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Journal articles on the topic 'Heart Rate Variability (HRV) Signals'
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1
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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Odinaev, Ismoil, Kwan Long Wong, Jing Wei Chin, Raghav Goyal, Tsz Tai Chan, and Richard H. Y. So. "Robust Heart Rate Variability Measurement from Facial Videos." Bioengineering 10, no. 7 (July 18, 2023): 851. http://dx.doi.org/10.3390/bioengineering10070851.
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Remote Photoplethysmography (rPPG) is a contactless method that enables the detection of various physiological signals from facial videos. rPPG utilizes a digital camera to detect subtle changes in skin color to measure vital signs such as heart rate variability (HRV), an important biomarker related to the autonomous nervous system. This paper presents a novel contactless HRV extraction algorithm, WaveHRV, based on the Wavelet Scattering Transform technique, followed by adaptive bandpass filtering and inter-beat-interval (IBI) analysis. Furthermore, a novel method is introduced to preprocess noisy contact-based PPG signals. WaveHRV is bench-marked against existing algorithms and public datasets. Our results show that WaveHRV is promising and achieves the lowest mean absolute error (MAE) of 10.5 ms and 6.15 ms for RMSSD and SDNN on the UBFCrPPG dataset.
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Francesco, Buccelletti, Bocci Maria Grazia, Gilardi Emanuele, Fiore Valentina, Calcinaro Sara, Fragnoli Chiara, Maviglia Riccardo, and Franceschi Francesco. "Linear and Nonlinear Heart Rate Variability Indexes in Clinical Practice." Computational and Mathematical Methods in Medicine 2012 (2012): 1–5. http://dx.doi.org/10.1155/2012/219080.
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Biological organisms have intrinsic control systems that act in response to internal and external stimuli maintaining homeostasis. Human heart rate is not regular and varies in time and such variability, also known as heart rate variability (HRV), is not random. HRV depends upon organism's physiologic and/or pathologic state. Physicians are always interested in predicting patient's risk of developing major and life-threatening complications. Understanding biological signals behavior helps to characterize patient's state and might represent a step toward a better care. The main advantage of signals such as HRV indexes is that it can be calculated in real time in noninvasive manner, while all current biomarkers used in clinical practice are discrete and imply blood sample analysis. In this paper HRV linear and nonlinear indexes are reviewed and data from real patients are provided to show how these indexes might be used in clinical practice.
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De Santis, Marta, Samanta Seganfreddo, Alberto Greco, Simona Normando, Daniele Benedetti, Franco Mutinelli, and Laura Contalbrigo. "Donkey Heart Rate and Heart Rate Variability: A Scoping Review." Animals 13, no. 3 (January 25, 2023): 408. http://dx.doi.org/10.3390/ani13030408.
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Heart rate (HR) and heart rate variability (HRV) are commonly used physiological measures in animals. While several studies exist on horse HRV, less information is available for donkeys. This scoping review aims to understand the extent and type of published evidence on donkey HR and HRV, their clinical and research applications, the devices used, and the analysis performed. Only quantitative primary studies published in English were considered. Four different databases were queried through the Web of Science platform, with additional evidence identified by citation chasing. After a two-stage screening phase, data were extracted considering study and population characteristics, information on HR/HRV analysis, and applications. The majority of the 87 included articles (about 80%) concerned a sample size of up to 20 individuals and were published since 2011 (about 65%). Forty-one articles employed an electronic device for signal acquisition (mainly electrocardiographs and heart rate monitors), yet only two articles reported HRV parameters. The literature on donkey HRV is lacking, and this gap can be filled by gaining knowledge on donkey characteristics and finding useful tools for welfare assessment. Comparison with what is known about the horse allows a discussion of the technical and interpretative difficulties that can be encountered with donkeys.
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Sieciński, Szymon, Ewaryst Janusz Tkacz, and Paweł Stanisław Kostka. "Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases." Sensors 23, no. 4 (February 14, 2023): 2152. http://dx.doi.org/10.3390/s23042152.
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Heart rate variability (HRV) is the physiological variation in the intervals between consecutive heartbeats that reflects the activity of the autonomic nervous system. This parameter is traditionally evaluated based on electrocardiograms (ECG signals). Seismocardiography (SCG) and/or gyrocardiography (GCG) are used to monitor cardiac mechanical activity; therefore, they may be used in HRV analysis and the evaluation of valvular heart diseases (VHDs) simultaneously. The purpose of this study was to compare the time domain, frequency domain and nonlinear HRV indices obtained from electrocardiograms, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) in healthy volunteers and patients with valvular heart diseases. An analysis of the time domain, frequency domain and nonlinear heart rate variability was conducted on electrocardiograms and gyrocardiograms registered from 29 healthy male volunteers and 30 patients with valvular heart diseases admitted to the Columbia University Medical Center (New York City, NY, USA). The results of the HRV analysis show a strong linear correlation with the HRV indices calculated from the ECG, SCG and GCG signals and prove the feasibility and reliability of HRV analysis despite the influence of VHDs on the SCG and GCG waveforms.
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FAUST, OLIVER, V. RAMANAN PRASAD, G. SWAPNA, SUBHAGATA CHATTOPADHYAY, and TEIK-CHENG LIM. "COMPREHENSIVE ANALYSIS OF NORMAL AND DIABETIC HEART RATE SIGNALS: A REVIEW." Journal of Mechanics in Medicine and Biology 12, no. 05 (December 2012): 1240033. http://dx.doi.org/10.1142/s0219519412400337.
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A large section of the world's population is affected by diabetes mellitus (DM), commonly referred to as "diabetes." Every year, the number of cases of DM is increasing. Diabetes has a strong genetic basis, hence it is very difficult to cure, but can be controlled with medications to prevent subsequent organ damage. Therefore, early diagnosis of diabetes is very important. In this paper, we examine how diabetes affects cardiac health, which is reflected through heart rate variability (HRV), as observed in electrocardiography (ECG) signals. Such signals provide clues for both the presence and severity of diabetes as well as diabetes-induced cardiac impairments. Heart rate (HR) is a non-linear and non-stationary signal. Thus, extracting useful information from HRV signals is a difficult task. We review several sophisticated signal processing and information extraction methods in order to establish measurable relationships between the presence and the extent of diabetes as well as the changes in the HRV signals. Furthermore, we discuss a typical range of values for several statistical, geometric, time domain, frequency domain, time–frequency, and non-linear features for HR signals from 15 normal and 15 diabetic subjects. We found that non-linear analysis is the most suitable approach to capture and analyze the subtle changes in HRV signals caused by diabetes.
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Shao, Shiliang, Ting Wang, Yawei Li, Chunhe Song, Yihan Jiang, and Chen Yao. "Comparison Analysis of Different Time-Scale Heart Rate Variability Signals for Mental Workload Assessment in Human-Robot Interaction." Wireless Communications and Mobile Computing 2021 (October 6, 2021): 1–12. http://dx.doi.org/10.1155/2021/8371637.
