Relevant bibliographies by topics / Heart Rate Variability (HRV) Signals

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Academic literature on the topic 'Heart Rate Variability (HRV) Signals'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "Heart Rate Variability (HRV) Signals"

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Çelik, Gamze, Mustafa Yıldırım, Mahmut Ilhan, Özcan Karaman, Ertuğrul Taşan, Sadık Kara, and Şükrü Okkesim. "Comparison of Pulse Rate Variability and Heart Rate Variability for Hypoglycemia Syndrome." Methods of Information in Medicine 55, no. 03 (2016): 250–57. http://dx.doi.org/10.3414/me15-01-0088.

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SummaryBackground: Heart rate variability (HRV) is a signal obtained from RR intervals of electro -cardiography (ECG) signals to evaluate the balance between the sympathetic nervous system and the parasympathetic nervous system; not only HRV but also pulse rate va -riability (PRV) extracted from finger pulse plethysmography (PPG) can reflect irregularities that may occur in heart rate and control procedures.Objectives: The purpose of this study is to compare the HRV and PRV during hypogly -cemia in order to evaluate the features that computed from PRV that can be used in detection of hypoglycemia.Methods: To this end, PRV and HRV of 10 patients who required testing with insulininduced hypoglycemia (IIHT) in Clinics of Endocrinology and Metabolism Diseases of Bezm-i Alem University (Istanbul, Turkey), were obtained. The recordings were done at three stages: prior to IIHT, during the IIHT, and after the IIHT. We used Bland-Altman analysis for comparing the parameters and to evaluate the correlation between HRV and PRV if exists.Results: Significant correlation (r > 0.90, p < 0.05) and close agreement were found between HRV and PRV for mean intervals, the root-mean square of the difference of successive intervals, standard deviation of successive intervals and the ratio of the low-to-high frequency power.Conclusions: In conclusion, all the features computed from PRV and HRV have close agreement and correlation according to Bland-Altman analyses’ results and features computed from PRV can be used in detection of hypoglycemia.
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Lee, Dae-Young, and Young-Seok Choi. "Multiscale Distribution Entropy Analysis of Short-Term Heart Rate Variability." Entropy 20, no. 12 (December 11, 2018): 952. http://dx.doi.org/10.3390/e20120952.

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Electrocardiogram (ECG) signal has been commonly used to analyze the complexity of heart rate variability (HRV). For this, various entropy methods have been considerably of interest. The multiscale entropy (MSE) method, which makes use of the sample entropy (SampEn) calculation of coarse-grained time series, has attracted attention for analysis of HRV. However, the SampEn computation may fail to be defined when the length of a time series is not enough long. Recently, distribution entropy (DistEn) with improved stability for a short-term time series has been proposed. Here, we propose a novel multiscale DistEn (MDE) for analysis of the complexity of short-term HRV by utilizing a moving-averaging multiscale process and the DistEn computation of each moving-averaged time series. Thus, it provides an improved stability of entropy evaluation for short-term HRV extracted from ECG. To verify the performance of MDE, we employ the analysis of synthetic signals and confirm the superiority of MDE over MSE. Then, we evaluate the complexity of short-term HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The experimental results exhibit that MDE is capable of quantifying the decreased complexity of HRV with aging and CHF disease with short-term HRV time series.
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Scheff, Jeremy D., Panteleimon D. Mavroudis, Steven E. Calvano, Stephen F. Lowry, and Ioannis P. Androulakis. "Modeling autonomic regulation of cardiac function and heart rate variability in human endotoxemia." Physiological Genomics 43, no. 16 (August 2011): 951–64. http://dx.doi.org/10.1152/physiolgenomics.00040.2011.

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Heart rate variability (HRV), the quantification of beat-to-beat variability, has been studied as a potential prognostic marker in inflammatory diseases such as sepsis. HRV normally reflects significant levels of variability in homeostasis, which can be lost under stress. Much effort has been placed in interpreting HRV from the perspective of quantitatively understanding how stressors alter HRV dynamics, but the molecular and cellular mechanisms that give rise to both homeostatic HRV and changes in HRV have received less focus. Here, we develop a mathematical model of human endotoxemia that incorporates the oscillatory signals giving rise to HRV and their signal transduction to the heart. Connections between processes at the cellular, molecular, and neural levels are quantitatively linked to HRV. Rhythmic signals representing autonomic oscillations and circadian rhythms converge to modulate the pattern of heartbeats, and the effects of these oscillators are diminished in the acute endotoxemia response. Based on the semimechanistic model developed herein, homeostatic and acute stress responses of HRV are studied in terms of these oscillatory signals. Understanding the loss of HRV in endotoxemia serves as a step toward understanding changes in HRV observed clinically through translational applications of systems biology based on the relationship between biological processes and clinical outcomes.
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Sieciński, Szymon, Paweł S. Kostka, and Ewaryst J. Tkacz. "Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms on Healthy Volunteers." Sensors 20, no. 16 (August 13, 2020): 4522. http://dx.doi.org/10.3390/s20164522.

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Physiological variation of the interval between consecutive heartbeats is known as the heart rate variability (HRV). HRV analysis is traditionally performed on electrocardiograms (ECG signals) and has become a useful tool in the diagnosis of different clinical and functional conditions. The progress in the sensor technique encouraged the development of alternative methods of analyzing cardiac activity: Seismocardiography and gyrocardiography. In our study we performed HRV analysis on ECG, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) using the PhysioNet Cardiovascular Toolbox. The heartbeats in ECG were detected using the Pan–Tompkins algorithm and the heartbeats in SCG and GCG signals were detected as peaks within 100 ms from the occurrence of the ECG R waves. The results of time domain, frequency domain and nonlinear HRV analysis on ECG, SCG and GCG signals are similar and this phenomenon is confirmed by very strong linear correlation of HRV indices. The differences between HRV indices obtained on ECG and SCG and on ECG and GCG were statistically insignificant and encourage using SCG or GCG for HRV estimation. Our results of HRV analysis confirm stronger correlation of HRV indices computed on ECG and GCG signals than on ECG and SCG signals because of greater tolerance to inter-subject variability and disturbances.
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Gospodinov, Mitko, Evgeniya Gospodinova, and Penio Lebamovski. "Analysis of Heart Rate Variability Using Photopletismnographic and Electrocardiographic Signals." Innovative STEM Education 3, no. 1 (June 29, 2021): 7–12. http://dx.doi.org/10.55630/stem.2021.0301.

