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Journal articles on the topic 'Photoplethysmographic signal'

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Author: Grafiati

Published: 4 June 2021

Last updated: 30 May 2022

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1

Gircys, Rolandas, Agnius Liutkevicius, Arunas Vrubliauskas, and Egidijus Kazanavicius. "Blood Pressure Estimation Accoording to Photoplethysmographic Signal Steepness." Information Technology And Control 44, no. 4 (December 18, 2015): 443–50. http://dx.doi.org/10.5755/j01.itc.44.4.12562.

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Abstract. The purpose of this paper is to prove the assumption that there is a correlation between the systolic blood pressure and the photoplethysmographic signal steepness. A method for indirect systolic blood pressure estimation based on photoplethysmographic signal steepness is proposed in this paper. Method: It is proved that based on Hooke’s law, the steepness of pressure and volume (diameter) of pulse waves differ by a constant. The coefficient for calculating arterial blood pressure when volume pulse wave steepness is known is presented in this paper. The Windkessel model is selected for the modeling. Experimental evaluation is based on veloergometrical trials. Volume pulse wave was obtained using a photoplethysmography device that is put on a finger. Blood pressure was measured using a semi-automatic OMRON blood pressure monitor. Results: The simulation of an arterial system using the Windkessel model shows that the steepness of pressure and volume pulse waves correlate. Ten veloergometrical trials were performed during the experimental evaluation. A significant 0.855±0.025 (p < 0.001) correlation between the photoplethysmographic signal steepness and the systolic blood pressure was obtained. The calculated and measured blood pressure values vary no more than ±5mmHg. Conclusions: The results demonstrate that the photoplethysmographic signal wavefront can be successfully applied in wearable devices that can be used for constant 24 hour registration of blood pressure for both home use and clinical practice.DOI: http://dx.doi.org/10.5755/j01.itc.44.4.12562

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2

Peng, Rong-Chao, Wen-Rong Yan, Ning-Ling Zhang, Wan-Hua Lin, Xiao-Lin Zhou, and Yuan-Ting Zhang. "Investigation of Five Algorithms for Selection of the Optimal Region of Interest in Smartphone Photoplethysmography." Journal of Sensors 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/6830152.

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Smartphone photoplethysmography is a newly developed technique that can detect several physiological parameters from the photoplethysmographic signal obtained by the built-in camera of a smartphone. It is simple, low-cost, and easy-to-use, with a great potential to be used in remote medicine and home healthcare service. However, the determination of the optimal region of interest (ROI), which is an important issue for extracting photoplethysmographic signals from the camera video, has not been well studied. We herein proposed five algorithms for ROI selection: variance (VAR), spectral energy ratio (SER), template matching (TM), temporal difference (TD), and gradient (GRAD). Their performances were evaluated by a 50-subject experiment comparing the heart rates measured from the electrocardiogram and those from the smartphone using the five algorithms. The results revealed that the TM and the TD algorithms outperformed the other three as they had less standard error of estimate (<1.5 bpm) and smaller limits of agreement (<3 bpm). The TD algorithm was slightly better than the TM algorithm and more suitable for smartphone applications. These results may be helpful to improve the accuracy of the physiological parameters measurement and to make the smartphone photoplethysmography technique more practical.

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3

Jeong, Jae Hoon, Sung Min Kim, Sung Yun Park, and Sangjoon Lee. "A Study on Measurement of Photoplethysmograph Using a Smartphone Camera." Applied Mechanics and Materials 479-480 (December 2013): 137–42. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.137.

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In this study, we proposed a method for measuring photoplethysmographic using a smartphone camera. A development algorithm is consists 6 procedures. The first is to convert RGB to Gray level from a camera image, the second is to detect ROI from image, the third is to extract photoplethysmography signal from a camera image, the fourth is to filter baseline, and the last is to oversample procedure using cubic spline interpolation. The proposed algorithm has been tested using several smartphone with a person and which can effectively acquire persons PPG signal at any situation. We supposed that the proposed algorithm can easily adapt for a smartphone m-health system.

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4

Avramenko, D. V., and V. G. Andrejev. "PHOTOPLETHYSMOGRAPHIC SIGNAL SPECTRUM ANALYSIS USING MODIFIED PRONY’S METHOD." Vestnik of Ryazan State Radio Engineering University 65 (2018): 130–35. http://dx.doi.org/10.21667/1995-4565-2018-65-3-130-135.

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5

Khanoka, B., Y. Slovik, D. Landau, and M. Nitzan. "Sympathetically induced spontaneous fluctuations of the photoplethysmographic signal." Medical & Biological Engineering & Computing 42, no. 1 (January 2004): 80–85. http://dx.doi.org/10.1007/bf02351014.

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6

Massmann, Jonas, Timo Tigges, and Reinhold Orglmeister. "Continuous signal quality estimation for robust heart rate extraction from photoplethysmographic signals." Current Directions in Biomedical Engineering 6, no. 3 (September 1, 2020): 510–13. http://dx.doi.org/10.1515/cdbme-2020-3131.