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Excessive mental workload affects human health and may lead to accidents. This study is motivated by the need to assess mental workload in the process of human-robot interaction, in particular, when the robot performs a dangerous task. In this study, the use of heart rate variability (HRV) signals with different time scales in mental workload assessment was analyzed. A humanoid dual-arm robot that can perform dangerous work was used as a human-robot interaction object. Electrocardiogram (ECG) signals of six subjects were collected in two states: during the task and in a relaxed state. Multiple time-scale (1, 3, and 5 min) HRV signals were extracted from ECG signals. Then, we extracted the same linear and nonlinear features from the HRV signals at different time scales. The performance of machine learning algorithms using the different time-scale HRV signals obtained during the human-robot interaction was evaluated. The results show that for the per-subject case with a 3 min HRV signal length, the K -nearest neighbor classifier achieved the best mental workload classification performance. For the cross-subject case with a 5 min time-scale signal length, the gentle boost classifier achieved the best mental workload classification accuracy. This study provides a novel research idea for using HRV signals to measure mental workload during human-robot interaction.
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Milena, Čukić, Chiara Romano, Francesca De Tommasi, Massimiliano Carassiti, Domenico Formica, Emiliano Schena, and Carlo Massaroni. "Linear and Non-Linear Heart Rate Variability Indexes from Heart-Induced Mechanical Signals Recorded with a Skin-Interfaced IMU." Sensors 23, no. 3 (February 2, 2023): 1615. http://dx.doi.org/10.3390/s23031615.
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Heart rate variability (HRV) indexes are becoming useful in various applications, from better diagnosis and prevention of diseases to predicting stress levels. Typically, HRV indexes are retrieved from the heart’s electrical activity collected with an electrocardiographic signal (ECG). Heart-induced mechanical signals recorded from the body’s surface can be utilized to record the mechanical activity of the heart and, in turn, extract HRV indexes from interbeat intervals (IBIs). Among others, accelerometers and gyroscopes can be used to register IBIs from precordial accelerations and chest wall angular velocities. However, unlike electrical signals, the morphology of mechanical ones is strongly affected by body posture. In this paper, we investigated the feasibility of estimating the most common linear and non-linear HRV indexes from accelerometer and gyroscope data collected with a wearable skin-interfaced Inertial Measurement Unit (IMU) positioned at the xiphoid level. Data were collected from 21 healthy volunteers assuming two common postures (i.e., seated and lying). Results show that using the gyroscope signal in the lying posture allows accurate results in estimating IBIs, thus allowing extracting of linear and non-linear HRV parameters that are not statistically significantly different from those extracted from reference ECG.
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DUA, SUMEET, XIAN DU, S. VINITHA SREE, and THAJUDIN AHAMED V. I. "NOVEL CLASSIFICATION OF CORONARY ARTERY DISEASE USING HEART RATE VARIABILITY ANALYSIS." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1240017. http://dx.doi.org/10.1142/s0219519412400179.
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Coronary artery disease (CAD) is a leading cause of death worldwide. Heart rate variability (HRV) has been proven to be a non-invasive marker of the autonomic modulation of the heart. Nonlinear analyses of HRV signals have shown that the HRV is reduced significantly in patients with CAD. Therefore, in this work, we extracted nonlinear features from the HRV signals using the following techniques: recurrence plots (RP), Poincare plots, and detrended fluctuation analysis (DFA). We also extracted three types of entropy, namely, Shannon entropy (ShanEn), approximation entropy (ApEn), and sample entropy (SampEn). These features were subjected to principal component analysis (PCA). The significant principal components were evaluated using eight classification techniques, and the performances of these techniques were evaluated to determine which presented the highest accuracy in classifying normal and CAD classes. We observed that the multilayer perceptron (MLP) method resulted in the highest classification accuracy (89.5%) using our proposed technique.
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Naranjo-Hernández, David, Laura M. Roa, Javier Reina-Tosina, Gerardo Barbarov-Rostan, and Omar Galdámez-Cruz. "Smart Device for the Determination of Heart Rate Variability in Real Time." Journal of Sensors 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/8910470.
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This work presents a first approach to the design, development, and implementation of a smart device for the real-time measurement and detection of alterations in heart rate variability (HRV). The smart device follows a modular design scheme, which consists of an electrocardiogram (ECG) signal acquisition module, a processing module and a wireless communications module. From five-minute ECG signals, the processing module algorithms perform a spectral estimation of the HRV. The experimental results demonstrate the viability of the smart device and the proposed processing algorithms.
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Shaffer, Fred, and Didier C. Combatalade. "Don't Add or Miss a Beat: A Guide to Cleaner Heart Rate Variability Recordings." Biofeedback 41, no. 3 (September 1, 2013): 121–30. http://dx.doi.org/10.5298/1081-5937-41.3.04.
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Heart rate variability (HRV) refers to the beat-to-beat variation in the time intervals between heart contractions. This phenomenon reflects physiological processes that are trained in many biofeedback applications. HRV is routinely monitored using an electrocardiograph (ECG) or photoplethysmograph (PPG), supplemented by a respirometer. This article explains the importance of inspecting raw signals, describes the effects of prescription medications and social drugs, identifies common sources of signal contamination, and recommends practical precautions to increase recording fidelity.
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Han, Xiangyu, Qian Zhai, Ning Zhang, Xiufeng Zhang, Long He, Min Pan, Bin Zhang, and Tao Liu. "A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring." Sensors 23, no. 15 (July 26, 2023): 6681. http://dx.doi.org/10.3390/s23156681.
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Noncontact vital sign monitoring based on radar has attracted great interest in many fields. Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. This paper proposes a new algorithm for HRV monitoring in which frequency-modulated continuous-wave (FMCW) radar is used to separate echo signals from different distances, and the beamforming technique is adopted to improve signal quality. After the phase reflecting the chest wall motion is demodulated, the acceleration is calculated to enhance the heartbeat and suppress the impact of respiration. The time interval of each heartbeat is estimated based on the smoothed acceleration waveform. Finally, a joint optimization algorithm was developed and is used to precisely segment the acceleration signal for analyzing HRV. Experimental results from 10 participants show the potential of the proposed algorithm for obtaining a noncontact HRV estimation with high accuracy. The proposed algorithm can measure the interbeat interval (IBI) with a root mean square error (RMSE) of 14.9 ms and accurately estimate HRV parameters with an RMSE of 3.24 ms for MEAN (the average value of the IBI), 4.91 ms for the standard deviation of normal to normal (SDNN), and 9.10 ms for the root mean square of successive differences (RMSSD). These results demonstrate the effectiveness and feasibility of the proposed method in emotion recognition, sleep monitoring, and heart disease diagnosis.
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Cui, Xingran, Leirong Tian, Zhengwen Li, Zikai Ren, Keyang Zha, Xinruo Wei, and Chung-Kang Peng. "On the Variability of Heart Rate Variability—Evidence from Prospective Study of Healthy Young College Students." Entropy 22, no. 11 (November 15, 2020): 1302. http://dx.doi.org/10.3390/e22111302.
