardiovascular functions is particularly complicated, especially during any physiological challenge (e.g., exercise), with physiological signals showing intricate oscillatory properties. Signal complexity is one of such properties, and reflects the adaptability of the physiological systems that generated them. However, it is still underexplored in vascular physiology. In the present study, we calculate the complexity of photoplethysmography (PPG) signals and their frequency components obtained with the wavelet transform (WT), with two analytical tools—(i) texture analysis (TA) of WT scalograms, and (ii) multiscale entropy (MSE) analysis. PPG signals were collected from twelve healthy young subjects (26.0 ± 5.0 y.o.) during a unilateral leg lowering maneuver to evoke the venoarteriolar reflex (VAR) while lying supine, with the contralateral leg remaining stationary. Results showed that TA was able to detect a decrease in complexity, viewed as an increase in texture entropy (TE), of the PPG scalograms during VAR, similarly to MSE, suggesting that a decrease in the competence of vascular regulation mechanisms might be present during VAR. Nonetheless, TA showed lower sensitivity than MSE for low frequency spectral regions. TA seems to be a promising and straightforward analytical tool for the assessment of the complexity of PPG perfusion signals.
44
Haugg, Fridolin, Mohamed Elgendi, and Carlo Menon. "Effectiveness of Remote PPG Construction Methods: A Preliminary Analysis." Bioengineering 9, no. 10 (September 20, 2022): 485. http://dx.doi.org/10.3390/bioengineering9100485.
Full textAbstract:
The contactless recording of a photoplethysmography (PPG) signal with a Red-Green-Blue (RGB) camera is known as remote photoplethysmography (rPPG). Studies have reported on the positive impact of using this technique, particularly in heart rate estimation, which has led to increased research on this topic among scientists. Therefore, converting from RGB signals to constructing an rPPG signal is an important step. Eight rPPG methods (plant-orthogonal-to-skin (POS), local group invariance (LGI), the chrominance-based method (CHROM), orthogonal matrix image transformation (OMIT), GREEN, independent component analysis (ICA), principal component analysis (PCA), and blood volume pulse (PBV) methods) were assessed using dynamic time warping, power spectrum analysis, and Pearson’s correlation coefficient, with different activities (at rest, during exercising in the gym, during talking, and while head rotating) and four regions of interest (ROI): the forehead, the left cheek, the right cheek, and a combination of all three ROIs. The best performing rPPG methods in all categories were the POS, LGI, and OMI methods; each performed well in all activities. Recommendations for future work are provided.
45
Sanguansri, Pornnapa, Nattapat Apiwong-Ngam, Athipong Ngamjarurojana, and Supab Choopun. "Development of non-invasive alcohol analyzer using Photoplethysmographytle." Journal of Physics: Conference Series 2145, no. 1 (December 1, 2021): 012059. http://dx.doi.org/10.1088/1742-6596/2145/1/012059.
Full textAbstract:
Abstract Photoplethysmography (PPG) is one of the optical signals commonly used in clinical research for measuring the vital signs. Previously, PPG is often used as an indicator for detecting blood volume changes in the micro-vascular. The advantages of PPG signal mentioned in studies are non-invasive, lower operation cost, and the simplicity of the procedure. Although some the components of the PPG signal are not fully understood, it is generally accepted that it can provide valuable information in clinical study. Thus, it is interesting for finding a relation between PPG signal and blood alcohol concentration. The objective of this study is to classify two groups of ten-volunteer: (1) group of people who consumed alcohol and (2) non-consumed alcohol, by using the difference of PPG signals in these two groups and compared the results with fuel-cell breath alcohol analyzer. A set of PPG reflection data is recorded from optical sensors including the wavelength light of the red light and the infrared light from the fingertip of individuals. In additional, the changes of each signals for distinguishing two groups of volunteers are examined. The set of data is computed and analysed to find the correlation coefficient between significant variables in statistic domain. The analysis techniques are included (1) slope of the signals over time, (2) peak to peak of the heart rate, and (3) deep of waveform valley after rotation for training generalized linear (GLM) classifiers to create classification models. The accuracy of GLM classification can be obtained up to 87.50%. This suggests that PPG technique with our lab prototype has a potential for screening test to classify people who consumed alcohol and non-consumed alcohol.
46
Rahmansyah, Azha Alvin, Satria Mandala, and Miftah Pramudyo. "Study of Classification Method to Detect Coronary Heart Disease Based On Signal Photoplethysmography (PPG)." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 4 (October 25, 2022): 2392. http://dx.doi.org/10.30865/mib.v6i4.4823.