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Heart rate variability (HRV) is a non-invasive marker for monitoring the physiological condition of patients and assisting in the diagnosis of cardiovascular disease. The aim of this study was to investigate the consistency between HRV parameters based on photoplethysmographic (PPG) and electrocardiographic (ECG) signals. Parameters from the linear analysis in the time domain were studied. The time domain indices are standardized and widely used to calculate HRV. These indices are statistical and geometric measurements. The statistical calculations of the successive heart rate intervals (RR interval series) are strictly interrelated (SDNN, SDANN, RMSSD, pNN50), while geometric measurements are based on TINN and HRVTi parameters. The ECG and PPG signals of a healthy individual were examined. The obtained results show a very good agreement between the HRV parameters obtained from the two types of signals. In view of this finding, it can be concluded that the PPG offers an alternative ECG option for HRV analysis without compromising accuracy. The correspondence between the studied parameters applied to the two types of signals provides potential support for the idea of using PPG instead of ECG in the extraction and analysis of HRV in outpatient cardiac monitoring of healthy individuals and patients with cardiovascular disease. A study of two groups of individuals: healthy and with cardiovascular disease based on PPG signals by applying the method: analysis in the time domain. The obtained results show that with the used method the two studied groups of subjects can be distinguished.
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Montano, N., S. Cerutti, and L. T. Mainardi. "Automatic Decomposition of Wigner Distribution and its Application to Heart Rate Variability." Methods of Information in Medicine 43, no. 01 (2004): 17–21. http://dx.doi.org/10.1055/s-0038-1633416.

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Summary Objective: We introduce an algorithm for the automatic decomposition of Wigner Distribution (WD) and we applied it for the quantitative extraction of Heart Rate Variability (HRV) spectral parameters during non-stationary events. Early response to tilt was investigated. Methods: Quantitative analysis of multi-components non-stationary signals is obtained through an automatic decomposition of WD based on least square (LS) fitting of the instantaneous autocorrelation function (ACF). Through this approach the different signal and interference terms which contributes to the ACF may be separated and their parameters (instantaneous frequency and amplitude) quantified. A beat-to-beat monitoring of HRV spectral components is obtained. Results: Analysis of simulated signals demonstrated the capability of the proposed approach to track and separate the signal components. Analysis of HRV data evidenced different dynamics in the early Autonomic Nervous System (ANS) response to tilt. Conclusions: The novel approach to the quantification of the beat-to-beat HRV spectral parameters obtained from decomposition of Wigner distribution was demonstrated to be effective in the analysis of HRV data. Relevant physiological information about the dynamics of the early sympathetic response to tilt were obtained. The method is a general approach which may be employed for a quantitative time-frequency analysis of non-stationary biological signals.
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Elgendi, Mohamed, Ian Norton, Matt Brearley, Socrates Dokos, Derek Abbott, and Dale Schuurmans. "A pilot study: Can heart rate variability (HRV) be determined using short-term photoplethysmograms?" F1000Research 5 (September 22, 2016): 2354. http://dx.doi.org/10.12688/f1000research.9556.1.

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To date, there have been no studies that investigate the independent use of the photoplethysmogram (PPG) signal to determine heart rate variability (HRV). However, researchers have demonstrated that PPG signals offer an alternative way of measuring HRV when electrocardiogram (ECG) and PPG signals are collected simultaneously. Based on these findings, we take the use of PPGs to the next step and investigate a different approach to show the potential independent use of short 20-second PPG signals collected from healthy subjects after exercise in a hot environment to measure HRV. Our hypothesis is that if the PPG--HRV indices are negatively correlated with age, then short PPG signals are appropriate measurements for extracting HRV parameters. The PPGs of 27 healthy male volunteers at rest and after exercise were used to determine the HRV indices: standard deviation of heartbeat interval (SDNN) and the root-mean square of the difference of successive heartbeats (RMSSD). The results indicate that the use of the $aa$ interval, derived from the acceleration of PPG signals, is promising in determining the HRV statistical indices SDNN and RMSSD over 20-second PPG recordings. Moreover, the post-exercise SDNN index shows a negative correlation with age. There tends to be a decrease of the PPG--SDNN index with increasing age, whether at rest or after exercise. This new outcome validates the negative relationship between HRV in general with age, and consequently provides another evidence that short PPG signals have the potential to be used in heart rate analysis without the need to measure lengthy sequences of either ECG or PPG signals.
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KARAMANOS, K., S. NIKOLOPOULOS, K. HIZANIDIS, G. MANIS, A. ALEXANDRIDI, and S. NIKOLAKEAS. "BLOCK ENTROPY ANALYSIS OF HEART RATE VARIABILITY SIGNALS." International Journal of Bifurcation and Chaos 16, no. 07 (July 2006): 2093–101. http://dx.doi.org/10.1142/s0218127406015933.

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In this paper we present a novel approach to the analysis of Heat Rate Variability (HRV) data, by coarse-graining analysis using the estimation of Block Entropies with the technique of lumping. HRV time series are generated from long recordings of Electrocardiograms (ECGs) and are then filtered in order to produce a coarse-grained symbolic dynamics. Block Entropy analysis is applied to these dynamics in order to examine its coarse-grained statistics. Our data set is comprised of two subsets, one of healthy subjects and another of Coronary Artery Disease (CAD) patients. It is found that Entropy analysis provides a quick and efficient tool for the differentiation of these series according to subject category. Healthy subjects provided more complex statistics compared to patients; specifically, the healthy data files provided higher values of block Entropies compared to patient ones. We also compare these results with the Correlation Dimension Estimation in order to establish coherency. We believe that this analysis may provide a useful statistical method towards the better understanding of the human cardiac system.
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Martinez-Delgado, Gerardo H., Alfredo J. Correa-Balan, José A. May-Chan, Carlos E. Parra-Elizondo, Luis A. Guzman-Rangel, and Antonio Martinez-Torteya. "Measuring Heart Rate Variability Using Facial Video." Sensors 22, no. 13 (June 21, 2022): 4690. http://dx.doi.org/10.3390/s22134690.

