Relevant bibliographies by topics / Photoplethysmography (PPG) signals

Academic literature on the topic 'Photoplethysmography (PPG) signals'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Photoplethysmography (PPG) signals"

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Tang, Ya Wen, and Yue Der Lin. "L2-EMD Filter Design for Photoplethysmography Signal." Applied Mechanics and Materials 479-480 (December 2013): 486–90. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.486.

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Photoplethysmography (PPG) is a noninvasive bio-signal measurement technique used to monitor peripheral hemal circulatory circumstance. Generally, PPG signal is treated as a morphologically equivalent signal of pulse signals. To prevent the affection of environment noise or motion artifact, an empirical mode decomposition (EMD) base filter with L2 norm similarity selector is proposed in this article. In this experiment, PPG signal of twelve healthy subjects is acquired with a stable state. The acquired PPG signals are mixed with different level white noise to exam the filter capability. 30dB SNR and 60dB SNR noisy PPG signals were adopted and applied with empirical mode decomposition to extract the imply mode of the input signal. After that, a l2-norm calculation is used to evaluate the similarity between each extracted intrinsic mode function (IMF) and the input signal. The high similarity IMFs are collected and used to reconstruct the filtered signal. Although the reconstructed signals may suffer a serious boundary effect as EMD faced, the results show effective noise elimination and prove the l2-EMD filter capability of PPG signals.
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Ju, Bin, Yun Tao Qian, and Huo Jie Ye. "Wavelet Based Measurement on Photoplethysmography by Smartphone Imaging." Applied Mechanics and Materials 380-384 (August 2013): 773–77. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.773.

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[Purpose] Smartphones video cameras can be used to detect the photoplethysmograph (PPG) signal.The pulse wave signal detected by smartphone always mixed mass noise because of finger moving, unevenness of pressure and outer light interference. Previous studies limit to the filtering algorithm that denoising signals, without considering characteristics information of pulse wave itself. [Method] In this paper, we propose an algorithm based on wavelet to detect qualified PPG, which captures three critical characteristic quantities through wavelet high frequency coefficient. [Results] Experiment illustrates that the detected PPG signal contain dicrotic wave, and whats more, further experiment on artery elasticity indexes indicates good robust of the algorithm. [Conclusions] Wavelet Based Measurement on Photoplethysmography by Smartphone Imaging can be used for the calculation of cardiovascular parameter such as angiosclerosis, arrhythmia, and vascular resistance.
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Alkhoury, Ludvik, JiWon Choi, Vishnu D. Chandran, Gabriela B. De Carvalho, Saikat Pal, and Moshe Kam. "Dual Wavelength Photoplethysmography Framework for Heart Rate Calculation." Sensors 22, no. 24 (December 17, 2022): 9955. http://dx.doi.org/10.3390/s22249955.

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The quality of heart rate (HR) measurements extracted from human photoplethysmography (PPG) signals are known to deteriorate under appreciable human motion. Auxiliary signals, such as accelerometer readings, are usually employed to detect and suppress motion artifacts. A 2019 study by Yifan Zhang and his coinvestigatorsused the noise components extracted from an infrared PPG signal to denoise a green PPG signal from which HR was extracted. Until now, this approach was only tested on “micro-motion” such as finger tapping. In this study, we extend this technique to allow accurate calculation of HR under high-intensity full-body repetitive “macro-motion”. Our Dual Wavelength (DWL) framework was tested on PPG data collected from 14 human participants while running on a treadmill. The DWL method showed the following attributes: (1) it performed well under high-intensity full-body repetitive “macro-motion”, exhibiting high accuracy in the presence of motion artifacts (as compared to the leading accelerometer-dependent HR calculation techniques TROIKA and JOSS); (2) it used only PPG signals; auxiliary signals such as accelerometer signals were not needed; and (3) it was computationally efficient, hence implementable in wearable devices. DWL yielded a Mean Absolute Error (MAE) of 1.22|0.57 BPM, Mean Absolute Error Percentage (MAEP) of 0.95|0.38%, and performance index (PI) (which is the frequency, in percent, of obtaining an HR estimate that is within ±5 BPM of the HR ground truth) of 95.88|4.9%. Moreover, DWL yielded a short computation period of 3.0|0.3 s to process a 360-second-long run.
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Chang, Cheng-Chun, Chien-Ta Wu, Byung Il Choi, and Tong-Jing Fang. "MW-PPG Sensor: An on-Chip Spectrometer Approach." Sensors 19, no. 17 (August 26, 2019): 3698. http://dx.doi.org/10.3390/s19173698.

