Heart rate

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Heart rate is the number of heartbeats per unit of time, typically expressed as beats per minute. Heart rate can vary as the body's need to absorb oxygen and excrete carbon dioxide changes, such as during exercise or sleep. (Wikipedia.org)






Conferences related to Heart rate

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2014 IEEE International Conference on Systems, Man and Cybernetics - SMC

SMC2014 targets advances in Systems Science and Engineering, Human-Machine Systems, and Cybernetics involving state-of-art technologies interacting with humans to provide an enriching experience and thereby improving the quality of lives including theories, methodologies, and emerging applications.


2012 International Conference on Computer and Communication Engineering (ICCCE)

The main objective of this conference is to provide a forum for engineers, academicians, scientists and researchers to present the result of their research activities in the field of Computer and Communication Engineering. The primary focus of the conference is to create an effective medium for institutions and industries to share ideas, innovations and problem solving techniques.

  • 2010 International Conference on Computer and Communication Engineering (ICCCE)

    The main objective of this conference is to provide a forum for engineers, academicians, scientists and researchers to present the result of their research activities in the field of Computer and Communication Engineering. The primary focus of the conference is to create an effective medium for institutions and industries to share ideas, innovations and problem solving techniques.


2011 8th Intl. Symp. on Noninvasive Functional Source Imaging of the Brain & Heart and the 8th Intl. Conference on Bioelectromagnetism (NFSI & ICBEM)

All aspects of bioelectromagnetic field measurement and noninvasive functional imaging of the human body. Research tools include EEG/MEG source analyzers, fMRI, PET, SPECT, EIT and NIRS applied, for example, to cognition and to the neurological and psychiatric disorders of the brain and to the electromechanical coupling in the heart.

  • 2007 Joint Mtg. of the 6th Intl. Symp. on Noninvasive Functional Source Imaging & the Intl. Conf. on Functional Biomedical Imaging (NFSI & ICFBI)


2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

The general theme of EMBC'08 is "Personalized Healthcare through Technology", covering a broad spectrum of topics from biomedical and clinical engineering and physics to medical and clinical applications. Transfer of research results from academia to industry will also be a focus of the conference.



Periodicals related to Heart rate

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Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Computing in Science & Engineering

Physics, medicine, astronomy—these and other hard sciences share a common need for efficient algorithms, system software, and computer architecture to address large computational problems. And yet, useful advances in computational techniques that could benefit many researchers are rarely shared. To meet that need, Computing in Science & Engineering (CiSE) presents scientific and computational contributions in a clear and accessible format. ...


Design & Test of Computers, IEEE

IEEE Design & Test of Computers offers original works describing the methods used to design and test electronic product hardware and supportive software. The magazine focuses on current and near-future practice, and includes tutorials, how-to articles, and real-world case studies. Topics include IC/module design, low-power design, electronic design automation, design/test verification, practical technology, and standards. IEEE Design & Test of ...




Xplore Articles related to Heart rate

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Device logic simulations for predicting rate behavior in a cardiosynchronous myostimulator

Bin Lu; Fred Vance 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1992

Two methods of predicting rate behavior were used in a telemetric implant device programmer to represent the rate behavior of an implantable cardiosynchronous myostimulator.


Different Approaches for Linear and Non-linear ECG Generation

Saeedeh Lotfi Mohammad Abad; Nader Jafarnia Dabanloo; Mohammadreza Mohagheghi 2008 International Conference on BioMedical Engineering and Informatics, 2008

Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of the challenges is to generate ECG signals with a wide range of waveforms, power spectra and variations in heart rate variability (HRV)-all of which are important indexes of human heart functions. In this paper we present a comprehensive ...


Fetal heart rate classification by non-parametric Bayesian methods

Kezi Yu; J. Gerald Quirk; Petar M. Djurić 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017

In this paper, we propose an application of non-parametric Bayesian (NPB) models to classification of fetal heart rate recordings. More specifically, the models are used to discriminate between fetal heart rate recordings that belong to fetuses that may have adverse asphyxia outcomes and those that are considered normal. In our work we rely on models based on hierarchical Dirichlet processes. ...


Real-time sensing, transmission and analysis for vital signs of persons during exercises

Shinsuke Hara; Takashi Kawabata; Hajime Nakamura 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

Real-time monitoring of vital signs from persons during exercises is useful from the medical, healthcare and sports physiological points of view. In professional team sports, physical trainers or technical coaches want to manage the physical conditions of athletes during exercise training in the grounds, on the other hand, in elementary and junior high schools, teachers want to take care of ...


QT interval measurement: What can we really expect?

J. Q. Xue 2006 Computers in Cardiology, 2006

This study is an effort of measuring QT interval with an automatic computerized algorithm. The aims of the algorithm are consistency as well as accuracy. The general methodology adopted in this algorithm is to seek more consistent QT interval measurement by using multi- lead and multi-beat information from a given segment of ECG. A representative beat is generated from selected ...