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AbstractThis study presents a novel method for estimating the signal quality of photoplethysmographic (PPG) signals. For this purpose a robust classifier is implemented and evaluated by using finger- and inear-PPG. A new procedure is proposed, which uses feature reduction to determine the Mahalanobis distance of the PPG-pulses to a statistical reference model and thus facilitates a robust heart rate extraction. The evaluation of the algorithm is based on a classical binary classification using a manually annotated gold standard. For the finger-PPG a sensitivity of 86 ± 15 % and a specificity of 94 ± 13 % was achieved. Additionally, a novel classification method which is based on a continuous signal quality index is used. Pulse rate estimation errors greater than 5 BPM can be detected with a sensitivity of 91 ± 13 % and a specificity of 91 ± 15 %. Also, a functional correlation between the signal quality index and the standard deviation of the pulse rate error is shown. The proposed classifier can be used for improving the heart rate extration in pulse rate variability analysis or in the area of mobile monitoring for battery saving.

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7

Argüello-Prada, Erick Javier. "The mountaineer's method for peak detection in photoplethysmographic signals." Revista Facultad de Ingeniería Universidad de Antioquia, no. 90 (January 14, 2019): 42–50. http://dx.doi.org/10.17533/udea.redin.n90a06.

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Several efforts have been made to develop algorithms for accurate peak detection in photoplethysmographic (PPG) signals. Most of those algorithms have been specifically conceived to perform under high motion artifact and baseline drift conditions. However, little has been done regarding peak detection in low-amplitude PPG signals. In an attempt to address this issue, a simple and real-time peak detection algorithm for PPG signals was proposed. In comparison with two other well-established peak detection algorithms, the proposed method was able to achieve over than 98% sensitivity and less than 3% failed detection rate, even when the amplitude of the PPG signal dropped to 0.2 V. Still, further work is needed to improve its robustness to motion artifacts.

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8

Yavorska, Evhenia, Oksana Strembitska, Michael Strembitskyi, and Iryna Pankiv. "Development of a simulation model of a photoplethysmographic signal under psychoemotional stress." Eastern-European Journal of Enterprise Technologies 2, no. 9 (110) (April 30, 2021): 36–45. http://dx.doi.org/10.15587/1729-4061.2021.227001.

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A simulation model of a photoplethysmographic signal under psychoemotional stress taking into account the nature of signals of biological origin and stress response stages was developed. The method of constructing the simulation model is based on reconstructing the waveform and coding points of the signal taking into account the stress response curve using harmonic functions at characteristic time intervals. Using the simulation model of the photoplethysmographic signal under psychoemotional stress with previously known parameters allows validation of methods and algorithms for processing such data. It was found that in the process of simulation, it is necessary to take into account the signal frequency, random component and stress response curve. This complicates the simulation algorithm. However, using the simulation model with variable input parameters allows reproducing the signal with an emphasis on stress response stages. One of the features of the proposed model is the ability to reproduce the signal by coding points for amplitude and time intervals using harmonic functions. The relative error for the amplitude variation of the model and experimental data is 3.97 %, and for the period – 3.41 %. Calculation of Student's t-test showed a statistically insignificant difference: p=0.296 for the amplitude and p=0.275 for the period. This indicates that the simulation model takes into account the signal characteristics under stress: frequency, random component and stress response curve. Using the proposed simulation model is an adequate way to assess methods and algorithms for analyzing the state of the cardiovascular system under psychoemotional stress

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9

Georgieva-Tsaneva, Galya, Evgeniya Gospodinova, Mitko Gospodinov, and Krasimir Cheshmedzhiev. "Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals." Applied Sciences 10, no. 3 (February 5, 2020): 1051. http://dx.doi.org/10.3390/app10031051.

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This article introduces an integrated photoplethysmographic (PPG) based cardiovascular monitoring system that consists of an individually portable PPG device for recording photoplethysmographic signals and a software system with a serverless architecture for processing, storing, and analyzing the obtained signals. The portable device uses the optical plethysmography technique for measuring blood volume in blood vessels. The device was tested and validated by a comparative analysis of three photoplethysmographic signals and one Electrocardiographic signal registered simultaneously in the target subject. The comparative analysis of these signals shows insignificant deviations in the obtained results, with the mean squared error between the studied signals being less than 21 ms. This deviation cannot affect the results that were obtained from the analysis of the interval series tested. Based on this result, we assume that the detected signals with the proposed device are realistic. The designed software system processes the registered data, performs preprocessing, determines the pulse rate variability, and performs mathematical analysis of PP intervals. Two groups of subjects were studied: 42 patients with arrhythmia and 40 healthy controls. Mathematical methods for data analysis in time and frequency domain and nonlinear methods (Poincaré plots, Rescaled Range Plot, Detrended Fluctuation Analysis, and MultiFractal Detrended Fluctuation Analysis) are applied. The obtained results are presented in tabular form and some of them in graphical form. The parameters studied in the time and frequency domain, as well as with the nonlinear methods, have statistical significance (p < 0.05) and they can distinguish between the two studied groups. Visual analysis of PP intervals, based on Poincare’s nonlinear method, provides important information on the physiological status of patients, allowing for one to see at a glance the entire PP interval series and quickly detect cardiovascular disorders, if any. The photoplethysmographic data of healthy individuals and patients diagnosed with arrhythmia were recorded, processed, and examined through the system under the guidance of a cardiologist. The results were analyzed and it was concluded that this system could serve to monitor patients with cardiovascular diseases and, when the condition worsens, a signal could be generated and sent to the hospital for undertaking immediate measures to stabilize patient’s health.