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Multi-wavelength photoplethysmography (MW-PPG) sensing technology has been known to be superior to signal-wavelength photoplethysmography (SW-PPG) sensing technology. However, limited by the availability of sensing detectors, many prior studies can only use conventional bulky and pricy spectrometers as the detectors, and hence cannot bring the MW-PPG technology to daily-life applications. In this study we developed a chip-scale MW-PPG sensor using innovative on-chip spectrometers, aimed at wearable applications. Also in this paper we present signal processing methods for robustly extracting the PPG signals, in which an increase of up to 50% in the signal-to-noise ratio (S/N) was observed. Example measurements of saturation of peripheral blood oxygen (SpO2) and blood pressure were conducted.
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Li, Suyi, Lijia Liu, Jiang Wu, Bingyi Tang, and Dongsheng Li. "Comparison and Noise Suppression of the Transmitted and Reflected Photoplethysmography Signals." BioMed Research International 2018 (September 26, 2018): 1–9. http://dx.doi.org/10.1155/2018/4523593.

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The photoplethysmography (PPG) is inevitably corrupted by many kinds of noise no matter whether its acquisition mode is transmittance or reflectance. To enhance the quality of PPG signals, many studies have made great progress in PPG denoising by adding extra sensors and developing complex algorithms. Considering the reasonable cost, compact size, and real-time and easy implementation, this study proposed a simple real-time denoising method based on double median filters which can be integrated in microcontroller of commercial or portable pulse oximeters without adding extra hardware. First, we used the boundary extension to preserve the signal boundary distortion and designed a first median filter with the time window at approximately 78 ms to eliminate the high-frequency components of the signal. Then, through the second median filter with a time window which was about 780 ms, we estimated the low-frequency components. Finally, we removed the estimated low-frequency components from the signal to obtain the denoised signal. Through comparing the multiple sets of signals under calmly sitting and slightly moving postures, the PPG signals contained noises no matter whether collected by the transmittance-mode or the reflectance-mode. To evaluate the proposed method, we conducted measured, simulated experiments and a strong noisy environment experiment. Through comparing the morphology distortions, frequency spectra, and the signal-to-noise ratios (SNRs), the results showed that the proposed method can suppress noise effectively and preserve the essential morphological features from PPG signals. As a result, the proposed method can enhance the quality of PPG signals and, thus, can contribute to the improvement of the calculation accuracy of the subsequent physiological parameters. In addition, the proposed method could be a good choice to address the real-time noise reduction of portable PPG measuring instruments.
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Liang, Yongbo, Zhencheng Chen, Rabab Ward, and Mohamed Elgendi. "Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification." Biosensors 8, no. 4 (October 26, 2018): 101. http://dx.doi.org/10.3390/bios8040101.

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Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper, we investigate whether or not deep learning can provide better results for hypertension risk stratification when compared to the classical signal processing and feature extraction methods. We tested a deep learning method for the classification and evaluation of hypertension using photoplethysmography (PPG) signals based on the continuous wavelet transform (using Morse) and pretrained convolutional neural network (using GoogLeNet). We collected 121 data recordings from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database, each containing arterial blood pressure (ABP) and photoplethysmography (PPG) signals. The ABP signals were utilized to extract blood pressure category labels, and the PPG signals were used to train and test the model. According to the seventh report of the Joint National Committee, blood pressure levels are categorized as normotension (NT), prehypertension (PHT), and hypertension (HT). For the early diagnosis and assessment of HT, the timely detection of PHT and the accurate diagnosis of HT are significant. Therefore, three HT classification trials were set: NT vs. PHT, NT vs. HT, and (NT + PHT) vs. HT. The F-scores of these three classification trials were 80.52%, 92.55%, and 82.95%, respectively. The tested deep method achieved higher accuracy for hypertension risk stratification when compared to the classical signal processing and feature extraction method. Additionally, the method achieved comparable results to another approach that requires electrocardiogram and PPG signals.
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Cheshmedzhiev, Krasimir. "A Photoplethysmography Signals Registering Device." Innovative STEM Education 2, no. 1 (August 10, 2020): 13–20. http://dx.doi.org/10.55630/stem.2020.0202.