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Heart Rate Variability (HRV) has become an important risk assessment tool when diagnosing illnesses related to heart health. HRV is typically measured with an electrocardiogram; however, there are multiple studies that use Photoplethysmography (PPG) instead. Measuring HRV with video is beneficial as a non-invasive, hands-free alternative and represents a more accessible approach. We developed a methodology to extract HRV from video based on face detection algorithms and color augmentation. We applied this methodology to 45 samples. Signals obtained from PPG and video recorded an average mean error of less than 1 bpm when measuring the heart rate of all subjects. Furthermore, utilizing PPG and video, we computed 61 variables related to HRV. We compared each of them with three correlation metrics (i.e., Kendall, Pearson, and Spearman), adjusting them for multiple comparisons with the Benjamini–Hochberg method to control the false discovery rate and to retrieve the q-value when considering statistical significance lower than 0.5. Using these methods, we found significant correlations for 38 variables (e.g., Heart Rate, 0.991; Mean NN Interval, 0.990; and NN Interval Count, 0.955) using time-domain, frequency-domain, and non-linear methods.
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Mejía-Mejía, Elisa, and Panicos A. Kyriacou. "Photoplethysmography-Based Pulse Rate Variability and Haemodynamic Changes in the Absence of Heart Rate Variability: An In-Vitro Study." Applied Sciences 12, no. 14 (July 18, 2022): 7238. http://dx.doi.org/10.3390/app12147238.

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Pulse rate variability (PRV), measured from pulsatile signals such as the photoplethysmogram (PPG), has been largely used in recent years as a surrogate of heart rate variability (HRV), which is measured from electrocardiograms (ECG). However, different studies have shown that PRV does not always replicate HRV as there are multiple factors that could affect their relationship, such as respiration and pulse transit time. In this study, an in-vitro model was developed for the simulation of the upper-circulatory system, and PPG signals were acquired from it when haemodynamic changes were induced. PRV was obtained from these signals and time-domain, frequency-domain and non-linear indices were extracted. Factorial analyses were performed to understand the effects of changing blood pressure and flow on PRV indices in the absence of HRV. Results showed that PRV indices are affected by these haemodynamic changes and that these may explain some of the differences between HRV and PRV. Future studies should aim to replicate these results in healthy volunteers and patients, as well as to include the HRV information in the in-vitro model for a more profound understanding of these differences.
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Dissertations / Theses on the topic "Heart Rate Variability (HRV) Signals"

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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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Soler, Anderson Ivan Rincon. "Impact of artifact correction methods on R-R interbeat signals to quantifying heart rate variability (HRV) according to linear and nonlinear methods." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-02052016-130306/.

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In the analysis of heart rate variability (HRV) are used temporal series that contains the distances between successive heartbeats in order to assess autonomic regulation of the cardiovascular system. These series are obtained from the electrocardiogram (ECG) signal analysis, which can be affected by different types of artifacts leading to incorrect interpretations in the analysis of the HRV signals. Classic approach to deal with these artifacts implies the use of correction methods, some of them based on interpolation, substitution or statistical techniques. However, there are few studies that shows the accuracy and performance of these correction methods on real HRV signals. This study aims to determine the performance of some linear and non-linear correction methods on HRV signals with induced artefacts by quantification of its linear and nonlinear HRV parameters. As part of the methodology, ECG signals of rats measured using the technique of telemetry were used to generate real heart rate variability signals without any error. In these series were simulated missing points (beats) in different quantities in order to emulate a real experimental situation as accurately as possible. In order to compare recovering efficiency, deletion (DEL), linear interpolation (LI), cubic spline interpolation (CI), moving average window (MAW) and nonlinear predictive interpolation (NPI) were used as correction methods for the series with induced artifacts. The accuracy of each correction method was known through the results obtained after the measurement of the mean value of the series (AVNN), standard deviation (SDNN), root mean square error of the differences between successive heartbeats (RMSSD), Lomb\'s periodogram (LSP), Detrended Fluctuation Analysis (DFA), multiscale entropy (MSE) and symbolic dynamics (SD) on each HRV signal with and without artifacts. The results show that, at low levels of missing points the performance of all correction techniques are very similar with very close values for each HRV parameter. However, at higher levels of losses only the NPI method allows to obtain HRV parameters with low error values and low quantity of significant differences in comparison to the values calculated for the same signals without the presence of missing points.
Na análise da variabilidade da frequência cardíaca (Heart Rate Variability - HRV) são usadas séries temporais que contém as distancias entre batimentos cardíacos sucessivos, com o m de avaliar a regulação autonômica do sistema cardiovascular. Estas séries são obtidas a partir da análise de sinais de eletrocardiograma (ECG), as quais podem ser afetados por distintos tipos de artefatos, levando a interpretações incorretas nas análises feitas sob as séries da HRV. Abordagem clássica para lidar com esses artefatos implica a utilização de métodos de correção, alguns deles com base na interpolação, substituição ou técnicas estatísticas. No entanto, existem poucos estudos que mostram a precisão e desempenho destes métodos de correção em sinais reais da HRV. Assim, o presente estudo tem como objetivo determinar cómo os diferentes níveis de artefatos presentes no sinal afetam as caraterísticas da mesma, utilizando-se diferentes métodos lineares e não lineares de correção e posteriormente quanticação dos parâmetros da HRV. Como parte da metodología utilizada, sinais ECG de ratos obtidas mediante a técnica da telemetria foram usadas para gerar séries de HRV reais sem nenhum tipo de erro. Nestas séries foram simulados batimentos perdidos para diferentes taxas de pontos a m de emular a situação real com a maior precisão possível. Adicionalmente, foram aplicados os métodos de eliminação de segmentos (DEL), interpolação linear (LI) e cúbica (CI), janela de média móvel (MAW) e interpolação preditiva não lineal (NPI) como métodos de correção dos artefatos simulados sob as séries com erros. A precisão de cada método de correção foi conhecida através dos resultados obtidos com a quanticação do valor médio da série (AVNN), desvio padrão (SDNN), erro quadrático médio das diferenças entre batimentos sucessivos (RMSSD), periodograma de Lomb (LSP), análise de flutuações destendenciadas (DFA), entropia multiescala (MSE) e dinâmica simbólica (SD) sob cada sinal de HRV com e sem erros. Os resultados obtidos mostram que para baixos níveis de perdas de batimentos o desempenho das técnicas de correção é similar, com valores muito semelhantes para cada parámetro quanticado da HRV. Não obstante, em níveis de perdas maiores só NPI permite obter valores muito próximos e sem muitas diferenças signicativas para os mesmos parâmetros da HRV, em comparação com os valores calculados para as séries sem perdas.
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Wang, Yuan. "Heart rate variability and respiration signals as late onset sepsis diagnostic tools in neonatal intensive care units." Thesis, Rennes 1, 2013. http://www.theses.fr/2013REN1S106/document.