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10

Pilt, Kristjan, Rain Ferenets, Kalju Meigas, Lars-Göran Lindberg, Kristina Temitski, and Margus Viigimaa. "New Photoplethysmographic Signal Analysis Algorithm for Arterial Stiffness Estimation." Scientific World Journal 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/169035.

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The ability to identify premature arterial stiffening is of considerable value in the prevention of cardiovascular diseases. The “ageing index” (AGI), which is calculated from the second derivative photoplethysmographic (SDPPG) waveform, has been used as one method for arterial stiffness estimation and the evaluation of cardiovascular ageing. In this study, the new SDPPG analysis algorithm is proposed with optimal filtering and signal normalization in time. The filter parameters were optimized in order to achieve the minimal standard deviation ofAGI, which gives more effective differentiation between the levels of arterial stiffness. As a result, the optimal low-pass filter edge frequency of 6 Hz and transitionband of 1 Hz were found, which facilitatesAGIcalculation with a standard deviation of 0.06. The study was carried out on 21 healthy subjects and 20 diabetes patients. The linear relationship(r=0.91)between each subject’s age andAGIwas found, and a linear model with regression line was constructed. For diabetes patients, the meanAGIvalue difference from the proposed modelyAGIwas found to be 0.359. The difference was found between healthy and diabetes patients groups with significance level ofP<0.0005.

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11

Kern, Fabian, and Stefan Bernhard. "Beat-to-beat blood pressure measurement from instantaneous harmonic phase-shifts in non-invasive photoplethysmographic signals." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 755–58. http://dx.doi.org/10.1515/cdbme-2017-0159.

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AbstractThe state-of-the-art blood pressure measurement is beside common cuff-based methods the cuff-less estimation of pulse-transit-time, which is the time a blood pressure wave requires to travel from left ventricle of the heart to another peripheral point in the cardiovascular system. Within this work we present a novel estimation method for cuff-less blood pressure measurement by analysing a single photoplethysmographic signal in the frequency domain. The harmonic phase-shift of the fundamental frequency and the first harmonic within the photoplethysmographic signal has proven a strong correlation of r = 0.8514 and r = 0.9315 with systolic and diastolic blood pressure respectively.

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12

Athaya, Tasbiraha, and Sunwoong Choi. "An Efficient Fingertip Photoplethysmographic Signal Artifact Detection Method: A Machine Learning Approach." Journal of Sensors 2021 (October 4, 2021): 1–18. http://dx.doi.org/10.1155/2021/9925033.

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A photoplethysmography method has recently been widely used to noninvasively measure blood volume changes during a cardiac cycle. Photoplethysmogram (PPG) signals are sensitive to artifacts that negatively impact the accuracy of many important measurements. In this paper, we propose an efficient system for detecting PPG signal artifacts acquired from a fingertip in the public healthcare database named Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) by using 11 features as the input of the random forest algorithm and classified the signals into two classes: acceptable and anomalous. A real-time algorithm is proposed to identify artifacts by using the method. The efficient Fisher score feature selection algorithm was used to order and select 11 relevant features from 19 available features that represented the PPG signal very effectively. Six machine learning algorithms (random forest, decision tree, Gaussian naïve Bayes, linear support vector machine, artificial neural network, and probabilistic neural network) were applied with the extracted features, and their classification accuracy was measured. Among them, the random forest had the best performance using only 11 out of 19 features with an accuracy of 85.68%. Our proposed method also achieved good sensitivity and specificity value of 86.57% and 85.09%, respectively. The proposed real-time algorithm can be an easy and convenient way for real-time PPG signal artifact detection using smartphones and wearable devices.

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Pilt, K., K. Meigas, R. Ferenets, K. Temitski, and M. Viigimaa. "Photoplethysmographic signal waveform index for detection of increased arterial stiffness." Physiological Measurement 35, no. 10 (September 19, 2014): 2027–36. http://dx.doi.org/10.1088/0967-3334/35/10/2027.

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14

Kuwalek, Piotr, Bartlomiej Burlaga, Waldemar Jesko, and Patryk Konieczka. "Research on methods for detecting respiratory rate from photoplethysmographic signal." Biomedical Signal Processing and Control 66 (April 2021): 102483. http://dx.doi.org/10.1016/j.bspc.2021.102483.