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Educational Resources on Heart rate

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eLearning

Device logic simulations for predicting rate behavior in a cardiosynchronous myostimulator

Bin Lu; Fred Vance 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1992

Two methods of predicting rate behavior were used in a telemetric implant device programmer to represent the rate behavior of an implantable cardiosynchronous myostimulator.


Different Approaches for Linear and Non-linear ECG Generation

Saeedeh Lotfi Mohammad Abad; Nader Jafarnia Dabanloo; Mohammadreza Mohagheghi 2008 International Conference on BioMedical Engineering and Informatics, 2008

Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of the challenges is to generate ECG signals with a wide range of waveforms, power spectra and variations in heart rate variability (HRV)-all of which are important indexes of human heart functions. In this paper we present a comprehensive ...


Fetal heart rate classification by non-parametric Bayesian methods

Kezi Yu; J. Gerald Quirk; Petar M. Djurić 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017

In this paper, we propose an application of non-parametric Bayesian (NPB) models to classification of fetal heart rate recordings. More specifically, the models are used to discriminate between fetal heart rate recordings that belong to fetuses that may have adverse asphyxia outcomes and those that are considered normal. In our work we rely on models based on hierarchical Dirichlet processes. ...


Real-time sensing, transmission and analysis for vital signs of persons during exercises

Shinsuke Hara; Takashi Kawabata; Hajime Nakamura 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

Real-time monitoring of vital signs from persons during exercises is useful from the medical, healthcare and sports physiological points of view. In professional team sports, physical trainers or technical coaches want to manage the physical conditions of athletes during exercise training in the grounds, on the other hand, in elementary and junior high schools, teachers want to take care of ...


QT interval measurement: What can we really expect?

J. Q. Xue 2006 Computers in Cardiology, 2006

This study is an effort of measuring QT interval with an automatic computerized algorithm. The aims of the algorithm are consistency as well as accuracy. The general methodology adopted in this algorithm is to seek more consistent QT interval measurement by using multi- lead and multi-beat information from a given segment of ECG. A representative beat is generated from selected ...


More eLearning Resources

IEEE-USA E-Books

  • Evaluation of the Autonomic Nervous System: From Algorithms to Clinical Practice

    This chapter contains sections titled: Introduction Relationship Between Heart Rate Variability and Myocardial Infarction Relationship Between Heart Rate Variability and Heart Failure Relationship Between Heart Rate and Blood Pressure Variability Sudden Death Risk Stratification, Prophylactic Treatment, and Unresolved Issues The Role of Autonomic Markers in Noninvasive Risk Stratification

  • Index

    Featuring current contributions by experts in signal processing and biomedical engineering, this book introduces the concepts, recent advances, and implementations of nonlinear dynamic analysis methods. Together with Volume I in this series, this book provides comprehensive coverage of nonlinear signal and image processing techniques. Nonlinear Biomedical Signal Processing: Volume II combines analytical and biological expertise in the original mathematical simulation and modeling of physiological systems. Detailed discussions of the analysis of steady-state and dynamic systems, discrete-time system theory, and discrete modeling of continuous-time systems are provided. Biomedical examples include the analysis of the respiratory control system, the dynamics of cardiac muscle and the cardiorespiratory function, and neural firing patterns in auditory and vision systems. Examples include relevant MATLAB® and Pascal programs. Topics covered include: Nonlinear dynamics Behavior and estimation Modeling of biomedical signals and systems Heart rate variability measures, models, and signal assessments Origin of chaos in cardiovascular and gastric myoelectrical activity Measurement of spatio-temporal dynamics of human epileptic seizures A valuable reference book for medical researchers, medical faculty, and advanced graduate students, it is also essential reading for practicing biomedical engineers. Nonlinear Biomedical Signal Processing, Volume II is an excellent companion to Dr. Akay's Nonlinear Biomedical Signal Processing, Volume I: Fuzzy Logic, Neural Networks, and New Algorithms.

  • About the Editor

    Featuring current contributions by experts in signal processing and biomedical engineering, this book introduces the concepts, recent advances, and implementations of nonlinear dynamic analysis methods. Together with Volume I in this series, this book provides comprehensive coverage of nonlinear signal and image processing techniques. Nonlinear Biomedical Signal Processing: Volume II combines analytical and biological expertise in the original mathematical simulation and modeling of physiological systems. Detailed discussions of the analysis of steady-state and dynamic systems, discrete-time system theory, and discrete modeling of continuous-time systems are provided. Biomedical examples include the analysis of the respiratory control system, the dynamics of cardiac muscle and the cardiorespiratory function, and neural firing patterns in auditory and vision systems. Examples include relevant MATLAB® and Pascal programs. Topics covered include: Nonlinear dynamics Behavior and estimation Modeling of biomedical signals and systems Heart rate variability measures, models, and signal assessments Origin of chaos in cardiovascular and gastric myoelectrical activity Measurement of spatio-temporal dynamics of human epileptic seizures A valuable reference book for medical researchers, medical faculty, and advanced graduate students, it is also essential reading for practicing biomedical engineers. Nonlinear Biomedical Signal Processing, Volume II is an excellent companion to Dr. Akay's Nonlinear Biomedical Signal Processing, Volume I: Fuzzy Logic, Neural Networks, and New Algorithms.