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Le sepsis tardif, défini comme une infection systémique chez les nouveaux nés âgés de plus de 3 jours, survient chez environ 7% à 10% de tous les nouveau-nés et chez plus de 25% des nouveau-nés de très faible poids de naissance qui sont hospitalisés dans les unités de soins intensifs néonatals (USIN). Les apnées et bradycardies (AB) spontanées récurrentes et graves sont parmi les principaux indicateurs précoces cliniques de l'infection systémique chez les prématurés. L'objectif de cette thèse est de déterminer si la variabilité du rythme cardiaque (VRC), la respiration et l'analyse de leurs relations aident au diagnostic de l'infection chez les nouveaux nés prématurés par des moyens non invasifs en USIN. Par conséquent, on a effectué l'analyse Mono-Voie (MV) et Bi-Voies (BV) sur deux groupes sélectionnés de nouveau-nés prématurés: sepsis (S) vs. non-sepsis (NS). (1) Tout d'abord, on a étudié la série RR non seulement par des méthodes de distribution (moy, varn, skew, kurt, med, SpAs), par les méthodes linéaire: le domaine temporel (SD, RMSSD) et dans le domaine fréquentiel (p_VLF, p_LF, p_HF), mais aussi par les méthodes non–linéaires: la théorie du chaos (alphas, alphaF) et la théorie de l'information (AppEn, SamEn, PermEn, Regul). Pour chaque méthode, nous étudions trois tailles de fenêtre 1024/2048/4096, puis nous comparons ces méthodes afin de trouver les meilleures façons de distinguer S de NS. Les résultats montrent que les indices alphaS, alphaF et SamEn sont les paramètres optimaux pour séparer les deux populations. (2) Ensuite, la question du couplage fonctionnel entre la VRC et la respiration nasale est adressée. Des relations linéaires et non-linéaires ont été explorées. Les indices linéaires sont la corrélation (r²), l'indice de la fonction de cohérence (Cohere) et la corrélation temps-fréquence (r2t,f) , tandis que le coefficient de régression non-linéaire (h²) a été utilisé pour analyser des relations non-linéaires. Nous avons calculé les deux directions de couplage pendant l'évaluation de l'indice h2 de régression non-linéaire. Enfin, à partir de l'ensemble du processus d'analyse, il est évident que les trois indices (r2tf_rn_raw_0p2_0p4, h2_rn_raw et h2_nr_raw) sont des moyens complémentaires pour le diagnostic du sepsis de façon non-invasive chez ces patients fragiles. (3) Après, l'étude de faisabilité de la détection du sepsis en USIN est réalisée sur la base des paramètres retenus lors des études MV et BV. Nous avons montré que le test proposé, basé sur la fusion optimale des six indices ci-dessus, conduit à de bonnes performances statistiques. En conclusion, les mesures choisies lors de l'analyse des signaux en MV et BV ont une bonne répétabilité et permettent de mettre en place un test en vue du diagnostic non invasif et précoce du sepsis. Le test proposé peut être utilisé pour fournir une alarme fiable lors de la survenue d'un épisode d'AB tout en exploitant les systèmes de monitoring actuels en USIN
Late-onset sepsis, defined as a systemic infection in neonates older than 3 days, occurs in approximately 10% of all neonates and in more than 25% of very low birth weight infants who are hospitalized in Neonatal Intensive Care Units (NICU). Recurrent and severe spontaneous apneas and bradycardias (AB) is one of the major clinical early indicators of systemic infection in the premature infant. Various hematological and biochemical markers have been evaluated for this indication but they are invasive procedures that cannot be repeated several times. The objective of this Ph.D dissertation was to determine if heart rate variability (HRV), respiration and the analysis of their relationships help to the diagnosis of infection in premature infants via non-invasive ways in NICU. Therefore, we carried out Mono-Channel (MC) and Bi-Channel (BC) Analysis in two selected groups of premature infants: sepsis (S) vs. non-sepsis (NS). (1) Firstly, we studied the RR series not only by distribution methods (moy, varn, skew, kurt, med, SpAs), by linear methods: time domain (SD, RMSSD) and frequency domain (p_VLF, p_LF, p_HF), but also by non-linear methods: chaos theory (alphaS, alphaF) and information theory (AppEn, SamEn, PermEn, Regul). For each method, we attempt three sizes of window 1024/2048/4096, and then compare these methods in order to find the optimal ways to distinguish S from NS. The results show that alphaS, alphaF and SamEn are optimal parameters to recognize sepsis from the diagnosis of late neonatal infection in premature infants with unusual and recurrent AB. (2) The question about the functional coupling of HRV and nasal respiration is addressed. Linear and non-linear relationships have been explored. Linear indexes were correlation (r²), coherence function (Cohere) and time-frequency index (r2t,f), while a non-linear regression coefficient (h²) was used to analyze non-linear relationships. We calculated two directions during evaluate the index h2 of non-linear regression. Finally, from the entire analysis process, it is obvious that the three indexes (r2tf_rn_raw_0p2_0p4, h2_rn_raw and h2_nr_raw) were complementary ways to diagnosticate sepsis in a non-invasive way, in such delicate patients.(3) Furthermore, feasibility study is carried out on the candidate parameters selected from MC and BC respectively. We discovered that the proposed test based on optimal fusion of 6 features shows good performance with the largest Area Under Curves (AUC) and the least Probability of False Alarm (PFA). As a conclusion, we believe that the selected measures from MC and BC signal analysis have a good repeatability and accuracy to test for the diagnosis of sepsis via non-invasive NICU monitoring system, which can reliably confirm or refute the diagnosis of infection at an early stage
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Franěk, Pavel. "Analýza variability srdečního rytmu pomocí rekurentního diagramu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220048.