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Heart rate variability (HRV) has been widely used as indices for autonomic regulation, including linear analyses, entropy and multi-scale entropy based nonlinear analyses, and however, it is strongly influenced by the conditions under which the signal is being recorded. To investigate the variability of healthy HRV under different settings, we recorded electrocardiograph (ECG) signals from 56 healthy young college students (20 h for each participant) at campus using wearable single-lead ECG device. Accurate R peak to R peak (RR) intervals were extracted by combing the advantages of five commonly used R-peak detection algorithms to eliminate data quality influence. Thorough and detailed linear and nonlinear HRV analyses were performed. Variability of HRV metrics were evaluated from five categories: (1) different states of daily activities; (2) different recording time period in the same day during free-running daily activities; (3) body postures of sitting and lying; (4) lying on the left, right and back; and (5) gender influence. For most of the analyzed HRV metrics, significant differences (p < 0.05) were found among different recording conditions within the five categories except lying on different positions. Results suggested that the standardization of ECG data collection and HRV analysis should be implemented in HRV related studies, especially for entropy and multi-scale entropy based analyses. Furthermore, this preliminary study provides reference values of HRV indices under various recording conditions of healthy young subjects that could be useful information for different applications (e.g., health monitoring and management).
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Sarhaddi, Fatemeh, Kianoosh Kazemi, Iman Azimi, Rui Cao, Hannakaisa Niela-Vilén, Anna Axelin, Pasi Liljeberg, and Amir M. Rahmani. "A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability." PLOS ONE 17, no. 12 (December 8, 2022): e0268361. http://dx.doi.org/10.1371/journal.pone.0268361.
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Background Photoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and pulse rate variability (PRV) which widely used as a substitute of heart rate variability (HRV). The method is used in various wearable devices. For example, Samsung smartwatches are PPG-based open-source wristbands used in remote well-being monitoring and fitness applications. However, PPG is highly susceptible to motion artifacts and environmental noise. A validation study is required to investigate the accuracy of PPG-based wearable devices in free-living conditions. Objective We evaluate the accuracy of PPG signals—collected by the Samsung Gear Sport smartwatch in free-living conditions—in terms of HR and time-domain and frequency-domain HRV parameters against a medical-grade chest electrocardiogram (ECG) monitor. Methods We conducted 24-hours monitoring using a Samsung Gear Sport smartwatch and a Shimmer3 ECG device. The monitoring included 28 participants (14 male and 14 female), where they engaged in their daily routines. We evaluated HR and HRV parameters during the sleep and awake time. The parameters extracted from the smartwatch were compared against the ECG reference. For the comparison, we employed the Pearson correlation coefficient, Bland-Altman plot, and linear regression methods. Results We found a significantly high positive correlation between the smartwatch’s and Shimmer ECG’s HR, time-domain HRV, LF, and HF and a significant moderate positive correlation between the smartwatch’s and shimmer ECG’s LF/HF during sleep time. The mean biases of HR, time-domain HRV, and LF/HF were low, while the biases of LF and HF were moderate during sleep. The regression analysis showed low error variances of HR, AVNN, and pNN50, moderate error variances of SDNN, RMSSD, LF, and HF, and high error variances of LF/HF during sleep. During the awake time, there was a significantly high positive correlation of AVNN and a moderate positive correlation of HR, while the other parameters indicated significantly low positive correlations. RMSSD and SDNN showed low mean biases, and the other parameters had moderate mean biases. In addition, AVNN had moderate error variance while the other parameters indicated high error variances. Conclusion The Samsung smartwatch provides acceptable HR, time-domain HRV, LF, and HF parameters during sleep time. In contrast, during the awake time, AVNN and HR show satisfactory accuracy, and the other HRV parameters have high errors.
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M G Srinivasa, P S Pandian. "Application of Entropy Techniques in Analyzing Heart Rate Variabilityusing ECG Signals." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 1 (January 31, 2019): 09–16. http://dx.doi.org/10.17762/ijritcc.v7i1.5219.
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The variation of the heart rate about a mean value is the Heart Rate Variability (HRV). HRV reflects the functioning of cardio-respiratory control system. It is used as one of the diagnostic measures to detect heart disorders. In the proposed work, HRV analysis using entropy measures is carried out on healthy, Congestive Heart Failure (CHF) and Atrial Fibrillations (AF) subjects using their ECG signals. The entropy methods used in the work are Approximate entropy (ApE), Symbolic entropy (SyE) and Spectral entropy (SpE). ECG signals of 20 healthy subjects in the age group of 21 – 30 years were acquired using dry electrode at a sampling rate of 500 Hz for 10 minutes. Signal processing algorithms for removal of baseline wandering, power line interference and motion artefacts were applied for the raw ECG signal. The ECG signals for CHF and AF subjects in the age group of 30 – 75 years were obtained from the Physionet database. From the analysis it was found that values of ApE and SyE were highest for AF subjects and for SpE, the value was highest for healthy subjects. Further, values of all the three entropies were lowest for CHF subjects. In conclusion, it indicates that the entropy techniques are useful tools in diagnosing patients having heart disorders.
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Frasch, Martin Gerbert. "Heart Rate Variability Code: Does It Exist and Can We Hack It?" Bioengineering 10, no. 7 (July 10, 2023): 822. http://dx.doi.org/10.3390/bioengineering10070822.
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A code is generally defined as a system of signals or symbols for communication. Experimental evidence is synthesized for the presence and utility of such communication in heart rate variability (HRV) with particular attention to fetal HRV: HRV contains signatures of information flow between the organs and of response to physiological or pathophysiological stimuli as signatures of states (or syndromes). HRV exhibits features of time structure, phase space structure, specificity with respect to (organ) target and pathophysiological syndromes, and universality with respect to species independence. Together, these features form a spatiotemporal structure, a phase space, that can be conceived of as a manifold of a yet-to-be-fully understood dynamic complexity. The objective of this article is to synthesize physiological evidence supporting the existence of HRV code: hereby, the process-specific subsets of HRV measures indirectly map the phase space traversal reflecting the specific information contained in the code required for the body to regulate the physiological responses to those processes. The following physiological examples of HRV code are reviewed, which are reflected in specific changes to HRV properties across the signal–analytical domains and across physiological states and conditions: the fetal systemic inflammatory response, organ-specific inflammatory responses (brain and gut), chronic hypoxia and intrinsic (heart) HRV (iHRV), allostatic load (physiological stress due to surgery), and vagotomy (bilateral cervical denervation). Future studies are proposed to test these observations in more depth, and the author refers the interested reader to the referenced publications for a detailed study of the HRV measures involved. While being exemplified mostly in the studies of fetal HRV, the presented framework promises more specific fetal, postnatal, and adult HRV biomarkers of health and disease, which can be obtained non-invasively and continuously.
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Pei, Zejun, Manhong Shi, Junping Guo, and Bairong Shen. "Heart Rate Variability Based Prediction of Personalized Drug Therapeutic Response: The Present Status and the Perspectives." Current Topics in Medicinal Chemistry 20, no. 18 (August 24, 2020): 1640–50. http://dx.doi.org/10.2174/1568026620666200603105002.
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Heart rate variability (HRV) signals are reported to be associated with the personalized drug response in many diseases such as major depressive disorder, epilepsy, chronic pain, hypertension, etc. But the relationships between HRV signals and the personalized drug response in different diseases and patients are complex and remain unclear. With the fast development of modern smart sensor technologies and the popularization of big data paradigm, more and more data on the HRV and drug response will be available, it then provides great opportunities to build models for predicting the association of the HRV with personalized drug response precisely. We here review the present status of the HRV data resources and models for predicting and evaluating of personalized drug responses in different diseases. The future perspectives on the integration of knowledge and personalized data at different levels such as, genomics, physiological signals, etc. for the application of HRV signals to the precision prediction of drug therapy and their response will be provided.