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Monitoring a heart rate provides an essential information about health status of a subjects. Photoplethysmography is a low-cost optical technique to monitor blood volume changes in human body. In this article is presented a portable microcontroller system to register PPG signals from two types of sensors, convert them and store data on internal storage or send it to personal computer for next processing.
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Yen, Chih-Ta, Sheng-Nan Chang, and Cheng-Hong Liao. "Deep learning algorithm evaluation of hypertension classification in less photoplethysmography signals conditions." Measurement and Control 54, no. 3-4 (March 2021): 439–45. http://dx.doi.org/10.1177/00202940211001904.

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This study used photoplethysmography signals to classify hypertensive into no hypertension, prehypertension, stage I hypertension, and stage II hypertension. There are four deep learning models are compared in the study. The difficulties in the study are how to find the optimal parameters such as kernel, kernel size, and layers in less photoplethysmographyt (PPG) training data condition. PPG signals were used to train deep residual network convolutional neural network (ResNetCNN) and bidirectional long short-term memory (BILSTM) to determine the optimal operating parameters when each dataset consisted of 2100 data points. During the experiment, the proportion of training and testing datasets was 8:2. The model demonstrated an optimal classification accuracy of 76% when the testing dataset was used.
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Yu, Su-Gyeong, So-Eui Kim, Na Hye Kim, Kun Ha Suh, and Eui Chul Lee. "Pulse Rate Variability Analysis Using Remote Photoplethysmography Signals." Sensors 21, no. 18 (September 17, 2021): 6241. http://dx.doi.org/10.3390/s21186241.

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Pulse rate variability (PRV) refers to the change in the interval between pulses in the blood volume pulse (BVP) signal acquired using photoplethysmography (PPG). PRV is an indicator of the health status of an individual’s autonomic nervous system. A representative method for measuring BVP is contact PPG (CPPG). CPPG may cause discomfort to a user, because the sensor is attached to the finger for measurements. In contrast, noncontact remote PPG (RPPG) extracts BVP signals from face data using a camera without the need for a sensor. However, because the existing RPPG is a technology that extracts a single pulse rate rather than a continuous BVP signal, it is difficult to extract additional health status indicators. Therefore, in this study, PRV analysis is performed using lab-based RPPG technology that can yield continuous BVP signals. In addition, we intended to confirm that the analysis of PRV via RPPG can be performed with the same quality as analysis via CPPG. The experimental results confirmed that the temporal and frequency parameters of PRV extracted from RPPG and CPPG were similar. In terms of correlation, the PRVs of RPPG and CPPG yielded correlation coefficients between 0.98 and 1.0.
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Charlton, Peter H., Panicos Kyriacou, Jonathan Mant, and Jordi Alastruey. "Acquiring Wearable Photoplethysmography Data in Daily Life: The PPG Diary Pilot Study." Engineering Proceedings 2, no. 1 (November 14, 2020): 80. http://dx.doi.org/10.3390/ecsa-7-08233.

