Relevant bibliographies by topics / Photoplethysmographic signal

Academic literature on the topic 'Photoplethysmographic signal'

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Published: 4 June 2021
Last updated: 30 May 2022

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Journal articles on the topic "Photoplethysmographic signal"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Photoplethysmographic signal"

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Li, Kejia. "Wireless reflectance pulse oximeter design and photoplethysmographic signal processing." Thesis, Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/4143.

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Odinsdottir, Gudny Björk, and Jesper Larsson. "Deep Learning Approach for Extracting Heart Rate Variability from a Photoplethysmographic Signal." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-21368.

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Photoplethysmography (PPG) is a method to detect blood volume changes in every heartbeat. The peaks in the PPG signal corresponds to the electrical impulses sent by the heart. The duration between each heartbeat varies, and these variances are better known as heart rate variability (HRV). Thus, finding peaks correctly from PPG signals provides the opportunity to measure an accurate HRV. Additional research indicates that deep learning approaches can extract HRV from a PPG signal with significantly greater accuracy compared to other traditional methods. In this study, deep learning classifiers were built to detect peaks in a noise-contaminated PPG signal and to recognize the performed activity during the data recording. The dataset used in this study is provided by the PhysioBank database consisting of synchronized PPG-, acceleration- and gyro data. The models investigated in this study were limited toa one-layer LSTM network with six varying numbers of neurons and four different window sizes. The most accurate model for the peak classification was the model consisting of 256 neurons and a window size of 15 time steps, with a Matthews correlation coefficient (MCC) of 0.74. The model consisted of64 neurons and a window duration of 1.25 seconds resulted in the most accurate activity classification, with an MCC score of 0.63. Concludingly, more optimization of a deep learning approach could lead to promising accuracy on peak detection and thus an accurate measurement of HRV. The probable cause for the low accuracy of the activity classification problem is the limited data used in this study.
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Johnston, William S. "Development of a signal processing library for extraction of SpO2, HR, HRV, and RR from photoplethysmographic waveforms." Worcester, Mass. Worcester Polytechnic Institute, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-073106-130906/.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: wearable medical sensors; arterial oxygen saturation; software development; embedded systems; heart rate; respiration rate; heart rate variability; pulse oximetry; digital signal processing Includes bibliographical references (leaves 125-133).
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Alomari, Abdul-Hakeem Hussein Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Spectral analysis of arterial blood prssure and stroke volume variability: the role of Calcium channel blockers and sensitizers." Publisher:University of New South Wales. Electrical Engineering & Telecommunications, 2008. http://handle.unsw.edu.au/1959.4/43923.

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In this thesis, we included results from two studies. The first one considered the effects of the blood volume changes, during blood donation, on the heart rate variability (HRV) measured, non-invasively, form electrocardiographic (ECG) and photoplethysmographic (PPG) signals. Our results showed that, during blood donation, there were no significant changes in the pulsatile area of PPG signal, while heart rate increased. No significant changes were noticed in HRV extracted from both signals. Error analysis between the HRV extracted from ECG and peak interval variability (PIV) suggested that the error during blood donation was increased which means that the use of PIV extracted from PPG signal, used as a replacement diagnostic tool in clinical applications, needs further investigations and should be carefully studied in non-stationary cardiovascular situations such as blood donation. The imbalance between the two branches of the autonomic nervous system, sympathetic and parasympathetic, vagal, may result in a harmful activation of myocardial tissues which cause arrhythmias and sudden cardiac death. Although the study of the sympathovagal balance have been attracting many researchers, further studies are needed to elucidate the effects of many kinds of drugs on the autonomic modulation of the cardiac muscle, specifically, the cells of sinoatrial (SA) node. The aim of the second part of this thesis was to assess the effects of calcium channel blocker (Verapamil), calcium channel sensitizer (Levosimendan), calcium chloride (CaCl2), the combinations of verapamil/ CaCl2, levosimendan/ CaCl2, and noradrenaline infusion on beat-to-beat cardiovascular variability represented, in this research, by systolic blood pressure variability (SBPV), and stroke volume variability (SVV) signals. We used Fat Fourier Transform (FFT) to evaluate the power spectral density of the fluctuations in both signals to evaluate the effects of short-term treatments with those drugs on the sympathovagal balance in normal rats. Then, we compared the spectra obtained from SBPV and SVV to decide which of these fluctuations along with corresponding spectrum was more able to provide a clear feedback about the autonomic nervous system. Our data suggests that there were a significant correlations between low- (LF), mid- (MF), and high-frequency (HF) spectra obtained from SBPV and SVV except between the HF spectra estimated from after the infusion of levosimendan where a poor correlation (r = 0.530, p = 0.281) was noticed. This that both HF components obtained provide different information regarding the autonomic nervous system modulation of the SA node cells, while the results obtained from the rest of experiments showed that both signals provide same information about the modulation of sympathetic and parasympathetic tone due to all stages of different drugs infusion studied in this thesis. Besides that, we found that both spectra may be used to track the fluctuations in the cardiac output as a result of the drugs infusion.
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Cherif, Safa. "Effective signal processing methods for robust respiratory rate estimation from photoplethysmography signal." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2018. http://www.theses.fr/2018IMTA0094/document.