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H.Almourish, Mohammed, Nishwan A.Al-khulaidi, Amin A. Mokbel, and Ahmed Y. A. Saeed. "The Classification of the Heart Rate Variability Using Radon Transform with Back-Propagation Neural Networks." International Journal of Innovative Science and Research Technology 5, no. 6 (July 9, 2020): 974–78. http://dx.doi.org/10.38124/ijisrt20jun872.
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This paper will present an algorithm for Heart Rate Variability HRV signals classifications. In this algorithm we used Radon transform of binary matrix of scatter-gram of heart rate HRV signals to extract features of binary matrix. Artificial neural network (ANN) technique with back-propagation networks (BPN) was used for binary matrix features classifications. Radon transform with 90 projections was selected because it presented the best inverse Radon transform that gave a closer image of the original scatter-gram. The optimum numbers of neurons in the hidden layer of BPN is 145 was obtained. Two databases were formed, one for training and the second for testing the accuracy of the BPN to recognize on types of heart rate variability. The two database consist of HRV signal pathologies, sympathetic activity, normal cardiac, parasympathetic activity, arrhythmia, availability problem with breath, existence of stress and the composition of these pathologies. This algorithm present the accuracy of diagnosis for sympathetic activity, normal cardiac, parasympathetic activity, arrhythmia, availability problem with breath and existence of stress were 97,396%, 98,438%, 100%, 94,792%, 87,3265% and 91,146% respectively.
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Bogucki, Sz, and A. Noszczyk-Nowak. "Short-term heart rate variability (HRV) in healthy dogs." Polish Journal of Veterinary Sciences 18, no. 2 (June 1, 2015): 307–12. http://dx.doi.org/10.1515/pjvs-2015-0040.
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AbstractHeart rate variability (HRV) is a well established mortality risk factor in both healthy dogs and those with heart failure. While the standards for short-term HRV analysis have been developed in humans, only reference values for HRV parameters determined from 24-hour ECG have been proposed in dogs. The aim of this study was to develop the reference values for short-term HRV parameters in a group of 50 healthy dogs of various breeds (age 4.86 ± 2.74 years, body weight 12.2 ± 3.88 kg). The ECG was recorded continuously for at least 180 min in a dark and quiet room. All electrocardiograms were inspected automatically and manually to eliminate atrial or ventricular premature complexes. Signals were transformed into a spectrum using the fast Fourier transform. The HRV parameters were measured at fixed times from 60-min ECG segments. The following time-domain parameters (ms) were analyzed: mean NN, SDNN, SDANN, SDNN index, rMSSD and pNN50. Moreover, frequency-domain parameters (Hz) were determined, including very low frequency (VLF), low frequency (LF) and high frequency (HF) components, total power (TP) and the LF/HF ratio. The results (means ± SD) were as follows: mean NN = 677.68 ± 126.89; SDNN = 208.86 ± 77.1; SDANN = 70.75 ± 30.9; SDNN index = 190.75 ± 76.12; rMSSD = 259 ± 120.17, pNN50 = 71.84 ± 13.96; VLF = 984.96 ± 327.7; LF = 1501.24 ± 736.32; HF = 5845.45 ± 2914.20; TP = 11065.31 ± 3866.87; LF/HF = 0.28 ± 0.11.
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Pellegrino, Peter R., Alicia M. Schiller, and Irving H. Zucker. "Validation of pulse rate variability as a surrogate for heart rate variability in chronically instrumented rabbits." American Journal of Physiology-Heart and Circulatory Physiology 307, no. 1 (July 1, 2014): H97—H109. http://dx.doi.org/10.1152/ajpheart.00898.2013.
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Heart rate variability (HRV) is a function of cardiac autonomic tone that is widely used in both clinical and animal studies. In preclinical studies, HRV measures are frequently derived using the arterial pulse waveform from an implanted pressure telemetry device, termed pulse rate variability (PRV), instead of the electrocardiogram signal in accordance with clinical guidelines. The acceptability of PRV as a surrogate for HRV in instrumented animals is unknown. Using rabbits implanted with intracardiac leads and chronically implanted pressure transducers, we investigated the correlation and agreement of time-domain, frequency-domain, and nonlinear indexes of HRV and PRV at baseline. We also investigated the effects of ventricular pacing and autonomic blockade on both measures. At baseline, HRV and PRV time- and frequency-domain parameters showed robust correlations and moderate to high agreement, whereas nonlinear parameters showed slightly weaker correlations and varied agreement. Ventricular pacing almost completely eliminated HRV, and spectral analysis of the PRV signal revealed a HRV-independent rhythm. After cardiac autonomic blockade with atropine or metoprolol, the changes in time- and non-normalized frequency-domain measures of PRV continued to show strong correlations and moderate to high agreement with corresponding changes in HRV measures. Blockade-induced changes in nonlinear PRV indexes correlated poorly with HRV changes and showed weak agreement. These results suggest that time- and frequency-domain measures of PRV are acceptable surrogates for HRV even in the context of changing cardiac autonomic tone, but caution should be used when nonlinear measures are a primary end point or when HRV is very low as HRV-independent rhythms may predominate.
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Chang, Jung-Chi, Wei-Lieh Huang, Chao-Yu Liu, Meg Mei-Chih Tseng, Cheryl C. H. Yang, and Terry B. J. Kuo. "Heart Rate Variability Reactivity to Food Image Stimuli is Associated with Body Mass Index." Applied Psychophysiology and Biofeedback 46, no. 3 (May 22, 2021): 271–77. http://dx.doi.org/10.1007/s10484-021-09514-2.
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AbstractAppetitive control is driven by the hedonic response to food and affected by several factors. Heart rate variability (HRV) signals have been used to index autonomic activity and arousal levels towards visual stimuli. The current research aimed to examine the influence of body mass index (BMI), disordered eating behaviors, and sex on the HRV reactivity to food in a nonclinical sample. Thirty-eight healthy male and sixty-one healthy female participants completed questionnaires assessing disordered eating symptoms. HRV was recorded when the participants received visual stimuli of high-calorie food, neutral and negative emotional signals. Generalized estimating equation models were used to investigate the associations between HRV, BMI, disordered eating behaviors, and sex across the three stimulus types. Male participants demonstrated a higher ratio of low-frequency power to high-frequency power (LF/HF) than females across all the stimulus types. An increase in LF/HF reactivity to food signals was observed in all the study subjects. The moderation effect of BMI on LF/HF in response to food signals was also observed. Our study suggests that body weight may play a role in the interaction between sympathetic activity and food stimuli; however, how the interaction between sympathetic activity and food stimuli contributes to diet control warrants further investigation.
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Gospodinova, Evgeniya. "Methods of Nonlinear Dynamics for Heart Rate Variability Analysis." Innovative STEM Education 4, no. 1 (June 10, 2022): 32–38. http://dx.doi.org/10.55630/stem.2022.0405.