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The photoplethysmogram (PPG) signal is widely measured by smart watches and fitness bands for heart rate monitoring. New applications of the PPG are also emerging, such as to detect irregular heart rhythms, track infectious diseases, and monitor blood pressure. Consequently, datasets of PPG signals acquired in daily life are valuable for algorithm development. The aim of this pilot study was to assess the feasibility of acquiring PPG data in daily life. A single subject was asked to wear a wrist-worn PPG sensor six days a week for four weeks, and to keep a diary of daily activities. The sensor was worn for 75.0% of the time, signals were acquired for 60.6% of the time, and signal quality was high for 30.5% of the time. This small pilot study demonstrated the feasibility of acquiring PPG data during daily living. Key lessons were learnt for future studies: (i) devices which are waterproof and require charging less frequently may provide signals for a greater proportion of the time; (ii) data should either be stored on the device or streamed via a reliable connection to a second device for storage; (iii) it may be beneficial to acquire signals during the night or during periods of low activity to achieve high signal quality; and (iv) there are several promising areas for PPG algorithm development including the design of pulse wave analysis techniques to track changes in cardiovascular properties in daily life. The dataset and code are publicly available at DOI: 10.5281/zenodo.3268500.
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Dissertations / Theses on the topic "Photoplethysmography (PPG) signals"

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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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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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Vařečka, Martin. "Stanovení krevního tlaku pomocí chytrého telefonu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-378143.

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Blood pressure is one of the basic indicators of the health state of the cardiovascular system. High blood pressure is the main risk factor of ischemic heart disease, atherosclerosis and stroke. Therefore, it is important to monitor long-term changes in blood pressure and respond in time to these changes. Blood pressure meters are not standard household equipment, while a well-equipped smartphone is. Smartphones contain a large number of sensors capable of measuring biomedical signals. This thesis focuses on creating an application capable of determining blood pressure using data obtained from these sensors.
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Benetti, Tiago. "Estimativa robusta da frequ?ncia card?aca a partir de sinais de fotopletismografia de pulso." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2018. http://tede2.pucrs.br/tede2/handle/tede/8337.

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Heart rate monitoring using Photoplethysmography (PPG) signals acquired from the individuals pulse has become popular due to emergence of numerous low cost wearable devices. However, monitoring during physical activities has obstacles because of the influence of motion artifacts in PPG signals. The objective of this work is to introduce a new algorithm capable of removing motion artifacts and estimating heart rate from pulse PPG signals. Normalized Least Mean Square (NLMS) and Recursive Least Squares (RLS) algorithms are proposed for an adaptive filtering structure that uses acceleration signals as reference to remove motion artifacts. The algorithm uses the Periodogram of the filtered signals to extract their heart rates, which will be used together with a PPG Signal Quality Index to feed the input of a Kalman Filter. Specific heuristics and the Quality Index collaborate so that the Kalman filter provides a heart rate estimate with high accuracy and robustness to measurement uncertainties. The algorithm was validated from the heart rate obtained from Electrocardiography signals and the proposed method with the RLS algorithm presented the best results with an absolute mean error of 1.54 beats per minute (bpm) and standard deviation of 0.62 bpm, recorded for 12 individuals performing a running activity on a treadmill with varying speeds. The results make the performance of the algorithm comparable and even better than several recently developed methods in this field. In addition, the algorithm presented a low computational cost and suitable to the time interval in which the heart rate estimate is performed. Thus, it is expected that this algorithm will improve the obtaining of heart rate in currently available wearable devices.
O monitoramento da frequ?ncia card?aca utilizando sinais de Fotopletismografia ou PPG (do ingl?s, Photopletismography) adquiridos do pulso de indiv?duos tem se popularizado devido ao surgimento de in?meros dispositivos wearable de baixo custo. No entanto, o monitoramento durante atividades f?sicas tem dificuldades em raz?o da influ?ncia de artefatos de movimento nos sinais de PPG. O objetivo deste trabalho ? introduzir um novo algoritmo capaz de remover artefatos de movimento e estimar a frequ?ncia card?aca de sinais de PPG de pulso. Os algoritmos do M?nimo Quadrado M?dio Normalizado ou NLMS (do ingl?s, Normalized Least Mean Square) e de M?nimos Quadrados Recursivos ou RLS (do ingl?s, Recursive Least Squares) s?o propostos para uma estrutura de filtragem adaptativa que utiliza sinais de acelera??o como refer?ncia para remover os artefatos de movimento. O algoritmo utiliza o Periodograma dos sinais filtrados para extrair suas frequ?ncias card?acas, que ser?o utilizadas juntamente com um ?ndice de Qualidade do Sinal de PPG para alimentar a entrada de um Filtro de Kalman. Heur?sticas espec?ficas e o ?ndice de Qualidade colaboram para que filtro de Kalman forne?a uma estimativa da frequ?ncia card?aca com alta acur?cia e robustez a incertezas de medi??o. O algoritmo foi validado a partir da frequ?ncia card?aca obtida de sinais de Eletrocardiografia e o m?todo proposto com o algoritmo RLS apresentou os melhores resultados com um erro m?dio absoluto de 1,54 batimentos por minuto (bpm) e desvio padr?o de 0,62 bpm, registrados para 12 indiv?duos realizando uma atividade de corrida em uma esteira com velocidades variadas. Os resultados tornam o desempenho do algoritmo compar?vel e at? mesmo melhor que v?rios m?todos desenvolvidos recentemente neste campo. Al?m disso, o algoritmo apresentou um custo computacional baixo e adequado ao intervalo de tempo em que a estimativa da frequ?ncia card?aca ? realizada. Dessa forma, espera-se que este algoritmo melhore a obten??o da frequ?ncia card?aca em dispositivos wearable atualmente dispon?veis.
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Shen, Chun-Jen, and 沈峻任. "Non-invasive blood glucose monitoring health-care system based on Photoplethysmography(PPG) signal." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/83527451741480718952.