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15

Donida Labati, Ruggero, Vincenzo Piuri, Francesco Rundo, and Fabio Scotti. "Photoplethysmographic biometrics: A comprehensive survey." Pattern Recognition Letters 156 (April 2022): 119–25. http://dx.doi.org/10.1016/j.patrec.2022.03.006.

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16

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.

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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.

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de Pedro-Carracedo, Javier, Ana María Ugena, and Ana Pilar Gonzalez-Marcos. "Dynamical Analysis of Biological Signals with the 0–1 Test: A Case Study of the PhotoPlethysmoGraphic (PPG) Signal." Applied Sciences 11, no. 14 (July 15, 2021): 6508. http://dx.doi.org/10.3390/app11146508.

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The 0–1 test distinguishes between regular and chaotic dynamics for a deterministic system using a time series as a starting point without appealing to any state space reconstruction method. A modification of the 0–1 test allows for the determination of a more comprehensive range of signal dynamic behaviors, particularly in the field of biological signals. We report the results of applying the test and study with more details the PhotoPlethysmoGraphic (PPG) signal behavior from different healthy young subjects, although its use is extensible to other biological signals. While mainly used for heart rate and blood oxygen saturation monitoring, the PPG signal contains extensive physiological dynamics information. We show that the PPG signal, on a healthy young individual, is predominantly quasi-periodic on small timescales (short span of time concerning the dominant frequency). However, on large timescales, PPG signals yield an aperiodic behavior that can be firmly chaotic or a prior transition via an SNA (Strange Nonchaotic Attractor). The results are based on the behavior of well-known time series that are random, chaotic, aperiodic, periodic, and quasi-periodic.

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MAAOUI, CHOUBEILA, FREDERIC BOUSEFSAF, and ALAIN PRUSKI. "AUTOMATIC HUMAN STRESS DETECTION BASED ON WEBCAM PHOTOPLETHYSMOGRAPHIC SIGNALS." Journal of Mechanics in Medicine and Biology 16, no. 04 (June 2016): 1650039. http://dx.doi.org/10.1142/s0219519416500391.

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One of the goals of affective computing field is to provide to computers the ability to recognize automatically the affective state of the user in order to have more intuitive human–machine communication. This paper aims to detect automatically the stress user when he is interacting with computer. The developed system is based on instantaneous pulse rate (PR) signal extracted from imaging photoplethysmography (PPG). Seven features from time and frequency domain are extracted from PR signal and processed by learning pattern recognition systems. Two methods based on Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) are used and compared to classify the user’s emotional state. A computer application based on “Stroop color word Test” is developed to elicit emotional stress in the subject. The proposed method can achieve the overall average classification accuracy of 94.42% and 91.10% with SVM and LDA, respectively. Current results indicate that our approach is effective for stress classification.

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19

Peng, Fulai, Hongyun Liu, and Weidong Wang. "A comb filter based signal processing method to effectively reduce motion artifacts from photoplethysmographic signals." Physiological Measurement 36, no. 10 (September 3, 2015): 2159–70. http://dx.doi.org/10.1088/0967-3334/36/10/2159.

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20

Mengko, Richard, Habibur Muhaimin, and Tati Latifah R. Mengko. "Coherent Modulation Analysis of Photoplethysmographic Signal s by Time -varying Filterbank." International Journal on Electrical Engineering and Informatics 9, no. 1 (March 31, 2017): 24–41. http://dx.doi.org/10.15676/ijeei.2017.9.1.2.

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21

Mohamed Yacin, S., M. Manivannan, and V. Srinivasa Chakravarthy. "On Non-Invasive Measurement of Gastric Motility from Finger Photoplethysmographic Signal." Annals of Biomedical Engineering 38, no. 12 (July 8, 2010): 3744–55. http://dx.doi.org/10.1007/s10439-010-0113-4.

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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.

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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.

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Reali, Pierluigi, Riccardo Lolatto, Stefania Coelli, Gabriella Tartaglia, and Anna Maria Bianchi. "Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates." Sensors 22, no. 4 (February 13, 2022): 1428. http://dx.doi.org/10.3390/s22041428.

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The increasingly widespread diffusion of wearable devices makes possible the continuous monitoring of vital signs, such as heart rate (HR), heart rate variability (HRV), and breath signal. However, these devices usually do not record the “gold-standard” signals, namely the electrocardiography (ECG) and respiratory activity, but a single photoplethysmographic (PPG) signal, which can be exploited to estimate HR and respiratory activity. In addition, these devices employ low sampling rates to limit power consumption. Hence, proper methods should be adopted to compensate for the resulting increased discretization error, while diverse breath-extraction algorithms may be differently sensitive to PPG sampling rate. Here, we assessed the efficacy of parabola interpolation, cubic-spline, and linear regression methods to improve the accuracy of the inter-beat intervals (IBIs) extracted from PPG sampled at decreasing rates from 64 to 8 Hz. PPG-derived IBIs and HRV indices were compared with those extracted from a standard ECG. In addition, breath signals extracted from PPG using three different techniques were compared with the gold-standard signal from a thoracic belt. Signals were recorded from eight healthy volunteers during an experimental protocol comprising sitting and standing postures and a controlled respiration task. Parabola and cubic-spline interpolation significantly increased IBIs accuracy at 32, 16, and 8 Hz sampling rates. Concerning breath signal extraction, the method holding higher accuracy was based on PPG bandpass filtering. Our results support the efficacy of parabola and spline interpolations to improve the accuracy of the IBIs obtained from low-sampling rate PPG signals, and also indicate a robust method for breath signal extraction.