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The heart rate variability (HRV) analysis, based on the methods of nonlinear dynamics, can provide important information for the physiological interpretation of the functioning of the cardiovascular system and assess the risk of its pathology. The article presents methods for nonlinear analysis of HRV, united in the following groups: fractal, multifractal, graphical and informational. The application of the methods of nonlinear dynamics in the study of the information characteristics of HRV in order to distinguish healthy subjects from sick ones is an important topic from the point of view of the application of the information technologies in the field of non-invasive cardiology. After determining the values of the studied parameters with the developed software and for the distinction of the two studied groups of subjects (healthy controls and patients with arrhythmia) statistical analysis was applied. The statistical analysis was performed by t-test and receiver operating characteristic (ROC) analysis. ROC curves are constructed and the area under the curves is calculated, on the basis of which the quality of the studied methods is evaluated. The results reported in this study may be useful in classifying the states of electrocardiographic signals and serve as a landmark for comparing healthy individuals to individuals with cardiovascular disease. The high information content of the used nonlinear methods for HRV analysis opens perspectives for their future use in the diagnosis and prognosis of cardiovascular diseases.
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Staderini, Enrico M., Harish Kambampati, Amith K. Ramakrishnaiah, Stefano Mugnaini, Andrea Magrini, and Sandro Gentili. "A New Algorithm for Estimating a Noiseless, Evenly Sampled, Heart Rate Modulating Signal." Bioengineering 10, no. 5 (May 4, 2023): 552. http://dx.doi.org/10.3390/bioengineering10050552.
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Heart rate variability (HRV) is commonly intended as the variation in the heart rate (HR), and it is evaluated in the time and frequency domains with various well-known methods. In the present paper, the heart rate is considered as a time domain signal, at first as an abstract model in which the HR is the instantaneous frequency of an otherwise periodic signal, such as with an electrocardiogram (ECG). In this model, the ECG is assumed to be a frequency modulated signal, or carrier signal, where HRV or HRV(t) is the time-domain signal which is frequency modulating the carrier ECG signal around its average frequency. Hence, an algorithm able to frequency demodulate the ECG signal to extract the signal HRV(t) is described, with possibly enough time resolution to analyse fast time-domain variations in the instantaneous HR. After exhaustive testing of the method on simulated frequency modulated sinusoidal signals, the new procedure is eventually applied on actual ECG tracings for preliminary nonclinical testing. The purpose of the work is to use this algorithm as a tool and a more reliable method for the assessment of heart rate before any further clinical or physiological analysis.
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Izzah, Nailul, Auditya Purwandini Sutarto, and Mohamad Hariyadi. "Machine Learning models for the Cognitive Stress Detection Using Heart Rate Variability Signals." Jurnal Teknik Industri 24, no. 2 (November 24, 2022): 83–94. http://dx.doi.org/10.9744/jti.24.2.83-94.
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Cognitive domains play a critical role in daily functioning. The prediction of cognitive stress state is important to better monitor work performance. This study aims to explore machine learning models to detect cognitive load or state using heart rate variability (HRV) signals. HRV data were recorded from thirty subjects during rest, two cognitive tasks (d2 Attention and Featuring Switcher task), and recovery. Seven HRV indexes from both time and frequency domains, extracted from raw R-R intervals, were used to identify whether subjects performed cognitive tasks or not. Five classifier models: linear support vector machine (LSVM), kernel SVM radial basis function, k-nearest neighbor (KNN), and random forest (RF), were trained and evaluated using a leave-one-out cross-validation approach. The accuracies and F1-score range from 0.54 to 0.62, with LSVM, showing the best. These acceptable performances indicate the machine learning approach could be used to further distinguish between rest and cognitive state. With the ubiquity of non-invasive and low-cost wearable devices, this finding offers insight to be incorporated into personal work performance monitoring in the digital age.
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Ritsert, Florian, Mohamed Elgendi, Valeria Galli, and Carlo Menon. "Heart and Breathing Rate Variations as Biomarkers for Anxiety Detection." Bioengineering 9, no. 11 (November 19, 2022): 711. http://dx.doi.org/10.3390/bioengineering9110711.
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With advances in portable and wearable devices, it should be possible to analyze and interpret the collected biosignals from those devices to tailor a psychological intervention to help patients. This study focuses on detecting anxiety by using a portable device that collects electrocardiogram (ECG) and respiration (RSP) signals. The feature extraction focused on heart-rate variability (HRV) and breathing-rate variability (BRV). We show that a significant change in these signals occurred between the non-anxiety-induced and anxiety-induced states. The HRV biomarkers were the mean heart rate (MHR; p¯ = 0.04), the standard deviation of the heart rate (SD; p¯ = 0.01), and the standard deviation of NN intervals (SDNN; p¯ = 0.03) for ECG signals, and the mean breath rate (MBR; p¯ = 0.002), the standard deviation of the breath rate (SD; p¯ < 0.0001), the root mean square of successive differences (RMSSD; p¯ < 0.0001) and SDNN (p¯ < 0.0001) for RSP signals. This work extends the existing literature on the relationship between stress and HRV/BRV by being the first to introduce a transitional phase. It contributes to systematically processing mental and emotional impulse data in humans measured via ECG and RSP signals. On the basis of these identified biomarkers, artificial-intelligence or machine-learning algorithms, and rule-based classification, the automated biosignal-based psychological assessment of patients could be within reach. This creates a broad basis for detecting and evaluating psychological abnormalities in individuals upon which future psychological treatment methods could be built using portable and wearable devices.
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Long, Xi, Pedro Fonseca, Reinder Haakma, Ronald M. Aarts, and Jerome Foussier. "Spectral Boundary Adaptation on Heart Rate Variability for Sleep and Wake Classification." International Journal on Artificial Intelligence Tools 23, no. 03 (May 28, 2014): 1460002. http://dx.doi.org/10.1142/s0218213014600021.
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A method of adapting the boundaries when extracting the spectral features from heart rate variability (HRV) for sleep and wake classification is described. HRV series can be derived from electrocardiogram (ECG) signals obtained from single-night polysomnography (PSG) recordings. Conventionally, the HRV spectral features are extracted from the spectrum of an HRV series with fixed boundaries specifying bands of very low frequency (VLF), low frequency (LF), and high frequency (HF). However, because they are fixed, they may fail to accurately reflect certain aspects of autonomic nervous activity which in turn may limit their discriminative power, e.g. in sleep and wake classification. This is in part related to the fact that the sympathetic tone (partially reflected in the LF band) and the respiratory activity (modulated in the HF band) vary over time. In order to minimize the impact of these variations, we adapt the HRV spectral boundaries using time-frequency analysis. Experiments were conducted on a data set acquired from two groups with 15 healthy and 15 insomnia subjects each. Results show that adapting the HRV spectral features significantly increased their discriminative power when classifying sleep and wake. Additionally, this method also provided a significant improvement of the overall classification performance when used in combination with other HRV non-spectral features. Furthermore, compared with the use of actigraphy, the classification performed better when combining it with the HRV features.
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Panjaitan, Febriyanti, Siti Nurmaini, and Radiyati Umi Partan. "Accurate Prediction of Sudden Cardiac Death Based on Heart Rate Variability Analysis Using Convolutional Neural Network." Medicina 59, no. 8 (July 29, 2023): 1394. http://dx.doi.org/10.3390/medicina59081394.