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The aim of this thesis is to describe the variability of cardiac rhythm and familiarity with the methods of the analysis, ie by monitoring changes in heart rhythm electrogram signal recording and using the methods in the time domain using recurrent diagram. The work describes the quantification of the methods and possibilities of quantifiers in the evaluation of heart rate variability analysis. It also describes the clinical significance of heart rate variability and diagnostic capabilities changes of heart rate variability caused by ischemic heart disease. The practical part describes how to create applications in Matlab to calculate the quantifiers analysis of heart rate variability in the time domain using recurrent diagram. The calculation was made of the positions R wave elektrogram signal isolated rabbit hearts. The calculated values of quantifiers both methods were statistically evaluated and discussed.
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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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Jež, Radek. "Software pro ruční rozměření signálů EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219251.

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This thesis deals with evaluation EKG in terms of classification rhythm and analysis HRV. In theoretic part of work are described basics of heart physiology and its usual pathology, basics of electrocardiography, evaluation EKG and standard methods of HRV evaluation. In practical part are described algorithms used in created application. Mainly describes technique of rhythm evaluation, ectopic rhythms and delineation error elimination, data preparing for HRV evaluation, drift removal from DES and HRV evaluation methods. Created program was tested on CSE and MIT- BIH database records. For lack of suitable data and absence of tested data, it wasn’t possible to test all the classification rules of used algorithms. Tested part of program appears reliable and functional.
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Heathers, James. "Methodological improvements in heart rate variability." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/13106.

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Heart rate variability (HRV) refers to the amplitude and period of fluctuations in the heart rate over time. HRV is an accessible, low-cost and straightforward technique for measuring autonomic outflow, but also a complicated epiphenomena of interacting autonomic, circulatory and respiratory factors. Confusion about the meaning of HRV is reflected in the literature establishing basic HRV theory, and in the applied literature which uses HRV as a dependent variable or predictor of psychological outcomes. Here, 2 straightforward issues present themselves: 1) best-case practice for methodological implementation is not being followed, and 2) sample sizes for between-subjects investigations of phenomena where HRV is a dependent variable are underpowered. Specifically addressing the above; 1) an attempt is made to a) understand and codify a best-case practice for methodological control in biobehavioural research, and b) investigate profound but common sources of error in HRV recording; 2) rationale for the development and field testing of a device which allows mass collection of HRV records from experimental participants is outlined. Best-case practice for experimental implementation is recommended: the use of within-subjects data, the consideration of the nature of ‘baseline’ periods against which experimental conditions are compared, and respiratory monitoring within participants to control for occasional or whole-sample artifacts. Current research is not well controlled – theoretical, statistical and practical errors are widely observed. For addressing experimental power, pulse monitoring shows acceptable reliability over time, and the device developed (a smartphone-based pulse rate monitor) shows excellent accuracy in comparison to conventional measurement. A novel solution for correcting pulse to pulse intervals is offered which improves measurement accuracy and performs well in field trials of mass-collected data.
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Boman, Kajsa. "Heart rate variability : A possible measure of subjective wellbeing?" Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15911.

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Wellbeing and subjective wellbeing (SWB) has become some the most important goals of our time, both individually and societally. Thus, there is a need for reliable ways to measure SWB, as concerns regarding many current measures have been raised. Due to the interwoven nature of physiology and psychology, heart rate variability (HRV) has the potential to assess psychological processes in a physiological manner. HRV is an attractive measure since it is inexpensive, easy and non-invasive. Hence, the aim is to, from a cognitive neuroscientific standpoint, investigate whether HRV could serve as an objective measure to assess SWB. Most studies demonstrate associations between HRV and SWB, in particular between high frequency (HF)-HRV and positive affect (PA). However, the one study fully matching the theoretical framework only showed an inverse correlation between HRV and negative affect (NA). Plausibly implying that HRV does not serve as a reliable measure of SWB, but may be able to indicate inverse associations with NA, and possibly index certain aspect of SWB such as deactivated PA. The study of the relationship between HRV and SWB is still in its infancy and results are inconsistent. The lack of common standards regarding measurements, implementation details, and variable values, make results difficult to compare and generalize. Further standardizations and research are much needed before accurate conclusions can be drawn.
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Suh, Sooyeon. "STRESS, ANXIETY, AND HEART RATE VARIABILITY IN CHRONIC OBSTRUCTIVE PULMONARY DISEASE." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1275495558.

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Minnis, James Michael. "Nutrition and athletic performance: implications of heart rate variability." Kansas State University, 2015. http://hdl.handle.net/2097/20344.