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Millán, César A., Nathalia A. Girón, and Diego M. Lopez. "Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification." International Journal of Environmental Research and Public Health 17, no. 2 (January 13, 2020): 498. http://dx.doi.org/10.3390/ijerph17020498.

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Atrial Fibrillation (AF) is the most common cardiac arrhythmia found in clinical practice. It affects an estimated 33.5 million people, representing approximately 0.5% of the world’s population. Electrocardiogram (ECG) is the main diagnostic criterion for AF. Recently, photoplethysmography (PPG) has emerged as a simple and portable alternative for AF detection. However, it is not completely clear which are the most important features of the PPG signal to perform this process. The objective of this paper is to determine which are the most relevant features for PPG signal analysis in the detection of AF. This study is divided into two stages: (a) a systematic review carried out following the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) statement in six databases, in order to identify the features of the PPG signal reported in the literature for the detection of AF, and (b) an experimental evaluation of them, using machine learning, in order to determine which have the greatest influence on the process of detecting AF. Forty-four features were found when analyzing the signal in the time, frequency, or time–frequency domains. From those 44 features, 27 were implemented, and through machine learning, it was found that only 11 are relevant in the detection process. An algorithm was developed for the detection of AF based on these 11 features, which obtained an optimal performance in terms of sensitivity (98.43%), specificity (99.52%), and accuracy (98.97%).

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Akishin, A. D., A. P. Nikolaev, and A. V. Pisareva. "PPG System Development for the Organism Physiological Parameters Monitoring with Artificial Intelligence Technologies." Journal of Physics: Conference Series 2096, no. 1 (November 1, 2021): 012187. http://dx.doi.org/10.1088/1742-6596/2096/1/012187.

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Abstract Monitoring such health parameters as cardiac rate (CR), respiration rate (RR), blood pressure (BP), degree of oxygen in blood (SpO2), body temperature and other requires careful approach to design and development of medical devices. New non-invasive methods introduced in measuring human physiological parameters based on photoplethysmography (PPG) demonstrated their significant potential in monitoring the state of an organism, but their use in wearable devices is largely hampered by exposure to motion artifacts. This article presents a device for photoplethysmographic studies using various adaptive algorithms for processing the registered signals. The work uses artificial intelligence technologies to monitor the heart rate exposed to external mechanical and electrical interference worsening accuracy characteristics of the system. Besides, system architecture was developed, and a device model was manufactured, which made it possible to measure the optimal algorithm for digital signal processing. When using the PPG system, methods of adaptive signal processing based on Wiener filters, filters on the method of least squares (MLS) and Kalman filtering were used. To ensure heart rate monitoring with the given accuracy, studies were performed with participation of volunteers, and analysis was carried out of the results of various signal processing algorithms operation. In the course of experimental studies, a method was proposed to estimate the heart rate calculation accuracy and to analyze the external noise filtering efficiency by adaptive algorithms. PPG designed and developed system made it possible to monitor the heart rate with the given accuracy, control the organism current state and could be used as a means of cardiovascular disease diagnostics.

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Ugnell, H., and P. Å. Öberg. "The time-variable photoplethysmographic signal; dependence of the heart synchronous signal on wavelength and sample volume." Medical Engineering & Physics 17, no. 8 (December 1995): 571–78. http://dx.doi.org/10.1016/1350-4533(95)00008-b.

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27

de Pedro-Carracedo, Javier, David Fuentes-Jimenez, Ana María Ugena, and Ana Pilar Gonzalez-Marcos. "Phase Space Reconstruction from a Biological Time Series: A Photoplethysmographic Signal Case Study." Applied Sciences 10, no. 4 (February 20, 2020): 1430. http://dx.doi.org/10.3390/app10041430.