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Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and without a history of heart disease. Early identification of SCD risk factors is crucial in reducing mortality rates. This study aims to utilize electrocardiogram (ECG) tools, specifically focusing on heart rate variability (HRV), to detect early SCD risk factors. In this study, we expand the comparison group dataset to include five groups: Normal Sinus Rhythm (NSR), coronary artery disease (CAD), Congestive Heart Failure (CHF), Ventricular Tachycardia (VT), and SCD. ECG signals were recorded for 30 min and segmented into 5 min intervals, following the recommended HRV feature analysis guidelines. We introduce an innovative approach to HRV signal analysis by utilizing Convolutional Neural Networks (CNN). The CNN model was optimized by tuning hyperparameters such as the number of layers, learning rate, and batch size, significantly impacting the prediction accuracy. The findings demonstrate that the HRV approach, in conjunction with linear features and the DL method, achieved a higher accuracy rate, averaging 99.30%, reaching 97% sensitivity, 99.60% specificity, and 97.87% precision. Future research should focus on further exploring and refining DL methods in the context of HRV analysis to improve SCD prediction.
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Mainardi, Luca T. "On the quantification of heart rate variability spectral parameters using time–frequency and time-varying methods." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1887 (October 20, 2008): 255–75. http://dx.doi.org/10.1098/rsta.2008.0188.
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In the last decades, one of the main challenges in the study of heart rate variability (HRV) signals has been the quantification of the low-frequency (LF) and high-frequency (HF) components of the HRV spectrum during non-stationary events. At this regard, different time–frequency and time-varying approaches have been proposed with the aim to track the modification of the HRV spectra during ischaemic attacks, provocative stress testing, sleep or daily-life activities. The quantitative evaluation of power (and frequencies) of the LF and HF components has been approached in various ways depending on the selected time–frequency method. This paper is an excursus through the most common time–frequency/time-varying representation of the HRV signal with a special emphasis on the algorithms employed for the reliable quantification of the LF and HF parameters and their tracking.
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Yamamoto, Y., J. O. Fortrat, and R. L. Hughson. "On the fractal nature of heart rate variability in humans: effects of respiratory sinus arrhythmia." American Journal of Physiology-Heart and Circulatory Physiology 269, no. 2 (August 1, 1995): H480—H486. http://dx.doi.org/10.1152/ajpheart.1995.269.2.h480.
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The purpose of the present study was to investigate the basic fractal nature of the variability in resting heart rate (HRV), relative to that in breathing frequency (BFV) and tidal volume (TVV), and to test the hypothesis that fractal HRV is due to the fractal BFV and/or TVV in humans. In addition, the possible fractal nature of respiratory volume curves (RVC) and HRV was observed. In the first study, eight subjects were tested while they sat quietly in a comfortable chair for 60 min. Beat-to-beat R-R intervals, i.e., HRV, and breath-by-breath BFV and TVV were measured. In the second study, six subjects were tested while they were in the supine position for 20-30 min. The RVC was monitored continuously together with HRV. Coarse-graining spectral analysis (Yamamoto, Y., and R. L. Hughson, Physica D 68: 250-264, 1993) was applied to these signals to evaluate the percentage of random fractal components in the time series (%Fractal) and the spectral exponent (beta), which characterizes irregularity of the signals. The estimates of beta were determined for each variable only over the range normally used to evaluate HRV. Values for %Fractal and beta of both BFV and TVV were significantly (P < 0.05) greater than those for HRV. In addition, there was no significant (P > 0.05) correlation between the beta values of HRV relative to either BFV (r = 0.14) or TVV (r = 0.34). RVC showed a smooth oscillation as compared with HRV; %Fractal for RVC (42.3 +/- 21.7%, mean +/- SD) was significantly (P < 0.05) lower than that for HRV (78.5 +/- 4.2%).(ABSTRACT TRUNCATED AT 250 WORDS)
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Weiner, Oren M., and Jennifer J. McGrath. "Test-Retest Reliability of Pediatric Heart Rate Variability." Journal of Psychophysiology 31, no. 1 (January 2017): 6–28. http://dx.doi.org/10.1027/0269-8803/a000161.
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Abstract. Heart rate variability (HRV), an established index of autonomic cardiovascular modulation, is associated with health outcomes (e.g., obesity, diabetes) and mortality risk. Time- and frequency-domain HRV measures are commonly reported in longitudinal adult and pediatric studies of health. While test-retest reliability has been established among adults, less is known about the psychometric properties of HRV among infants, children, and adolescents. The objective was to conduct a meta-analysis of the test-retest reliability of time- and frequency-domain HRV measures from infancy to adolescence. Electronic searches (PubMed, PsycINFO; January 1970–December 2014) identified studies with nonclinical samples aged ≤ 18 years; ≥ 2 baseline HRV recordings separated by ≥ 1 day; and sufficient data for effect size computation. Forty-nine studies (N = 5,170) met inclusion criteria. Methodological variables coded included factors relevant to study protocol, sample characteristics, electrocardiogram (ECG) signal acquisition and preprocessing, and HRV analytical decisions. Fisher’s Z was derived as the common effect size. Analyses were age-stratified (infant/toddler < 5 years, n = 3,329; child/adolescent 5–18 years, n = 1,841) due to marked methodological differences across the pediatric literature. Meta-analytic results revealed HRV demonstrated moderate reliability; child/adolescent studies (Z = 0.62, r = 0.55) had significantly higher reliability than infant/toddler studies (Z = 0.42, r = 0.40). Relative to other reported measures, HF exhibited the highest reliability among infant/toddler studies (Z = 0.42, r = 0.40), while rMSSD exhibited the highest reliability among child/adolescent studies (Z = 1.00, r = 0.76). Moderator analyses indicated greater reliability with shorter test-retest interval length, reported exclusion criteria based on medical illness/condition, lower proportion of males, prerecording acclimatization period, and longer recording duration; differences were noted across age groups. HRV is reliable among pediatric samples. Reliability is sensitive to pertinent methodological decisions that require careful consideration by the researcher. Limited methodological reporting precluded several a priori moderator analyses. Suggestions for future research, including standards specified by Task Force Guidelines, are discussed.
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Yamamoto, Y., and R. L. Hughson. "Coarse-graining spectral analysis: new method for studying heart rate variability." Journal of Applied Physiology 71, no. 3 (September 1, 1991): 1143–50. http://dx.doi.org/10.1152/jappl.1991.71.3.1143.
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Heart rate variability (HRV) spectra are typically analyzed for the components related to low- (less than 0.15 Hz) and high- (greater than 0.15 Hz) frequency variations. However, there are very-low-frequency components with periods up to hours in HRV signals, which might smear short-term spectra. We developed a method of spectral analysis suitable for selectively extracting very-low-frequency components, leaving intact the low- and high-frequency components of interest in HRV spectral analysis. Computer simulations showed that those low-frequency components were well characterized by fractional Brownian motions (FBMs). If the scale invariant, or self-similar, property of FBMs is considered a new time series (x′) was constructed by sampling only every other point (course graining) of the original time series (x). Evaluation of the cross-power spectra between these two (Sxx′) showed that the power of the FBM components was preserved, whereas that of the harmonic components vanished. Subtraction of magnitude of Sxx from the autopower spectra of the original sequence emphasized only the harmonic components. Application of this method to HRV spectral analyses indicated that it might enable one to observe more clearly the low- and high-frequency components characteristic of autonomic control of heart rate.