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Book chapters on the topic "Photoplethysmography (PPG) signals"

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Ramírez Mena, Andrés David, Leonardo Antonio Bermeo Varón, Rodolfo Molano Valencia, and Erick Javier Argüello Prada. "Mechanical Pain Assessment Through Parameters Derived from Photoplethysmographic (PPG) Signals: A Pilot Study." In Communications in Computer and Information Science, 168–78. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42517-3_13.

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Kim, Myong-hwan, and Hee-Je Kim. "An Analysis on the Particular Pulse Related to the Human Bio-signal by Using Photoplethysmography(PPG)." In Intelligent Robotics and Applications, 18–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40852-6_3.

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Xu, Yang, Zhipei Huang, Jiankang Wu, and Zhongdi Liu. "Continuous Blood Pressure Monitoring Method Based on Multiple Photoplethysmography Features." In Computer Methods in Medicine and Health Care. IOS Press, 2021. http://dx.doi.org/10.3233/atde210246.

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Continuous blood pressure monitoring is of great significance for the prevention and early diagnosis of cardiovascular diseases. However, the existing continuous blood pressure monitoring methods, especially the sleeveless blood pressure monitoring methods, are complex and computationally heavy. In this paper, we propose a method, using plethysmography (PPG) signals alone, to estimate continuous blood pressure by extracting multiple PPG features related to intravascular blood flow characteristics. The performance of our method was evaluated using ten minutes synchronized PPG signals and continuous blood pressure from 21 healthy volunteers and 19 patients with hypertension and diabetes. The test results have shown that the absolute mean errors and standard deviation errors between the estimated and referenced blood pressure are 3.22±0.66 mmHg for systolic blood pressure and 2.11±1.11 mmHg for diastolic blood pressure, which meet AAMI (the association for the advancement of medical instrumentation) error acceptance.
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Rajasekaran, K., Anitha Mary Xavier, and R. Jegan. "Smart Technology for Non Invasive Biomedical Sensors to Measure Physiological Parameters." In Handbook of Research on Healthcare Administration and Management, 318–47. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0920-2.ch019.

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Communication and Information technologies are transforming our lifestyles, social interactions, and workplaces. One of the promising applications of the information and communication technology is healthcare and wellness management. Advancement in electronic health care and mobile have made doctors and patients to involve the modern healthcare system by extending the capabilities of physiological monitoring devices. Various biomedical sensors are being used to measure the physiological parameters like pulse rate, blood glucose level, blood pressure etc., Among various bio-sensor, Researchers from different field of science are particularly and increasingly interested in Photoplethysmography (PPG) signals. This chapter addresses the importance of bio sensors and its principle, significance of remote monitoring of PPG signal using Radio Frequency (RF) and design challenges in RF connectivity. Also this chapter presents a reliable low power wireless transmission mechanism of biomedical signals which works on narrow band RF frequencies.
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Rajasekaran, K., Anitha Mary Xavier, and R. Jegan. "Smart Technology for Non Invasive Biomedical Sensors to Measure Physiological Parameters." In Biomedical Engineering, 749–78. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3158-6.ch034.