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Le photopléthysmogramme (PPG) est un signal optique acquis à partir de l’oxymètre de pouls, dont l’usage principal consiste à mesurer la saturation en oxygène. Avec le développement des technologies portables, il est devenu la technique de base pour la surveillance de l’activité cardio-respiratoire des patients et la détection des anomalies. En dépit de sa simplicité d'utilisation, le déploiement de cette technique reste encore limité pour deux principales raisons : 1. L’extrême sensibilité du signal aux distorsions. 2. La non-reproductibilité entre les sujets et pour les mêmes sujets, en raison de l'âge et des conditions de santé. L’objectif de cette thèse est le développement de méthodes robustes et universelles afin d’avoir une estimation précise de la fréquence respiratoire (FR) indépendamment de la variabilité intra et interindividuelle du PPG. Plusieurs contributions originales en traitement statistiques du signal PPG sont proposées. En premier lieu, une méthode adaptative de détection des artefacts basée sur la comparaison de modèle a été développée. Des tests par la technique Random Distortion Testing sont introduits pour détecter les pulses de PPG avec des artefacts. En deuxième lieu, une analyse de plusieurs méthodes spectrales d’estimation de la FR est proposée. Afin de mettre en évidence la robustesse des méthodes proposées face à la variabilité du signal, plusieurs tests ont été effectués sur deux bases de données avec de différentes tranches d'âge et des différents modes respiratoires. En troisième lieu, un indice de qualité respiratoire spectral (SRQI) est conçu dans le but de communiquer au clinicien que les valeurs fiables de la FR avec un certain degré de confiance
One promising area of research in clinical routine involves using photoplethysmography (PPG) for monitoring respiratory activities. PPG is an optical signal acquired from oximeters, whose principal use consists in measuring oxygen saturation. Despite its simplicity of use, the deployment of this technique is still limited because of the signal sensitivity to distortions and the non-reproducibility between subjects, but also for the same subject, due to age and health conditions. The main aim of this work is to develop robust and universal methods for estimating accurate respiratory rate regardless of the intra- and inter-individual variability that affects PPG features. For this purpose, firstly, an adaptive artefact detection method based on template matching and decision by Random Distortion Testing is introduced for detecting PPG pulses with artefacts. Secondly, an analysis of several spectral methods for Respiratory Rate (RR) estimation on two different databases, with different age ranges and different respiratory modes, is proposed. Thirdly, a Spectral Respiratory Quality Index (SRQI) is attributed to respiratory rate estimates, in order that the clinician may select only RR values with a large confidence scale. Promising results are found for two different databases
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Schäck, Tim [Verfasser], Abdelhak M. [Akademischer Betreuer] Zoubir, D. Robert [Akademischer Betreuer] Iskander, and Michael [Akademischer Betreuer] Muma. "Photoplethysmography-Based Biomedical Signal Processing / Tim Schäck ; Abdelhak M. Zoubir, D. Robert Iskander, Michael Muma." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2019. http://d-nb.info/1176702009/34.

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Patancheru, Govardhan Reddy. "Wearable Heart Rate Measuring Unit." Thesis, Mittuniversitetet, Avdelningen för elektronikkonstruktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-23351.