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In the analysis of biological time series, the state space is comprised of a framework for the study of systems with presumably deterministic and stationary properties. However, a physiological experiment typically captures an observable that characterizes the temporal response of the physiological system under study; the dynamic variables that make up the state of the system at any time are not available. Only from the acquired observations should state vectors be reconstructed to emulate the different states of the underlying system. This is what is known as the reconstruction of the state space, called the phase space in real-world signals, in many cases satisfactorily resolved using the method of delays. Each state vector consists of m components, extracted from successive observations delayed a time τ . The morphology of the geometric structure described by the state vectors, as well as their properties depends on the chosen parameters τ and m. The real dynamics of the system under study is subject to the correct determination of the parameters τ and m. Only in this way can be deduced features have true physical meaning, revealing aspects that reliably identify the dynamic complexity of the physiological system. The biological signal presented in this work, as a case study, is the photoplethysmographic (PPG) signal. We find that m is five for all the subjects analyzed and that τ depends on the time interval in which it is evaluated. The Hénon map and the Lorenz flow are used to facilitate a more intuitive understanding of the applied techniques.

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Masinelli, Giulio, Fabio Dell’Agnola, Adriana Arza Valdés, and David Atienza. "SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices." Sensors 21, no. 8 (April 13, 2021): 2725. http://dx.doi.org/10.3390/s21082725.

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The photoplethysmographic (PPG) signal is an unobtrusive blood pulsewave measure that has recently gained popularity in the context of the Internet of Things. Even though it is commonly used for heart rate detection, it has been lately employed on multimodal health and wellness monitoring applications. Unfortunately, this signal is prone to motion artifacts, making it almost useless in all situations where a person is not entirely at rest. To overcome this issue, we propose SPARE, a spectral peak recovery algorithm for PPG signals pulsewave reconstruction. Our solution exploits the local semiperiodicity of the pulsewave signal, together with the information about the cardiac rhythm provided by an available simultaneous ECG, to reconstruct its full waveform, even when affected by strong artifacts. The developed algorithm builds on state-of-the-art signal decomposition methods, and integrates novel techniques for signal reconstruction. Experimental results are reported both in the case of PPG signals acquired during physical activity and at rest, but corrupted in a systematic way by synthetic noise. The full PPG waveform reconstruction enables the identification of several health-related features from the signal, showing an improvement of up to 65% in the detection of different biomarkers from PPG signals affected by noise.

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Palmeri, Lynn C., Meir Nitzan, Gideon Gradwohl, Yehuda Shapir, and Robert Koppel. "Changes in Photoplethysmographic Signal Characteristics after Surgical Repair of Neonatal Aortic Coarctation." Pediatrics 137, Supplement 3 (February 2016): 479A. http://dx.doi.org/10.1542/peds.137.supplement_3.479a.

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Sinchai, Sakkarin, Pattana Kainan, Paramote Wardkein, and Jeerasuda Koseeyaporn. "A Photoplethysmographic Signal Isolated From an Additive Motion Artifact by Frequency Translation." IEEE Transactions on Biomedical Circuits and Systems 12, no. 4 (August 2018): 904–17. http://dx.doi.org/10.1109/tbcas.2018.2829708.

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Pilt, K., K. Meigas, R. Ferenets, and J. Kaik. "Photoplethysmographic signal processing using adaptive sum comb filter for pulse delay measurement." Estonian Journal of Engineering 16, no. 1 (2010): 78. http://dx.doi.org/10.3176/eng.2010.1.08.

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32

Danushka, Nadun. "PS 08-19 STUDY ON VARIATIONS OF PHOTOPLETHYSMOGRAPHIC (PPG) SIGNAL IN HYPERTENSION." Journal of Hypertension 34, Supplement 1 (September 2016): e298. http://dx.doi.org/10.1097/01.hjh.0000500714.05222.53.

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Shikhmamedov, T. R., and B. I. Podlepetskii. "Procedure for identifying pulse wave parameters in computer processing of photoplethysmographic signal." Measurement Techniques 36, no. 7 (July 1993): 827–29. http://dx.doi.org/10.1007/bf00981665.

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Johansson, A., and P. Å. Öberg. "Estimation of respiratory volumes from the photoplethysmographic signal. Part I: experimental results." Medical & Biological Engineering & Computing 37, no. 1 (January 1999): 42–47. http://dx.doi.org/10.1007/bf02513264.

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Sharma, Sunil, Paul Mather, Jimmy T. Efird, Daron Kahn, Mohammed Cheema, Sharon Rubin, Gordon Reeves, Raphael Bonita, Raymond Malloy, and David J. Whellan. "Photoplethysmographic Signal to Screen Sleep-Disordered Breathing in Hospitalized Heart Failure Patients." JACC: Heart Failure 3, no. 9 (September 2015): 725–31. http://dx.doi.org/10.1016/j.jchf.2015.04.015.

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36

Foo, J. Y. A., S. J. Wilson, G. R. Williams, M. Harris, and D. M. Cooper. "Motion artefact reduction of the photoplethysmographic signal in pulse transit time measurement." Australasian Physics & Engineering Sciences in Medicine 27, no. 4 (December 2004): 165–73. http://dx.doi.org/10.1007/bf03178645.

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37

Jobbágy, A. "Using photoplethysmographic signal for increasing the accuracy of indirect blood pressure measurement." Proceedings of the Estonian Academy of Sciences. Engineering 10, no. 2 (2004): 110. http://dx.doi.org/10.3176/eng.2004.2.05.