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Nikolaou, F., C. Orphanidou, P. Papakyriakou, K. Murphy, R. G. Wise, and G. D. Mitsis. "Spontaneous physiological variability modulates dynamic functional connectivity in resting-state functional magnetic resonance imaging." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2067 (May 13, 2016): 20150183. http://dx.doi.org/10.1098/rsta.2015.0183.
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It is well known that the blood oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI) is influenced—in addition to neuronal activity—by fluctuations in physiological signals, including arterial CO 2 , respiration and heart rate/heart rate variability (HR/HRV). Even spontaneous fluctuations of the aforementioned physiological signals have been shown to influence the BOLD fMRI signal in a regionally specific manner. Related to this, estimates of functional connectivity between different brain regions, performed when the subject is at rest, may be confounded by the effects of physiological signal fluctuations. Moreover, resting functional connectivity has been shown to vary with respect to time (dynamic functional connectivity), with the sources of this variation not fully elucidated. In this context, we examine the relation between dynamic functional connectivity patterns and the time-varying properties of simultaneously recorded physiological signals (end-tidal CO 2 and HR/HRV) using resting-state fMRI measurements from 12 healthy subjects. The results reveal a modulatory effect of the aforementioned physiological signals on the dynamic resting functional connectivity patterns for a number of resting-state networks (default mode network, somatosensory, visual). By using discrete wavelet decomposition, we also show that these modulation effects are more pronounced in specific frequency bands.
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Zhang, Lulu, Mingyu Fu, Fengguo Xu, Fengzhen Hou, and Yan Ma. "Heart Rate Dynamics in Patients with Obstructive Sleep Apnea: Heart Rate Variability and Entropy." Entropy 21, no. 10 (September 24, 2019): 927. http://dx.doi.org/10.3390/e21100927.
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Background: Obstructive sleep apnea (OSA), a highly prevalent sleep disorder, is closely related to cardiovascular disease (CVD). Our previous work demonstrated that Shannon entropy of the degree distribution (EDD), obtained from the network domain of heart rate variability (HRV), might be a potential indicator for CVD. Method: To investigate the potential association between OSA and EDD, OSA patients and healthy controls (HCs) were identified from a sleep study database. Then EDD was calculated from electrocardiogram (ECG) signals during sleep, followed by cross-sectional comparisons between OSA patients and HCs, and longitudinal comparisons from baseline to follow-up visits. Furthermore, for OSA patients, the association between EDD and OSA severity, measured by apnea-hypopnea index (AHI), was also analyzed. Results: Compared with HCs, OSA patients had significantly increased EDD during sleep. A positive correlation between EDD and the severity of OSA was also observed. Although the value of EDD became larger with aging, it was not OSA-specified. Conclusion: Increased EDD derived from ECG signals during sleep might be a potential dynamic biomarker to identify OSA patients from HCs, which may be used in screening OSA with high risk before polysomnography is considered.
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Chou, Yongxin, Ruilei Zhang, Yufeng Feng, Mingli Lu, Zhenli Lu, and Benlian Xu. "A Real-Time Analysis Method for Pulse Rate Variability Based on Improved Basic Scale Entropy." Journal of Healthcare Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/7406896.
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Base scale entropy analysis (BSEA) is a nonlinear method to analyze heart rate variability (HRV) signal. However, the time consumption of BSEA is too long, and it is unknown whether the BSEA is suitable for analyzing pulse rate variability (PRV) signal. Therefore, we proposed a method named sliding window iterative base scale entropy analysis (SWIBSEA) by combining BSEA and sliding window iterative theory. The blood pressure signals of healthy young and old subjects are chosen from the authoritative international database MIT/PhysioNet/Fantasia to generate PRV signals as the experimental data. Then, the BSEA and the SWIBSEA are used to analyze the experimental data; the results show that the SWIBSEA reduces the time consumption and the buffer cache space while it gets the same entropy as BSEA. Meanwhile, the changes of base scale entropy (BSE) for healthy young and old subjects are the same as that of HRV signal. Therefore, the SWIBSEA can be used for deriving some information from long-term and short-term PRV signals in real time, which has the potential for dynamic PRV signal analysis in some portable and wearable medical devices.
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Wang, Jian, Houqin Wang, Yuemei Luo, Hongying Tang, Hongwei Mao, and Shubo Bi. "Psychological stress recognition from heart rate variability parameters based on field programmable gate arrays." Review of Scientific Instruments 93, no. 11 (November 1, 2022): 115107. http://dx.doi.org/10.1063/5.0118630.
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Psychological stress is a big threat to people’s health. Early detection of psychological stress is important. The design of a stress recognition device based on the ECG (electrocardiograph) signal is presented in this paper. The device features intelligence, precision, portability, fast response, and low power consumption. In the design, the ECG signals are acquired by the AD8232 ECG module and processed by a low power consumption FPGA (Field Programmable Gated Array) development board PYNQ-Z2. Meanwhile, a modified Deep Forest model named Aw-Deep Forest (Adaptive Weight Deep Forest) is proposed. The Aw-Deep Forest has better performance than the Deep Forest model because it improves the fitting quality of the forests. By implementing the Aw-Deep Forest model on the FPGA, the device can assess people’s state of psychological stress by analyzing the HRV (heart rate variability) parameters from ECG data. This paper mainly introduces the detailed process of ECG signal collecting, filtering, analog signal to digital signal conversion, HRV parameter analysis, and psychological stress recognition with Aw-Deep Forest. The final accuracy is 81.39%.
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Ara, Iffat. "Wavelet Transform Based Heart Rate Variability Analysis of ECG." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 4 (November 30, 2022): 19–22. http://dx.doi.org/10.35940/ijrte.d7294.1111422.
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Electrocardiography (ECG) is recording of heart electrical activity. For analyzing and diagnosis of heart diseases ECG is very important. In graphical ECG which used for clinical diagnosis all features are not visible. Different types of signal processing methods are present which can be used for extracting ECG signal features. Wavelet transforms is one kind of signal processing tool which is used for analyzing ECG signal. For features extraction multi-resolution wavelet transform can be used. During recording of ECG different kind of noise are added with ECG. So noise should be removed from ECG, than R peaks were detected which amplitude is higher than the other peaks. Referring to R peaks the others peak as P, Q, S and T were detected. Then different feature of the ECG signal were detected. Time differences between R peaks were calculated and then heart rate calculated from mean RR interval. In ECG RR interval indicate the change between consecutive heart rate (HR). Heart rate variability (HRV) explored how RR interval varies over time. HRV is calculated from RR interval series obtained from ECG signal analysis. From the RR intervals time domain indices of HRV were determined by using MATLAB programming and MIT-BIH database signal were used as input. In the time domain method SDNN, RMSSD, and pNN50 etc were determined here.
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Kazmi, Syed Zaki Hassan, Nazneen Habib, Rabia Riaz, Sanam Shahla Rizvi, Syed Ali Abbas, and Tae-Sun Chung. "Multiscale based nonlinear dynamics analysis of heart rate variability signals." PLOS ONE 15, no. 12 (December 17, 2020): e0243441. http://dx.doi.org/10.1371/journal.pone.0243441.