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Despite having the numerous evolved heart rate measuring devices and progress in their development over the years, there always remain the challenges of modern signal processing implementation by a comparatively small size wearable device. This thesis paper presents a wearable reflectance photoplethysmography (PPG) sensor system for measuring the heart rate of a user both in steady and moving states. The size and, power consumption of the device are considered while developing, to ensure an easy deployment of the unit at the measuring site and the ability to power the entire unit with a battery .The selection of both the electronic circuits and signal processing techniques is based on their sensitivity to PPG signals, robustness against noise inducing artifacts and miniaturization of the entire measuring unit. The entire signal chain operates in the discrete-time, which allows the entire signal processing to be implemented in firmware on an embedded microprocessor. The PPG sensor system is implemented on a single PCB that consumes around 7.5mW of power. Benchmarking tests with standard heart rate measuring devices reveal that the developed measurement unit (combination of the PPG sensor system, and inertial measurement unit (IMU) developed in-house at Acreo Swedish ICT, and a battery) is comparable to the devices in detecting heart rate even in motion artifacts environment. This thesis work is carried out in Acreo Swedish ICT, Gothenburg, Sweden in collaboration with MidSweden University, Sundsvall, Department of Electronics Design. This report can be used as ground work for future development of wearable heart rate measuring units at Acreo Swedish ICT.
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Alghoul, Karim. "Heart Rate Variability Extraction from Video Signals." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33003.

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Heart Rate Variability (HRV) analysis has been garnering attention from researchers due to its wide range of applications. Medical researchers have always been interested in Heart Rate (HR) and HRV analysis, but nowadays, investigators from variety of other fields are also probing the subject. For instance, variation in HR and HRV is connected to emotional arousal. Therefore, knowledge from the fields of affective computing and psychology, can be employed to devise machines that understand the emotional states of humans. Recent advancements in non-contact HR and HRV measurement techniques will likely further boost interest in emotional estimation through . Such measurement methods involve the extraction of the photoplethysmography (PPG) signal from the human's face through a camera. The latest approaches apply Independent Component Analysis (ICA) on the color channels of video recordings to extract a PPG signal. Other investigated methods rely on Eulerian Video Magnification (EVM) to detect subtle changes in skin color associated with PPG. The effectiveness of the EVM in HR estimation has well been established. However, to the best of our knowledge, EVM has not been successfully employed to extract HRV feature from a video of a human face. In contrast, ICA based methods have been successfully used for HRV analysis. As we demonstrate in this thesis, these two approaches for HRV feature extraction are highly sensitive to noise. Hence, when we evaluated them in indoor settings, we obtained mean absolute error in the range of 0.012 and 28.4. Therefore, in this thesis, we present two approaches to minimize the error rate when estimating physiological measurements from recorded facial videos using a standard camera. In our first approach which is based on the EVM method, we succeeded in extracting HRV measurements but we could not get rid of high frequency noise, which resulted in a high error percentage for the result of the High frequency (HF) component. Our second proposed approach solved this issue by applying ICA on the red, green and blue (RGB) colors channels and we were able to achieve lower error rates and less noisy signal as compared to previous related works. This was done by using a Buterworth filter with the subject's specific HR range as its Cut-Off. The methods were tested with 12 subjects from the DISCOVER lab at the University of Ottawa, using artificial lights as the only source of illumination. This made it a challenge for us because artificial light produces HF signals which can interfere with the PPG signal. The final results show that our proposed ICA based method has a mean absolute error (MAE) of 0.006, 0.005, 0.34, 0.57 and 0.419 for the mean HR, mean RR, LF, HF and LF/HF respectively. This approach also shows that these physiological parameters are highly correlated with the results taken from the electrocardiography (ECG).
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Chang, Serene Hsi-Lin. "Clinical evaluation of a new optical fibre method of measuring oxygen saturation using photoplethysmograph signals reflected from internal tissues." Thesis, Queen Mary, University of London, 2013. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8719.