  • Analysis of Nonstationary Signals

    This chapter contains sections titled: Problem Statement Illustration of the Problem with Case-studies Time-variant Systems Fixed Segmentation Adaptive Segmentation Use of Adaptive Filters for Segmentation Application: Adaptive Segmentation of EEG Signals Application: Adaptive Segmentation of PCG Signals Application: Time-varying Analysis of Heart-rate Variability Remarks Study Questions and Problems Laboratory Exercises and Projects

  • Fractal Analysis of Heart Rate Variability

    This chapter contains sections titled: Introduction The fBm Model The Autocorrelation Function for DFGN The Probability Density Function for DFGN A Maximum Likelihood Estimator for DFGN PSD Estimators for fBm and DFGN A Wavelet Estimator for DFGN The Heart Rate Variability Signal This chapter contains sections titled: References

  • Applications and Future Research

    Radar technology for sensing of physiological motion has reached the point of adoption for basic commercial applications in medicine and security. This chapter gives a brief review on some existing US Food and Drug Administration (FDA)???approved and other commercial devices followed by ongoing research efforts. Ongoing research in the field is directed toward broader, more robust applications of remote sensing of physiological motion. Remote sensing of heart rate, respiratory rate, and gross bodily motion has been demonstrated using radar technology in monitoring systems, which makes no contact with the patient. Doppler radar can be used to detect motion ranging from arbitrary limb movement to periodic chest displacement associated with cardiopulmonary activity. Technology that can capture minute changes in physiological parameters has proven highly effective at assessment of sleep in elaborate and expensive sleep laboratory studies.

  • Heart Rate Variability: Measures and Models

    This chapter contains sections titled: Introduction Methods and Measures Discriminating Heart-Failure Patients from Normal Subjects Markers for other Cardiac Pathologies Does Deterministic Chaos Play a Role in Heart Rate Variability? Mathematical Models for Heart Rate Variability Conclusion This chapter contains sections titled: References Appendix A

  • Doppler Radar Physiological Assessments

    The Doppler radar detects all motion in the radar field of view, through detection of phase variations in the received signal. The challenge in physiological monitoring via Doppler radar is to effectively isolate the subject's random fidgeting physiological motion. The percentage of measurement interval containing significant motion may be used as a measure of subject rest/activity cycle, determining the degree of restlessness, for example, actigraphy. Phase demodulation provides the output proportional to chest displacement, and this information can be further analyzed to extract respiratory and heart rates, analyze the shape of respiratory signals, assess heart rate variability (HRV) parameters, and estimate displacement amplitude and related respiratory volume. The magnitude of received RF power can be analyzed to determine cardiopulmonary radar cross section (RCS) and further determine subject orientation. The variation of RCS with size and curvature of the target surface is the basis for detecting orientation of a human subject.

  • Smart m???Health Sensing

    This chapter presents a new taxonomy of m???Health sensing. It also charts the evolution of medical sensing from the early remote monitoring principles to recent advances in true m???Health monitoring with wearable technologies, body area networks (BAN), wireless implantable bimolecular sensors, and many more devices. The new m???Health sensor taxonomy was composed of the following categories: health and wellness monitoring sensors; diagnostic sensors; prognostic and treatment sensors; and assistive sensors. The chapter describes briefly each of these categories and subcategories for completeness. There are a number of universal physiological and physical parameters for bio???sensing and wireless monitoring. Among these are the electrocardiogram, electroencephalogram (EEG), electromyogram (EMG), blood pressure (systolic and dia???stolic), body or skin temperature, respiratory rate (RR), oxygen saturation, heart rate, perspiration (sweating or skin) conductivity, heart sounds, blood glucose level, and body movements. Smartphones are increasingly acting to sense, collect, process, and distribute medical data.

  • Modeling Biomedical Signalgenerating Processes and Systems

    This chapter contains sections titled: Problem Statement Illustration of the Problem with Case-Studies Point Processes Parametric System Modeling Autoregressive or All-pole Modeling Pole-zero Modeling Electromechanical Models of Signal Generation Application: Analysis of Heart-rate Variability Application: Spectral Modeling and Analysis of PCG Signals Application: Detection of Coronary Artery Disease Remarks Study Questions and Problems Laboratory Exercises and Projects



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