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Master of Science
Department of Human Nutrition
Mark Haub
The purpose of this review is to examine the role of heart rate variability (HRV) as a predictor of athletic readiness and performance and assess whether nutrition strategies can be implemented to create favorable HRV indices with the goal of improving athletic readiness and performance. The balance between training and recovery is crucial for reaching an optimal adaptation and avoiding overtraining, allowing for improved readiness to train and compete. The measurement of HRV is non-invasive and is used primarily to quantify physical and mental stress in athletes by monitoring the effects of the autonomic nervous system on the heart. Current data suggests a relationship between resting parasympathetic tone, via time and frequency domains, and athletic performance. Parasympathetic modulated HRV indices have been associated with performance metrics such as peripheral work capacity, aerobic power, running and sprint performance, swimming performance, weight lifting performance, anaerobic capacity, strength, and enhanced mental focus/skill execution. The use of nutrition to help enhance sports performance is becoming more common. Evidence-based sports nutrition provides fuel for training/competition, assists in maximizing training adaptations, enhances recovery, improves mental focus, and aids in injury prevention and recovery. The use of nutrition strategies to influence HRV is novel and current evidence is scarce in regards to nutritional effects on HRV, specifically in athletes. Current research suggests that achieving energy balance and decreasing body fat in overweight/obese individuals has positive effects on the vagal component of HRV indices. Proper hydration, fruit and vegetable intake, a moderate carbohydrate diet, omega-3 fatty acid supplementation/intake also seem to have positive effects on HRV indices. Certain individual supplements have been studied in regards to HRV including casein hydrolysate, amaranth oil, and bovine colostrum. Caffeine seems to have the opposite effect on HRV indices, increasing sympathetic modulation while decreasing parasympathetic modulation. Much more research needs to be done in regard to potential nutritional influences on HRV so that sport dietitians feel confident in the methods currently used to assess athlete readiness and determining what types of nutrition strategies may be used to further improve the performance of an athlete.
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Books on the topic "Heart Rate Variability (HRV) Signals"

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Heart rate variability (HRV) signal analysis: Clinical applications. Boca Raton: Taylor & Francis, 2013.

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Kamath, Markad V., Mari Watanabe, and Adrian Upton, eds. Heart Rate Variability (HRV) Signal Analysis. CRC Press, 2016. http://dx.doi.org/10.1201/b12756.

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Watanabe, Mari, Markad V. Kamath, Adrian R. M. Upton, and Carlos A. Morillo. Heart Rate Variability (Hrv) Signal Analysis. Taylor & Francis Group, 2012.

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Trimmel, Karin, Jerzy Sacha, and Heikki Veli Huikuri, eds. Heart Rate Variability: Clinical Applications and Interaction between HRV and Heart Rate. Frontiers Media SA, 2015. http://dx.doi.org/10.3389/978-2-88919-652-4.

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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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Jovanovic, Tanja, and Seth Davin Norrholm. Human Psychophysiology and PTSD. Edited by Israel Liberzon and Kerry J. Ressler. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190215422.003.0015.

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Psychophysiological measures provide useful tools for investigating neurobiological mechanisms of trauma-related sequalae. In addition, they can serve as objective biological assessments of symptom severity in clinical research. This chapter describes the methods for collection of psychophysiological measures. These include muscle contractions (startle), electrodermal skin conductance, heart rate, and heart rate variability (HRV) at baseline, under stress, and following Pavlovian fear conditioning. These approaches are important both for understanding biology as well as for providing objective biomarkers that can be compared translationally from animals to humans. It also reviews the literature that has used these measures in PTSD. The evidence to date strongly suggests that these data provide robust correlates of PTSD severity.
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Book chapters on the topic "Heart Rate Variability (HRV) Signals"

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Anandhi, B., and S. Jerritta. "Hilbert Huang Transform (HHT) Analysis of Heart Rate Variability (HRV) in Recognition of Emotion in Children with Autism Spectrum Disorder (ASD)." In Biomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders, 65–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97845-7_4.

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Orbell, Sheina, Havah Schneider, Sabrina Esbitt, Jeffrey S. Gonzalez, Jeffrey S. Gonzalez, Erica Shreck, Abigail Batchelder, et al. "Heart Rate Variability (HRV)." In Encyclopedia of Behavioral Medicine, 953. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_100783.

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Ernst, Gernot. "HRV in Oncology and Palliative Medicine." In Heart Rate Variability, 261–68. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4309-3_13.

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Ernst, Gernot. "HRV and Alterations in the Vegetative Nervous System." In Heart Rate Variability, 119–28. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4309-3_5.

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Kiyono, Ken, Junichiro Hayano, Eiichi Watanabe, and Yoshiharu Yamamoto. "Heart Rate Variability (HRV) and Sympathetic Nerve Activity." In Clinical Assessment of the Autonomic Nervous System, 147–61. Tokyo: Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-56012-8_9.

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Rompelman, Otto, and Ben J. TenVoorde. "Analysis of Heart Rate Variability." In Advances in Processing and Pattern Analysis of Biological Signals, 225–34. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-9098-6_16.

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Cerutti, Sergio, and Maria G. Signorini. "The Heart Rate Variability Signal." In Advances in Processing and Pattern Analysis of Biological Signals, 235–49. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-9098-6_17.

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Khandoker, Ahsan Habib, Chandan Karmakar, Michael Brennan, Andreas Voss, and Marimuthu Palaniswami. "Poincaré Plot Interpretation of HRV Using Physiological Model." In Poincaré Plot Methods for Heart Rate Variability Analysis, 25–46. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-7375-6_3.

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Khandoker, Ahsan Habib, Chandan Karmakar, Michael Brennan, Andreas Voss, and Marimuthu Palaniswami. "Poincaré Plot in Capturing Nonlinear Temporal Dynamics of HRV." In Poincaré Plot Methods for Heart Rate Variability Analysis, 47–68. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-7375-6_4.

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García Martínez, Constantino Antonio, Abraham Otero Quintana, Xosé A. Vila, María José Lado Touriño, Leandro Rodríguez-Liñares, Jesús María Rodríguez Presedo, and Arturo José Méndez Penín. "Comparing HRV Variability Across Different Segments of a Recording." In Heart Rate Variability Analysis with the R package RHRV, 117–32. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65355-6_6.

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Conference papers on the topic "Heart Rate Variability (HRV) Signals"

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Liou, Jian-Chiun, and Ting-Yu Su. "Instantaneous Heart Rate Variability(HRV) Signal Cloud Portable Flat Panel Observation." In 2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). IEEE, 2018. http://dx.doi.org/10.1109/icce-china.2018.8448483.