Full textAbstract:
Coronary heart disease (CHD) is one of the deadliest diseases in the world, especially in Indonesia. This disease is caused by the accumulation of fat in blood vessels and can cause heart attacks that can endanger a person's health and safety. There are several methods for detecting CAD, such as using Electrocardiogram (ECG) signals and Photophlethysmograph (PPG) signals. However, studies that have tested machine learning classification methods to detect CAD using PPG signals are rarely found compared to detection using ECG. This study uses PPG signals taken from smartphone cameras to detect CHD, so that CHD detection is easier and affordable. To be able to diagnose CHD, machine learning assistance is needed to determine whether CHD is positive or negative. This study proposes a classification algorithm study to detect CAD. There are 3 classification methods used in this study. The three methods are KNN, SVM, and decision tree. The final results obtained in this study resulted in the best classification for KNN 81%, SVM 90%, and Decision Tree 90%. Each classification used has been carried out before and after tuning
47
Treebupachatsakul, Treesukon, Apivitch Boosamalee, Siratchakrit Shinnakerdchoke, Suejit Pechprasarn, and Nuntachai Thongpance. "Cuff-Less Blood Pressure Prediction from ECG and PPG Signals Using Fourier Transformation and Amplitude Randomization Preprocessing for Context Aggregation Network Training." Biosensors 12, no. 3 (March 4, 2022): 159. http://dx.doi.org/10.3390/bios12030159.
Full textAbstract:
This research proposes an algorithm to preprocess photoplethysmography (PPG) and electrocardiogram (ECG) signals and apply the processed signals to the context aggregation network-based deep learning to achieve higher accuracy of continuous systolic and diastolic blood pressure monitoring than other reported algorithms. The preprocessing method consists of the following steps: (1) acquiring the PPG and ECG signals for a two second window at a sampling rate of 125 Hz; (2) separating the signals into an array of 250 data points corresponding to a 2 s data window; (3) randomizing the amplitude of the PPG and ECG signals by multiplying the 2 s frames by a random amplitude constant to ensure that the neural network can only learn from the frequency information accommodating the signal fluctuation due to instrument attachment and installation; (4) Fourier transforming the windowed PPG and ECG signals obtaining both amplitude and phase data; (5) normalizing both the amplitude and the phase of PPG and ECG signals using z-score normalization; and (6) training the neural network using four input channels (the amplitude and the phase of PPG and the amplitude and the phase of ECG), and arterial blood pressure signal in time-domain as the label for supervised learning. As a result, the network can achieve a high continuous blood pressure monitoring accuracy, with the systolic blood pressure root mean square error of 7 mmHg and the diastolic root mean square error of 6 mmHg. These values are within the error range reported in the literature. Note that other methods rely only on mathematical models for the systolic and diastolic values, whereas the proposed method can predict the continuous signal without degrading the measurement performance and relying on a mathematical model.
48
Tigges, Timo, Jonas Rockstroh, Alexandru Pielmuş, Michael Klum, Aarne Feldheiser, Oliver Hunsicker, and Reinhold Orglmeister. "In-ear photoplethysmography for central pulse waveform analysis in non-invasive hemodynamic monitoring." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 587–90. http://dx.doi.org/10.1515/cdbme-2017-0122.
Full textAbstract:
AbstractIn recent years, the analysis of the photoplethys-mographic (PPG) pulse waveforms has attracted much research focus. However, the considered signals are primarily recorded at the fingertips, which suffer from reduced peripheral perfusion in situations like hypovolemia or sepsis, rendering waveform analysis infeasible. The ear canal is not affected by cardiovascular centralization and could thus prove to be an ideal alternate measurement site for pulse waveform analysis. Therefore, we developed a novel system that allows for highly accurate photoplethysmographic measurements in the ear canal. We conducted a measurement study in order to assess the signal-to-noise ratio of our developed system Hereby, we achieved a mean SNR of 40.65 dB. Hence, we could show that our system allows for highly accurate PPG recordings in the ear canal facilitating sophisticated pulse waveform analysis. Furthermore, we demonstrated that the pulse decomposition analysis is also applicable to in-ear PPG recordings.
49
Pradhapan, Paruthi, Muthukaruppan Swaminathan, Hari Krishna Salila Vijayalal Mohan, and N. Sriraam. "A Novel Detection Approach for Cardio-Respiratory Disorders Using PPG Signals." International Journal of Biomedical and Clinical Engineering 1, no. 2 (July 2012): 13–23. http://dx.doi.org/10.4018/ijbce.2012070102.
Full textAbstract:
The aim of the study was to determine the use of Photoplethysmography (PPG) as a tool for identifying cardiac and respiratory disorders using Decision tree mining technique. PPG signals were recorded from 45 healthy volunteers in the 19-22 age group. Recordings were carried out under normal, induced cardiac stress and induced apnea conditions to assess the changes in the PPG morphology under these settings. Three features, stiffness index (SI), reflection index (RI) and power ratio (PR), have been used for classification. Classification accuracy of 94.44% and 97.19% has been achieved for induced cardiac stress and induced apnea recordings respectively, using the decision tree classifier. The study indicates that PPG can be used as an effective screening tool for preliminary diagnosis of different cardiac and respiratory conditions. The results need to be validated for large datasets as well as for offline analysis of measurements from real life situations.
50
Chen, Cheng-Hsuan, Kuo-Kai Shyu, Cheng-Kai Lu, Chi-Wen Jao, and Po-Lei Lee. "Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation." Brain Sciences 11, no. 6 (May 26, 2021): 701. http://dx.doi.org/10.3390/brainsci11060701.
Full textAbstract:
The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.