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Traditional methods of measuring oxygen saturation, e.g. pulse oximetry, depend on an adequate peripheral circulation and have a 20–30 second lag time before readings are obtained. This was a series of evaluations of novel optical probes, designed to measure oxygen saturation using fibreoptic technology directly from internal organs including the brain, oesophagus and organs with splanchnic circulations. A series of pilot studies were proposed and research ethics approval obtained to carry out studies in humans, under general anaesthesia, using these probes. Innovative reflectance probes were designed specifically for each of the four applications, so as to obtain potentially useful signals needed for signal processing, analysis and evaluation. Signals were successfully obtained from the brain, oesophagus and splanchnic region in almost all of the patients recruited. Good quality photoplethysmograph signals were recorded and these were translated into clinically meaningful values of oxygen saturation comparable to traditional methods of pulse oximetry. Overall, the signals were prone to movement artefacts as well as occasional interference from surgical diathermy and other sources. Nonetheless, the probes could prove to be a useful alternative to conventional external transmittance pulse oximetry methods as well as providing useful information regarding regional perfusion and oxygenation. The success of these pilot studies will form the basis of more research in the area and further development of such probes on the medical engineering front.
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Uggla, Lingvall Kristoffer. "Remote heart rate estimation by evaluating measurements from multiple signals." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210303.

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Heart rate can say a lot about a person's health. While most conventional methods for heart rate measurement require contact with the subject, these are not always applicable. In this thesis, a non-invasive method for pulse detection is implemented and analyzed. Different signals from the color of the forehead—including the green channel, the hue channel and different ICA and PCA components—are inspected, and their resulted heart rates are weighted together according to the significance of their FFT peaks. The system is tested on videos with different difficulties regarding the amount of movement and setting of the scene. The results show that the approach of weighting measurements from different signals together has great potential. The system in this thesis, however, does not perform very well on videos with a lot of movement because of motion noise. Though, with better, less noisy signals, good results can be expected.
En människas puls säger en hel del om dennes hälsa. För att mäta pulsenanvänds vanligtvis metoder som vidrör människan, vilket iblandär en nackdel. I det här examensarbetet tas en metod för pulsmätningpå avstånd fram, som endast använder klipp från en vanlig videokamera. Färgen i pannan mäts och utifrån den genereras flera signalersom analyseras, vilket resulterar i olika mätvärden för pulsen. Genomatt värdera dessa mätvärden med avseende på hur tydliga signalernaär, beräknas ett viktat medelvärde som ett slutgiltigt estimat på medelpulsen. Metoden testas på videoklipp med varierande svårighetsgrad,beroende på hur mycket rörelser som förekommer och på vilketavstånd från kameran försökspersonen står. Resultaten visar att metodenhar mycket god potential och att man kan man förvänta sig finaresultat med bättre, mindre brusiga signaler.
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Books on the topic "Photoplethysmographic signal"

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John, Allen, and Panicos A. Kyriacou. Photoplethysmography: Technology, Signal Analysis and Applications. Elsevier Science & Technology Books, 2021.

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Kamal, Adel Abdul Rahim. Signal analysis of blood flow in skin: Analysis of... signals acquired by photoplethysmograph and piezoclectricplethysmograph on investigation of autonomic nervous functions. This has led to the prediction of time of ovulation in healthy females. Bradford, 1987.

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Zarneh, Alexander Tahmassian. An instrument for on-line autonomic function testing: Design, construction and application of a microcomputer based data acquisition and analysis system used for study of the photoplethysmograph and heart rate variability signals of healthy and diseased people. Bradford, 1985.

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Book chapters on the topic "Photoplethysmographic signal"

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Xiang, Chen, Huang Yuanyuan, and Li Jin. "Correlation between Variability of Parameters in Photoplethysmographic Signal." In IFMBE Proceedings, 402–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29305-4_107.

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Pilt, Kristjan, Sandra Silluta, Kristina Kööts, Deniss Karai, Kalju Meigas, and Margus Viigimaa. "Investigation of photoplethysmographic signal augmentation index estimation differences between fingers." In EMBEC & NBC 2017, 819–22. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5122-7_205.

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Pilt, K., K. Meigas, K. Temitski, and M. Viigimaa. "Second derivative analysis of forehead photoplethysmographic signal in healthy volunteers and diabetes patients." In IFMBE Proceedings, 410–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29305-4_109.

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Solosenko, A., and V. Marozas. "Automatic Extrasystole Detection Using Photoplethysmographic Signals." In IFMBE Proceedings, 985–88. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00846-2_244.

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Li, Pin-Lu, Shao-Hung Lu, Kang-Ping Lin, and Cheng-Lun Tsai. "Photoplethysmographic Signals Measured at the Nose." In IFMBE Proceedings, 204–11. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30636-6_29.