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Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects. In this study, a novel approach multiscale ACI (MACI) is proposed by incorporating multiple time scales for improving the classification ability of ACI. We evaluated the performance of MACI for classifying, normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation subjects. The findings reveal that MACI provided better classification between healthy and pathological subjects compared to ACI. We also compared MACI with other scale-based techniques such as multiscale entropy, multiscale permutation entropy (MPE), multiscale normalized corrected Shannon entropy (MNCSE) and multiscale permutation entropy (IMPE). The preliminary results show that MACI values are more stable and reliable than IMPE and MNCSE. The results show that MACI based features lead to higher classification accuracy.
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Zhao, Lina, Peng Li, Jianqing Li, and Chengyu Liu. "Influence of Ectopic Beats on Heart Rate Variability Analysis." Entropy 23, no. 6 (May 22, 2021): 648. http://dx.doi.org/10.3390/e23060648.
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The analysis of heart rate variability (HRV) plays a dominant role in the study of physiological signal variability. HRV reflects the information of the adjustment of sympathetic and parasympathetic nerves on the cardiovascular system and, thus, is widely used to evaluate the functional status of the cardiovascular system. Ectopic beats may affect the analysis of HRV. However, the quantitative relationship between the burden of ectopic beats and HRV indices, including entropy measures, has not yet been investigated in depth. In this work, we analyzed the effects of different numbers of ectopic beats on several widely accepted HRV parameters in time-domain (SDNN), frequency-domain (LF/HF), as well as non-linear features (SampEn and Pt-SampEn (physical threshold-based SampEn)). The results showed that all four indices were influenced by ectopic beats, and the degree of influence was roughly increased with the increase of the number of ectopic beats. Ectopic beats had the greatest impact on the frequency domain index LF/HF, whereas the Pt-SampEn was minimally accepted by ectopic beats. These results also indicated that, compared with the other three indices, Pt-SampEn had better robustness for ectopic beats.
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Hsu, Hsiu-Chin, Hsiu-Fang Lee, and Mei-Hsiang Lin. "Exploring the Association between Sleep Quality and Heart Rate Variability among Female Nurses." International Journal of Environmental Research and Public Health 18, no. 11 (May 22, 2021): 5551. http://dx.doi.org/10.3390/ijerph18115551.
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The quality of nurses’ work has a direct effect on patient health, and poor sleep has been positively associated with nurses’ medical errors. The aim of this study was to investigate the relationship between quality of sleep and heart rate variability (HRV) among female nurses. A descriptive cross-sectional correlational study design was used in January 2014 to study female nurses (n = 393) employed in a medical center in Taiwan. Data were obtained from several questionnaires. HRV was analyzed with five-minute recordings of heart rate signals obtained using a Heart Rater SA-3000P. Approximately 96% of the participants self-reported a poor quality of sleep. Compared to non-shift nurses, significant decreases were found in total power (TP) and low-frequency HRV among shift-work nurses. However, negative correlations were found between sleep quality and HRV, including total power, low frequency, and the low frequency/high frequency ratio (r = −0.425, p < 0.05; r = −0.269, −0.266, p < 0.05). In a stepwise multiple regression analysis, 23.1% of variance in quality of sleep can be explained by TP and heart rate. The sleep quality of female nurses was poor and this affected their autonomic nervous system, which can contribute unfavorable consequences for their health.
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Fairchild, Karen D., Varadamurthy Srinivasan, J. Randall Moorman, Ronald P. A. Gaykema, and Lisa E. Goehler. "Pathogen-induced heart rate changes associated with cholinergic nervous system activation." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 300, no. 2 (February 2011): R330—R339. http://dx.doi.org/10.1152/ajpregu.00487.2010.
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The autonomic nervous system plays a central role in regulation of host defense and in physiological responses to sepsis, including changes in heart rate and heart rate variability. The cholinergic anti-inflammatory response, whereby infection triggers vagal efferent signals that dampen production of proinflammatory cytokines, would be predicted to result in increased vagal signaling to the heart and increased heart rate variability. In fact, decreased heart rate variability is widely described in humans with sepsis. Our studies elucidate this apparent paradox by showing that mice injected with pathogens demonstrate transient bradyarrhythmias of vagal origin in a background of decreased heart rate variability (HRV). Intraperitoneal injection of a large inoculum of Gram-positive or Gram-negative bacteria or Candida albicans rapidly induced bradyarrhythmias of sinus and AV nodal block, characteristic of cardiac vagal firing and dramatically increased short-term HRV. These pathogen-induced bradycardias were immediately terminated by atropine, an antagonist of muscarinic cholinergic receptors, demonstrating the role of vagal efferent signaling in this response. Vagal afferent signaling following pathogen injection was demonstrated by intense nuclear c-Fos activity in neurons of the vagal sensory ganglia and brain stem. Surprisingly, pathogen-induced bradycardia demonstrated rapid and prolonged desensitization and did not recur on repeat injection of the same organism 3 h or 3 days after the initial exposure. After recovery from the initial bradycardia, depressed heart rate variability developed in some mice and was correlated with elevated plasma cytokine levels and mortality. Our findings of decreased HRV and transient heart rate decelerations in infected mice are similar to heart rate changes described by our group in preterm neonates with sepsis. Pathogen sensing and signaling via the vagus nerve, and the desensitization of this response, may account for periods of both increased and decreased heart rate variability in sepsis.
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Carnevali, Luca, Rosario Statello, and Andrea Sgoifo. "Resting Heart Rate Variability Predicts Vulnerability to Pharmacologically-Induced Ventricular Arrhythmias in Male Rats." Journal of Clinical Medicine 8, no. 5 (May 10, 2019): 655. http://dx.doi.org/10.3390/jcm8050655.
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The electrical stability of the myocardium is dependent on the dynamic balance between sympathetic and parasympathetic influences on the heart, which is reflected by heart rate variability (HRV). Reduced HRV is a proposed predictor of sudden death caused by ventricular tachyarrhythmias in cardiac patients. However, the link between individual differences in HRV and ventricular tachyarrhythmic risk in populations without known pre-existing cardiac conditions is less well explored. In this study we investigated the extent to which individual differences in resting state HRV predict susceptibility to spontaneous and pharmacologically-induced ventricular arrhythmias in healthy rats. Radiotelemetric transmitters were implanted in 42 adult male Wild-type Groningen rats. ECG signals were recorded during 24-h resting conditions and under β-adrenoceptor pharmacological stimulation with isoproterenol and analyzed by means of time- and frequency-domain indexes of HRV. No significant association was found between individual differences in resting measures of HRV and spontaneous incidence of ventricular arrhythmias. However, lower resting values of HRV predicted a higher number of ventricular ectopic beats following β-adrenergic pharmacological stimulation with isoproterenol (0.02 mg/kg). Moreover, after isoproterenol administration, one rat with low resting HRV developed sustained ventricular tachycardia that led to death. The present results might be indicative of the potential utility of HRV measures of resting cardiac autonomic function for the prediction of ventricular arrhythmias, particularly during conditions of strong sympathetic activation, in populations without known cardiac disease.