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Carvalho, Diogo, Luis Silva, Miguel Carvalho, Mariana Dias, Nelson Costa, Duarte Folgado, Maria Lua, Hugo Gamboa, and Elazer Edelman. "Heart rate variability during repetitive work in the presence of fatigue." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003433.

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Fatigue has been demonstrated to have a direct negative effect on the productivity of the worker and a marked rise in the long-term occupational risk. The measures being presently applied mainly target the muscular activity directly, that together with subjective understandings of one’s own fatigue levels make up an inaccurate cumulative evaluation of fatigue. “Industry 4.0” wearable devices allows for a more complete and continuous measurement of variables related to fatigue, and thus represent a more accurate value of the worker’s fatigue level.Aim: To analyse the structure of heart rate variability, as a measure of cardiovascular responsiveness, during repetitive work when muscular fatigue is present. Tasks: A protocol was developed to simulate a real-life workplace scenario with a set of low-intensity repetitive tasks that are commonly practiced. The signals obtained were then processed, and heart rate variability was calculated using multifractal analysis and frequency domain variables.Hypothesis: It was hypothesized that the structure of variability will change during repetitive work in the presence of fatigue.Methodology: Participants were asked to perform three 10-minute trials of a repetitive task involving a specific set of movements commonly required for work. Between each trial, a fatigue protocol was carried out, targeting the main agonist muscle. An ECG was collected through a wearable band placed on the level of the xiphoid appendix during the three trials denominated: Baseline, Fatigue 1, and Fatigue 2. Results: Significant differences were found in Very Low Frequency and Low Frequency in the Baseline and Fatigue 2 conditions. However, there were no significant differences in High Frequency. The results of the fractal analysis did not show any significant differences for any q-order, indicating that the fractal of HRV is maintained.Conclusions: These results are enthusiastic for applying algorithms that use heart rate variability to quantify cardiovascular responsiveness to fatigue during repetitive work. These results suggest that fatigue alters the variability structure of HRV, but the fractal structure of HRV remains unchanged.
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Podaru, Alexandru, and Valeriu David. "A STUDY REGARDING THE ACQUISITION OF PHOTOPLETHYSMOGRAPIC SIGNAL USED IN DETERMINATION OF THE PRV PARAMETERS." In eLSE 2020. University Publishing House, 2020. http://dx.doi.org/10.12753/2066-026x-20-203.

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In this paper, two areas for collecting the photoplethysmographic signal were studied. The areas of interest we have considered are the index of the left hand and the lobe of the left ear. To record these signals we have created a portable system. To achieve this system we used 3 biological signal detection modules. With this system, three biological signals were recorded, namely, the electrocardiographic signal (ECG) acquired with the help of three electrodes, the PPG signal acquired from the left-hand index and the PPG signal acquired from the ear lobe. For the ECG signal, we used the AD8232 module, compatible with Arduino kits. For the photoplethysmographic signal, we used a pulse sensor, and for the other signal, we used an earlobe clip sensor, which generates a rectangular signal. The recordings of these signals were made on 16 subjects. All the recordings were made in the same way, with the subjects sitting on the chair. The signals were recorded simultaneously, using the same sampling frequency, to have a closer approximation in detecting the time interval between two cardiac cycles. From the recorded signals we determined the heart rate variability (HRV) parameters and the pulse rate variability (PRV) parameters from the two photoplethysmographic signals. With the data obtained we made 2 correlations as follows: a correlation between HRV parameters with PRV parameters determined from the left-hand index, and the second correlation was made between HRV parameters and PRV parameters determined from the signal acquired from the left earlobe, and the values obtained for both correlations are over 98%
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Gao, Jianbo, Jing Hu, and Wen-wen Tung. "Multiscale Analysis of Biological Signals." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6084.

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Complex systems often generate highly nonstationary and multiscale signals, due to nonlinear and stochastic interactions among their component systems and hierarchical regulations imposed by the operating environments. The further advances in the fields of life sciences, systems biology, nano-sciences, information systems, and physical sciences, have made it increasingly important to develop complexity measures that incorporate the concept of scale explicitly, so that different behaviors of the signals on varying scales can be simultaneously characterized by the same scale-dependent measure. Here, we propose such a measure, the scale-dependent Lyapunov exponent (SDLE), and develop a unified theory of multiscale analysis of complex data. We show that the SDLE can readily characterize low-dimensional chaos and random 1/fα processes, as well as accurately detect epileptic seizures from EEG data and distinguish healthy subjects from patients with congestive heart failure from heart rate variability (HRV) data. More importantly, our analyses of EEG and HRV data illustrate that commonly used complexity measures from information theory, chaos theory, and random fractal theory can be related to the values of the SDLE at specific scales, and useful information on the structured components of the data is also embodied by the SDLE.
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Wanqing Wu and Jungtae Lee. "Development of full-featured ECG system for visual stress induced heart rate variability (HRV) assessment." In 2010 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2010. http://dx.doi.org/10.1109/isspit.2010.5711762.

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Rezaei, Shahab, Sadaf Moharreri, and Ali Ghorshi. "Designing the FPGA-based system for Triangle Phase space Mapping (TPSM) of heart rate variability (HRV) signal." In 2015 38th International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2015. http://dx.doi.org/10.1109/tsp.2015.7296403.

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Luca, Catalina, Calin Corciova, and Daniela Matei. "THE IMPORTANCE OF COMPUTER METHODS IN BIOMEDICINE - THE ANALIZATION BETWEEN HRV AND TYPES OF EXERCISE." In eLSE 2018. Carol I National Defence University Publishing House, 2018. http://dx.doi.org/10.12753/2066-026x-18-207.