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38

Liu, Jing, Shu Ming Ye, Hang Chen, Xuan Wang, and Xiu Quan Fu. "Cardiovascular Multi-Parameter Monitoring System during Surgery." Advanced Materials Research 341-342 (September 2011): 646–50. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.646.

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Cardiovascular Monitoring[1], which is important evidence used to analyze the therapeutic efficacy, plays a significant role in the operation. A multi-parameter monitoring system is introduced in this paper. The system not only detects initial physiologic signals like photoplethysmographic pulse signal, blood pressure and electrocardiogram signal, but also extracts cardiovascular parameters, including the amplitude of photoplethysmogram, area ratio, pulse beat interval, pulse decay time constant, etc. The operating principle of the system, hardware composition, a flow chart of software module, direction of data flow and algorithm for extracting parameters are introduced. Finally, a validation clinical experiment was undertaken, and results confirmed that the system realized real-time monitoring of cardiovascular parameters, which reflected the variation of cardiovascular system during surgery and could assist doctors with drug administration.

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de Pedro-Carracedo, Javier, David Fuentes-Jimenez, Ana María Ugena, and Ana Pilar Gonzalez-Marcos. "Transcending Conventional Biometry Frontiers: Diffusive Dynamics PPG Biometry." Sensors 21, no. 16 (August 23, 2021): 5661. http://dx.doi.org/10.3390/s21165661.

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This paper presents the first photoplethysmographic (PPG) signal dynamic-based biometric authentication system with a Siamese convolutional neural network (CNN). Our method extracts the PPG signal’s biometric characteristics from its diffusive dynamics, characterized by geometric patterns in the (p,q)-planes specific to the 0–1 test. PPG signal diffusive dynamics are strongly dependent on the vascular bed’s biostructure, unique to each individual. The dynamic characteristics of the PPG signal are more stable over time than its morphological features, particularly in the presence of psychosomatic conditions. Besides its robustness, our biometric method is anti-spoofing, given the complex nature of the blood network. Our proposal trains using a national research study database with 40 real-world PPG signals measured with commercial equipment. Biometric system results for input data, raw and preprocessed, are studied and compared with eight primary biometric methods related to PPG, achieving the best equal error rate (ERR) and processing times with a single attempt, among all of them.

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Přibil, Jiří, Anna Přibilová, and Ivan Frollo. "Physiological Impact of Vibration and Noise in an Open-Air Magnetic Resonance Imager: Analysis of a PPG Signal of an Examined Person." Proceedings 42, no. 1 (November 14, 2019): 14. http://dx.doi.org/10.3390/ecsa-6-06631.

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The paper represents a preliminary analysis of the physiological and psychological impact of vibration and acoustic noise on a person examined by a low-field magnetic resonance imaging (MRI) tomograph. First, a methodology for the measurement of different signals of a tested person was found. The main investigation consists of a parallel heart rate and blood pressure measurement using a photoplethysmographic (PPG) optical sensor and standard portable blood pressure monitors. The recorded PPG signal is filtered and processed to obtain a clean waveform used to determine an instantaneous heart rate. Different types of portable blood pressure monitors are tested and compared to choose the best one for further experiments.

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Zheng, Xiaoyu, Vincent M. Dwyer, Laura A. Barrett, Mahsa Derakhshani, and Sijung Hu. "Adaptive notch-filtration to effectively recover photoplethysmographic signals during physical activity." Biomedical Signal Processing and Control 72 (February 2022): 103303. http://dx.doi.org/10.1016/j.bspc.2021.103303.

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42

Liu, Shing-Hong, Jia-Jung Wang, Wenxi Chen, Kuo-Li Pan, and Chun-Hung Su. "Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement." Applied Sciences 10, no. 4 (February 21, 2020): 1476. http://dx.doi.org/10.3390/app10041476.

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Photoplethysmography (PPG) has been extensively employed to acquire some physiological parameters such as heart rate, oxygen saturation, and blood pressure. However, PPG signals are frequently corrupted by motion artifacts and baseline wandering, especially for the reflective PPG sensor. Several different algorithms have been studied for determining the signal quality of PPG by the characteristic parameters of its waveform and the rule-based methods. The levels of signal quality usually were defined by the manual operations. Thus, whether the good PPG waveforms are enough to increase the accuracy of the measurement is still a subjective issue. The aim of this study is to use a fuzzy neural network to determine the signal quality indexes (SQI) of PPG pulses measured by the impedance cardiography. To test the algorithm performance, the beat-to-beat stroke volumes (SV) were measured with our device and the medis® CS 2000, synchronously. A total of 1466 pulses from 10 subjects were used to validate our algorithm in which the SQIs of 1007 pulses were high, those of 71 pulses were in the middle, and those of 388 pulses were low. The total error of SV measurement was −18 ± 22.0 mL. The performances of the classification were that the sensitivity and specificity for the 1007 pulses with the high SQIs were 0.81 and 0.90, and the error of SV measurement was 6.4 ± 12.8 mL. The sensitivity and specificity for the 388 pulses with the low SQIs were 0.84 and 0.93, while the error of SV measurement was 30.4 ± 3.6 mL. The results show that the proposed algorithm could be helpful in choosing good-quality PPG pulses to increase the accuracy of SV measurement in the impedance plethysmography.