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Communication and Information technologies are transforming our lifestyles, social interactions, and workplaces. One of the promising applications of the information and communication technology is healthcare and wellness management. Advancement in electronic health care and mobile have made doctors and patients to involve the modern healthcare system by extending the capabilities of physiological monitoring devices. Various biomedical sensors are being used to measure the physiological parameters like pulse rate, blood glucose level, blood pressure etc., Among various bio-sensor, Researchers from different field of science are particularly and increasingly interested in Photoplethysmography (PPG) signals. This chapter addresses the importance of bio sensors and its principle, significance of remote monitoring of PPG signal using Radio Frequency (RF) and design challenges in RF connectivity. Also this chapter presents a reliable low power wireless transmission mechanism of biomedical signals which works on narrow band RF frequencies.
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S., Dhanalakshmi, Gayathiridevi B., Kiruthika S., and E. Smily Jeya Jothi. "PPG-Based Cardiovascular Disease Predictor Using Artificial Intelligence." In Leveraging AI Technologies for Preventing and Detecting Sudden Cardiac Arrest and Death, 218–39. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8443-9.ch010.

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Heart disease is estimated to be the major cause of death among the middle-aged population worldwide. Researchers assess huge volumes of medical data using a variety of statistical, machine learning, and deep learning methods, supporting healthcare practitioners in predicting heart illness. This work aims to predict the likelihood of people developing heart disease using a wearable wristband that can record photoplethysmography (PPG) signals. Cardiovascular features extracted from the PPG signal are used to train the prediction algorithm. It enables the patient to self-monitor their health and take precautionary measures and treatment at the onset of symptoms of the disease. Random forest, convolutional neural network, long short-term memory networks are trained using publicly available databases comprising both affected and standard parameters and thereby used for comparison with the acquired sensor data for predictive analysis.
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Jothi, E. Smily Jeya, J. Anitha, and D. Jude Hemanth. "Deep Transfer Learning Approach for Obstructive Sleep Apnea Classification with Photoplethysmography Signal." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia220703.

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Human health and quality of life are negatively impacted by apnea, an increasingly prevalent sleep disorder. For monitoring and managing sleep apnea’s side effects and consequences, accurate automatic algorithms for detecting sleep apnea are crucial. In this paper, deep transfer learning methods are employed for the detection of OSA events from Electrocardiograph (ECG) and Photoplethysmography (PPG) signals. ResNet34 is a deep learning model based on convolutional neural networks (CNNs). Transfer learning algorithms such as AlexNet, VGG16, VGG19 and ResNet50 are implemented. In order to train the ResNet34 model data augmentation, optimal learning rate finding, and fine-tuning are used. To obtain generalizable models, a training set of data is divided into three sets: a validation set for adjusting hyperparameters and improving generalizability, and a test set for evaluating generalizability on unknown data. Deep transfer learning models have the best accuracy, sensitivity, specificity, precision, and F1 score with 97.86±1.24%, 99.65%, 97.12%, 98.16% and 98.90% respectively. It can assist sleep lab technicians in screening patients for OSA events continuously through PPG and ECG signals.
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Nitzan, Meir, and Zehava Ovadia-Blechman. "Physical and physiological interpretations of the PPG signal." In Photoplethysmography, 319–40. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-12-823374-0.00009-8.

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Harikrishna, Ette, and Komalla Ashoka Reddy. "Use of Transforms in Biomedical Signal Processing and Analysis." In Real Perspective of Fourier Transforms and Current Developments in Superconductivity. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98239.