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The purpose of this paper is to evaluate the reliability of short term recordings (5 minutes) of Heart Rate Variability (HRV) and the association between HRV and type of exercise, using an advanced computer software. For this study short term electrocardiogram recording was acquired during supine for 10 min using the BIOPAC MP 150 data acquisition system. AcqKnowledge Softwareversion 4.1.1. (BIOPAC Inc., Goleta, CA, USA) was used to analyze and remove from the recorded ECG all artefacts and ectopic beats. Kubios HRV(R)Analysis Software 2.0 for Windows (The Biomedical Signal and Medical Imaging Analysis Group, Department of Applied Physics, University of Kuopio, Finland) was used to generate the HRV parameters. Data acquisition was performed in a quiet room with temperature between 20 and 220C. The data were recorded between 9 and 10 am, after an adaptation period of 15 minutes. All individuals were asked to avoid caffeine and alcohol 24 hours before the tests. HRV time and frequency domain parameters during rest were calculated on 10 (6 males and 4 females) and after 10 minute of static exercise and 10minutes of dynamic exercise, using Kubios HRV(R)Analysis Software 2.0. HRV is a non invasive assessement method of the autonomic nervous system activity which regulates heart rate (HR). The conclusion of our study was that there is a relation between HRV and type of exercise. Kubios HRV(R)Analysis Software correlated very well the values with the referent method among healthy volunteers and may be used by researchers for HRV studies. Together with a modern heart rate monitor capable of recording RR intervals this freely distributed program forms a complete low-cost HRV measuring and analysis system.
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Hu, Shan, and Xun Yu. "Non-Intrusive ECG Measurement on Vehicle Steering Wheel and Driver Seat." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192963.

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Driver drowsiness is one of the major causes of deadly traffic accidents. Continuous monitoring of drivers’ drowsiness thus is of great importance for preventing drowsiness-caused accidents. Previous psychophysiological studies have shown that heart rate variability (HRV) has established differences between waking and sleep stages [1, 2]. This offers a way to detect driver’s drowsiness by analyzing HRV, which is typically measured and analyzed from electrocardiogram (ECG) signal. Although ECG measurement techniques are well developed, most of them involve electrode contacts on chest or head. Wiring and discomfort problems inherent in those techniques prevent implementing them on cars. To address these problems, we make full use of the environment settings in a car to develop two non-intrusive real-time ECG measurement methods for drivers.
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GUOMING CHEN, GUOMING CHEN, QI ZHANG, XUANKE TONG, and GUOFU LIAO. "VITAL SIGNS MONITORING BASED ON WEBCAM FOR HOME TELEMEDICINE APPLICATIONS." In 2021 INTERNATIONAL CONFERENCE ON ADVANCED EDUCATION AND INFORMATION MANAGEMENT (AEIM 2021). Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/dtssehs/aeim2021/35982.

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Abstract. In this paper, we proposed a novel approach to monitor the vital signs based webcam for home telemedicine applications. This approach can continuously monitor the vital signs without wearable sensors. It uses the real time video processing algorithm to obtain the instantaneous heart rate(HR) and respiration rate(RR). Furthermore, the heart rate variability (HRV) was analyzed by power spectral density (PSD) estimation using the Lomb periodogram. Experiments in different scenarios were performed to verify the efficacy of the proposed noncontact monitoring vital signs based on webcam. The real time experimental system can be used to measure the instantaneous HR and RR, at the same time the low frequency and high frequency components were extracted. All experimental results show that the proposed concept can be applied to the home telemedicine in the future.
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Higuera Trujillo, Juan Luis, Javier Marín Morales, Juan Carlos Rojas, and Juan López Tarruella Maldonado. "Emotional maps: neuro architecture and design applications." In Systems & Design: Beyond Processes and Thinking. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/ifdp.2016.3170.

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Neurophysiological measurements have increased in Design and Architecture to emotionally assess products. Objective understanding of emotion states brings an enormous opportunity to explain how environments aspects affects persons. New methodology must be developed for a successfully approach between Neuroscience and design areas, in order to build this recent synergy. This paper contribute to profound concept of Emotional Maps (EM), which is a challenge for two reasons: the characterization of emotional states and the uncertain relation with maps illustrations. In order to create an EM, Heart Rate Variability (HRV) was used to detect certain emotional states and Virtual Reality (VR) to generate an environment condition. The study was conducted by VR environment displayed in Head-mounted Display Oculus DK2. Twelve persons participated in data acquisition by two tools: during environment exploration, a portable physiological device (Empatica E4) recorded HRV signal; and at the end of study, a Likert scale questionnaire collected emotional impressions. HRV signal was analyze in time-frequency to detect activation or relax levels. The statistics results prove that design guidelines used in environments evoked the stressful state sought, and that the physiological measure used are appropriate to be represented. The final result shows the possibility to mapping emotional states. This novel technique allows to quantify objectively a subjective experience and locate in specific place when occurs. Our technique supposes a contribution toward emotional states measurements applied to design and architecture.DOI: http://dx.doi.org/10.4995/IFDP.2016.3170
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Reports on the topic "Heart Rate Variability (HRV) Signals"

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Brusseau, Valentin, I. Tauveron, R. Bagheri, U. Ugbolue, V. Magnon, J. B. Bouillon-Minois, V. Navel, and F. Dutheil. Effect of hyperthyroidism treatments on heart rate variability: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2022. http://dx.doi.org/10.37766/inplasy2022.8.0062.

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Review question / Objective: The reversibility of HRV abnormalities in hyperthyroidism remains contradictory. The purpose of the study is to conduct a systematic review and meta-analysis on the effect of antithyroid treatments on HRV in hyperthyroidism. Population: Untreated hyperthyroid patients Intervention: Antithyroid treatment Control: Controls without hyperthyroidism Outcomes: Reversibility of heart rate variability abnormalities in hyperthyroidism Study design: Systematic review. Information sources: All studies that addressed the effect of hyperthyroidism treatment on HRV were reviewed. Studies were searched electronically through the major article databases (PubMed, Cochrane Library, Embase, and Google Scholar) with the following keywords: ("hyperthyroidism" OR "hyperthyroid") AND ("heart rate variability" OR "HRV") until April 4, 2022.
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Zhou, Mu-Jiao, and Yong-Hong Yang. Effects of Transcutaneous Electric Acupoint Stimulation (TEAS) on Heart Rate Variability (HRV): a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0137.

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