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BHAT, SHREYA, MUHAMMAD ADAM, YUKI HAGIWARA, and EDDIE Y. K. NG. "THE BIOPHYSICAL PARAMETER MEASUREMENTS FROM PPG SIGNAL." Journal of Mechanics in Medicine and Biology 17, no. 07 (November 2017): 1740005. http://dx.doi.org/10.1142/s021951941740005x.

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Early investigation on blood circulation by Hertzman (1937) leads to the observation of vital body signs such as respiration rate, heart rate (HR), blood oxygenation and vascular assessment using photoplethysmographic (PPG) device. PPG is a noninvasive, painless optical technique used to monitor the pulsations linked to alteration in the blood volume. The PPG waveform is a summation of pulsatile and nonpulsatile components and contains useful information about the physiological systems. With the breakthrough in technology and development of powerful analytical tools, PPG devices are constantly being used in advanced medical equipments such as smart-watches and smart-wristbands for HR monitoring, pulse oximeters for measuring respiratory rate and noncontact PPG device for blood oxygen saturation measurement. This paper presents description on PPG and its characteristic waveform and working principle. It also includes brief explanation on nonlinear analysis of PPG signals and salient applications of PPG followed by its advantages and limitations.

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Pielmuş, Alexandru-Gabriel, Dennis Osterland, Michael Klum, Timo Tigges, Aarne Feldheiser, Oliver Hunsicker, and Reinhold Orglmeister. "Correlation of arterial blood pressure to synchronous piezo, impedance and photoplethysmographic signal features." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 749–53. http://dx.doi.org/10.1515/cdbme-2017-0158.

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AbstractIn this paper we investigate which pulse wave pick-up technologies are well suited for blood pressure trend estimation. We use custom built hardware to acquire electrocardiographic, applanation-tonometric, photo- and impedance-plethysmographic signals during low intensity workouts. Beat-to-beat features and pulse wave runtimes are correlated to the reference arterial blood pressure. Temporal lag adjustment is performed to determine the latency of feature response. Best results are obtained for systolic arterial blood pressure. These suggest that every subject has a range of well-performing features, but it is not consistent among all. Spearman Rho values reach in excess of 0.8, with their significance being validated by p-values lower than 0.01.

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Hickey, M., J. P. Phillips, and P. A. Kyriacou. "The effect of vascular changes on the photoplethysmographic signal at different hand elevations." Physiological Measurement 36, no. 3 (February 5, 2015): 425–40. http://dx.doi.org/10.1088/0967-3334/36/3/425.

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Enr$iacute$quez, Rolando Hong, Miguel Sauti$eacute$ Castellanos, Jersys Falc$oacute$n Rodr$iacute$guez, and Jos$eacute$ Luis Hern$aacute$ndez C$aacute$ceres. "Analysis of the photoplethysmographic signal by means of the decomposition in principal components." Physiological Measurement 23, no. 3 (June 13, 2002): N17—N29. http://dx.doi.org/10.1088/0967-3334/23/3/402.

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Sommermeyer, Dirk, Ding Zou, Joachim H. Ficker, Winfried Randerath, Christoph Fischer, Thomas Penzel, Bernd Sanner, Jan Hedner, and Ludger Grote. "Detection of cardiovascular risk from a photoplethysmographic signal using a matching pursuit algorithm." Medical & Biological Engineering & Computing 54, no. 7 (November 4, 2015): 1111–21. http://dx.doi.org/10.1007/s11517-015-1410-8.

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Islam, Mohammad Tariqul, Ishmam Zabir, Sk Tanvir Ahamed, Md Tahmid Yasar, Celia Shahnaz, and Shaikh Anowarul Fattah. "A time-frequency domain approach of heart rate estimation from photoplethysmographic (PPG) signal." Biomedical Signal Processing and Control 36 (July 2017): 146–54. http://dx.doi.org/10.1016/j.bspc.2017.03.020.

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Nilsson, L., A. Johansson, and S. Kalman. "Respiratory variations in the reflection mode photoplethysmographic signal. Relationships to peripheral venous pressure." Medical & Biological Engineering & Computing 41, no. 3 (May 2003): 249–54. http://dx.doi.org/10.1007/bf02348428.

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Nitzan, M., J. J. Vatine, A. Babchenko, B. Khanokh, J. Tsenter, and J. Stessman. "Simultaneous Measurement of the Photoplethysmographic Signal Variability in the Right and Left Hands." Lasers in Medical Science 13, no. 3 (October 1, 1998): 189–95. http://dx.doi.org/10.1007/s101030050073.

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