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Li, Dazhou, Hai Zhao, Sinan Li, and Huanxia Zheng. "A New Representation of Photoplethysmography Signal." In Wireless Algorithms, Systems, and Applications, 279–89. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07782-6_26.

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Biagetti, Giorgio, Paolo Crippa, Laura Falaschetti, Simone Orcioni, and Claudio Turchetti. "Human Activity Recognition Using Accelerometer and Photoplethysmographic Signals." In Intelligent Decision Technologies 2017, 53–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59424-8_6.

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Couceiro, R., Paulo Carvalho, R. P. Paiva, J. Henriques, I. Quintal, and J. Muehlsteff. "Detection of Motion Artifacts in Photoplethysmographic Signals: Algorithms Comparison." In IFMBE Proceedings, 327–30. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03005-0_83.

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Madhan Mohan, P., V. Nagarajan, and J. C. Vignesh. "Real-Time Automatic Peaks and Onsets Detection of Photoplethysmographic Signals." In Lecture Notes in Electrical Engineering, 79–90. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7251-2_9.

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Kumar, Piyush, Arpan Sharma, Mayanmi Zimik, Shivam Parashar, Renu Singh, Joyatri Bora, Aswini K. Patra, and Madhusudhan Mishra. "Measurement of Heartbeats for Well-Being Assessment Using Photoplethysmographic Signals." In Lecture Notes in Electrical Engineering, 485–94. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7031-5_46.

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Conference papers on the topic "Photoplethysmographic signal"

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Ratner, Ehud, Meir Nitzan, Yoseph Shomer, Alexander Gutman, and Anatoly Babchenko. "Analysis of the photoplethysmographic signal." In 8th Meeting in Israel on Optical Engineering, edited by Moshe Oron, Itzhak Shladov, and Yitzhak Weissman. SPIE, 1993. http://dx.doi.org/10.1117/12.151123.

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Pflugradt, Maik, and Reinhold Orglmeister. "Improved signal quality indication for photoplethysmographic signals incorporating motion artifact detection." In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6943975.

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Jianfeng Weng, Zhiqian Ye, and Jianling Weng. "An Improved Pre-processing Approach for Photoplethysmographic Signal." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1616337.

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Nitzan, Meir, Yoseph Shomer, and Alexander Gutman. "Device for reliable recording of the photoplethysmographic signal." In Europto Biomedical Optics '93, edited by Nathan I. Croitoru and Riccardo Pratesi. SPIE, 1994. http://dx.doi.org/10.1117/12.167299.

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Raghuram, M., K. Venu Madhav, E. Hari Krishna, Nagarjuna Reddy Komalla, Kosaraju Sivani, and K. Ashoka Reddy. "HHT based signal decomposition for reduction of motion artifacts in photoplethysmographic signals." In 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2012. http://dx.doi.org/10.1109/i2mtc.2012.6229404.

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Abeysekera, Saman S. "Photoplethysmographic signal analysis via beat-to-beat periodicity estimation." In TENCON 2016 - 2016 IEEE Region 10 Conference. IEEE, 2016. http://dx.doi.org/10.1109/tencon.2016.7848315.

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Andritoi, Doru, Valeriu David, and Radu Ciorap. "An portable device for ECG and photoplethysmographic signal acquisition." In 2014 International Conference and Exposition on Electrical and Power Engineering (EPE). IEEE, 2014. http://dx.doi.org/10.1109/icepe.2014.6969967.

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Kalkhaire, Shrutkirti D., and Vishal G. Puranik. "Remote detection of photoplethysmographic signal and SVM based classification." In 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT). IEEE, 2016. http://dx.doi.org/10.1109/icaecct.2016.7942568.

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Spachos, Petros, Jiexin Gao, and Dimitrios Hatzinakos. "Feasibility study of photoplethysmographic signals for biometric identification." In 2011 17th International Conference on Digital Signal Processing (DSP). IEEE, 2011. http://dx.doi.org/10.1109/icdsp.2011.6004938.

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Savaskan, Deniz Alp, Alihan Mahanoglu, Burak Soner, and Alisher Kholmatov. "Heart Rate Measurement from Motion Compensated Photoplethysmographic Signals." In 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE, 2020. http://dx.doi.org/10.1109/siu49456.2020.9302041.

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