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Biomedical signals like electrocardiogram (ECG), photoplethysmographic (PPG) and blood pressure were very low frequency signals and need to be processed for further diagnosis and clinical monitoring. Transforms like Fourier transform (FT) and Wavelet transform (WT) were extensively used in literature for processing and analysis. In my research work, Fourier and wavelet transforms were utilized to reduce motion artifacts from PPG signals so as to produce correct blood oxygen saturation (SpO2) values. In an important contribution we utilized FT for generation of reference signal for adaptive filter based motion artifact reduction eliminating additional sensor for acquisition of reference signal. Similarly we utilized the transforms for other biomedical signals.
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Kumar, Arun, Padmini Sharma, and Mukesh Kumar Chandrakar. "Discriminating Significant Morphological Attributes of Photoplethysmograph Signal for Cuffless Blood Pressure Measurement." In Advances in Medical Technologies and Clinical Practice, 269–81. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9831-3.ch013.

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Photoplethysmograph signal carries very useful cardiac information such heart rate, oxygen saturation level, blood pressure, and diabetic condition. Blood pressure is one such cardiac information that can be estimated by extracting features of PPG signal. Cuff-less blood pressure measurement using photoplethysmograph (PPG) signal is one of non-invasive methods. It allows continuous monitoring of blood pressure in simple, rapid, and low-cost mode. This chapter segregates PPG features and re-investigates their effectiveness in terms of BP measurement. Machine learning algorithm based on K-nearest neighbour is applied for classification of samples. MIMIC II multi-parameter database of ECG and finger PPG is applied on the KNN classifiers. Classification accuracy comes to 92%, and correlation between predicted and observed SBP and DSB are 0.89 and 0.85, respectively.
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Conference papers on the topic "Photoplethysmography (PPG) signals"

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Nabavi, Seyedfakhreddin, John Cogan, Asim Roy, Brandon Canfield, Robert Kibler, and Collin Emerick. "Sleep Monitoring with Intraorally Measured Photoplethysmography (PPG) Signals." In 2022 IEEE Sensors. IEEE, 2022. http://dx.doi.org/10.1109/sensors52175.2022.9967075.

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Chao, Paul C. P., and Pei-Yu Chiang. "Photoplethysmography Signals Processing Using Polynomial Profile Fitting for Measuring the Blood Flow Volume in Arteriovenous Fistula." In ASME 2017 Conference on Information Storage and Processing Systems collocated with the ASME 2017 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/isps2017-5445.

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In this work, a new photoplethysmography (PPG) signal processing methodology using polynomial profile fitting for measuring blood flow volume (BFV) in arteriovenous fistula (AVF) is proposed. After calibration, the experiment results using proposed method shows higher correlation (R = 0.7883) and lower error (RMSE = 109 ml/min) compared to the ones using conventional methods (R = 0.3212, RMSE = 168 ml/min).
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Ihsan, Muhammad Fadhil, Satria Mandala, and Miftah Pramudyo. "Study of Feature Extraction Algorithms on Photoplethysmography (PPG) Signals to Detect Coronary Heart Disease." In 2022 International Conference on Data Science and Its Applications (ICoDSA). IEEE, 2022. http://dx.doi.org/10.1109/icodsa55874.2022.9862855.

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Senturk, Umit, Ibrahim Yucedag, and Kemal Polat. "Cuff-less continuous blood pressure estimation from Electrocardiogram(ECG) and Photoplethysmography (PPG) signals with artificial neural network." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404255.

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Parsaoran, Aldrin Jozefan, Satria Mandala, and Miftah Pramudyo. "Study of Denoising Algorithms on Photoplethysmograph (PPG) Signals." In 2022 International Conference on Data Science and Its Applications (ICoDSA). IEEE, 2022. http://dx.doi.org/10.1109/icodsa55874.2022.9862918.

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Chen, Yu-Ting, Tse-Yi Tu, and Paul C. P. Chao. "The Multi Wavelength Arrayed Flexible PPG Sensing Patch for to Estimate Heart Rate and Blood Oxygen." In ASME 2020 29th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/isps2020-1923.

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Abstract This study aims to develop the Photoplethysmography (PPG) sensor patch for to estimate the heart rate (HR) and blood oxygen (SpO2). A newly developed multi wavelength arrayed flexible OLED-OPD PPG sensing patch elevates the performance of motion artifact for not only for heart rate estimation but also blood oxygen estimation. The PPG sensing patch ensures the long-time continuous monitoring of the PPG signal from the wrist artery during sleeping, walking and cycling. The accuracy of the HRs is 92% and the accuracy of SpO2 is 95%.
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Park, Seung-Ho, and Kyoung-Su Park. "Advance Monitoring of Blood Pressure and Respiratory Rate Using De-Noising Auto Encoder." In ASME 2021 30th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/isps2021-65921.

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Abstract As the importance of continuous vital signs monitoring increases, the need for wearable devices to measure vital sign is increasing. In this study, the device is designed to measure blood pressure (BP), respiratory rate (RR), and heartrate (HR) with one sensor. The device is in earphone format and is manufactured as wireless type using Arduino-based bluetooth module. The device measures pulse signal in the Superficial temporal artery using Photoplethysmograghy (PPG) sensor. The device uses the Auto Encoder to remove noise caused by movement, etc., contained in the pulse signal. Extract the feature from the pulse signal and use them for the vital sign measurement. The device is measured using Slope transit time (STT) method for BP and Respiratory sinus arrhythmia (RSA) method for RR. Finally, the accuracy is determined by comparing the vital signs measured through the device with the reference vital signs measured simultaneously.
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Pandey, Rajeev Kumar, Jerry Lin, and Paul C. P. Chao. "Design of a New Long-Time Continuous Photoplethysmography Signal Acquisition System to Obtain Accurate Measurement of Heart Rate." In ASME 2020 29th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/isps2020-1916.

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Abstract This study presents a time-interleave and low DC drift long-time continuous photoplethysmography (PPG) signal acquisition system to obtain accurate measurement of heart rate (HR) in real-time. Time-interleave functionality is used herein to minimize the mispositioning issue. Intensity tuning and transimpedance amplifier gain tuning is used herein to acquire a high-quality PPG signal. The front-end analog readout circuit is designed and implemented by using T18 process. The experimental result shows that the design readout system has the DC settling time of 1 second after the motion artifact. The measured current sensing range is 30nA–10uA. The estimated signal to noise ratio is 68dB@1Hz. The backend pre-signal processing incorporates a new convolution-based moving average filter, signal quality index estimator, and a peak-through detector. The non-invasive PPG sensor is applied to the wrist artery of the 40 healthy subjects for sensing the pulsation of the blood vessel. During the measurement, the subject did not drink (alcohol), eat, smoke or workout. The Measurement results shows that the heart rate accuracy and standard error are 95%, and 0.37±1.96bpm, respectively.
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Karimian, Nima, Zimu Guo, Mark Tehranipoor, and Domenic Forte. "Human recognition from photoplethysmography (PPG) based on non-fiducial features." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953035.

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Pribadi, Eka Fitrah, Rajeev Kumar Pandey, and Paul C. P. Chao. "A High-Resolution and Low Offset Delta-Sigma Analog to Digital Converter for Detecting Photoplethysmography Signal." In ASME 2021 30th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/isps2021-65248.

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Abstract A high-resolution, low offset delta-sigma analog to digital converter for detecting photoplethysmography (PPG) signal is presented in this study. The PPG signal is a bio-optical signal incorporated with heart functionality and located in the range of 0.1–10 Hz. The location to get PPG signal is on a pulsating artery. Thus the delta-sigma analog-to-digital (DS ADC) converter is designed specifically in that range. However, the DS ADC circuitry suffers from 1/f noise under 10 Hz frequency range. A chopper based operational amplifier is implemented in DS ADC to push the 1/f noise into high-frequency noise. The dc offset of the operational amplifier is also pushed to the high-frequency region. The DS ADC circuitry consists of a second-order continuous-time delta-sigma modulator. The delta-sigma modulator circuitry is designed and simulated using TSMC 180 nm technology. The continuous-time delta-sigma modulator active area layout is 746μm × 399 μm and fabricated using TSMC 180 nm technology. It operates in 100 Hz bandwidth and 4096 over-sampling ratios. The SFDR of the circuit is above 70 dB. The power consumption of the delta-sigma modulator is 35.61μW. The simulation is performed in three different kinds of corner, SS, TT, and FF corner, to guarantee the circuitry works in different conditions.
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