Atrial fibrillation

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Atrial fibrillation (AF or A-fib) is the most common cardiac arrhythmia (abnormal heart rhythm), and involves the two upper chambers of the heart. (Wikipedia.org)






Conferences related to Atrial fibrillation

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2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore


2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.


2019 IEEE International Ultrasonics Symposium (IUS)

The conference covers all aspects of the technology associated with ultrasound generation and detection and their applications.


ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world.


2018 24th International Conference on Pattern Recognition (ICPR)

ICPR will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields

  • 2016 23rd International Conference on Pattern Recognition (ICPR)

    ICPR'2016 will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields.

  • 2014 22nd International Conference on Pattern Recognition (ICPR)

    ICPR 2014 will be an international forum for discussions on recent advances in the fields of Pattern Recognition; Machine Learning and Computer Vision; and on applications of these technologies in various fields.

  • 2012 21st International Conference on Pattern Recognition (ICPR)

    ICPR is the largest international conference which covers pattern recognition, computer vision, signal processing, and machine learning and their applications. This has been organized every two years by main sponsorship of IAPR, and has recently been with the technical sponsorship of IEEE-CS. The related research fields are also covered by many societies of IEEE including IEEE-CS, therefore the technical sponsorship of IEEE-CS will provide huge benefit to a lot of members of IEEE. Archiving into IEEE Xplore will also provide significant benefit to the all members of IEEE.

  • 2010 20th International Conference on Pattern Recognition (ICPR)

    ICPR 2010 will be an international forum for discussions on recent advances in the fields of Computer Vision; Pattern Recognition and Machine Learning; Signal, Speech, Image and Video Processing; Biometrics and Human Computer Interaction; Multimedia and Document Analysis, Processing and Retrieval; Medical Imaging and Visualization.

  • 2008 19th International Conferences on Pattern Recognition (ICPR)

    The ICPR 2008 will be an international forum for discussions on recent advances in the fields of Computer vision, Pattern recognition (theory, methods and algorithms), Image, speech and signal analysis, Multimedia and video analysis, Biometrics, Document analysis, and Bioinformatics and biomedical applications.

  • 2002 16th International Conference on Pattern Recognition


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Periodicals related to Atrial fibrillation

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Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


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. ...


Education, IEEE Transactions on

Educational methods, technology, and programs; history of technology; impact of evolving research on education.


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Most published Xplore authors for Atrial fibrillation

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Xplore Articles related to Atrial fibrillation

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Prediction of successful cardioversion in atrial fibrilation using wavelet analysis parameters and sample entropy

2006 Computers in Cardiology, 2006

In a substantial number of patients atrial fibrillation (AF) recurs after successful electrical cardioversion, but at present there are no reliable clinical markers for confidently identifying the patients in which recurrence will occur within a short period of time. This study evaluates the predictive classification performance of some Discrete Wavelet Transform (DWT) indices in distinguishing recurrent and non-recurrent AF episodes. ...


Detection And Distinction Of Atrial Arrhythmias From Bipolar Electrograms

[1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1990

None


Prediction of the supraventricular arrhythmias in hypertensive patients with different forms of the left ventricular geometry

2006 Computers in Cardiology, 2006

Left ventricular hypertrophy (LVH) in course of hypertension has been linked to the development of atrial fibrillation (AF) and other supraventricular arrhythmias (SVA). The aim was to discover the predictors of electrical instability which could identify hypertensives with the risk of SVA and to investigate the role of different types of left ventricle (LV) geometry on the risk of arrhythmias. ...


Wireless Telecardiological Monitoring System for the Homecare Area

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2006

In following the directive "ambulant medical treatment is preferable to in hospital treatment" the home care area is becoming more and more important in western societies. In this paper a system for monitoring cardiological risk patients is presented. Special attention was paid to an automatic detection of life-threatening events and the initiation of immediate help


Ventricular response during atrial fibrillation: Evaluation of exercise and flecainide effects

2006 Computers in Cardiology, 2006

Ventricular response (VR) during atrial fibrillation (AF) is a complex process, which is modulated by the autonomic nervous system (ANS). The purpose of this study was to characterize VR to both exercise as ANS stimulus and flecainide which is also supposed to have effects on the ANS. RR series were analyzed by means of time domain parameters and non-linear methods. ...


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Educational Resources on Atrial fibrillation

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IEEE.tv Videos

No IEEE.tv Videos are currently tagged "Atrial fibrillation"

IEEE-USA E-Books

  • Prediction of successful cardioversion in atrial fibrilation using wavelet analysis parameters and sample entropy

    In a substantial number of patients atrial fibrillation (AF) recurs after successful electrical cardioversion, but at present there are no reliable clinical markers for confidently identifying the patients in which recurrence will occur within a short period of time. This study evaluates the predictive classification performance of some Discrete Wavelet Transform (DWT) indices in distinguishing recurrent and non-recurrent AF episodes. A validated database of 33 ECG recordings acquired from AF subjects undergoing cardioversion was used throughout the study, together with their known recurrence status at one month. The DWT was applied to these ECG recordings. Several parameters were extracted from the decomposition bands as potential features for predicting the recurrence of AF episodes. The estimated classification rate of the extracted features was evaluated using linear discriminant analysis (LDA). For a separate 11 registers training set and 22 registers testing set, the performance of the classifier testing set gave an estimated accuracy of 82%. We conclude that features extracted from sub-band decomposition of the ECG can provide some indicator of the likelihood of AF recurrence.

  • Detection And Distinction Of Atrial Arrhythmias From Bipolar Electrograms

    None

  • Prediction of the supraventricular arrhythmias in hypertensive patients with different forms of the left ventricular geometry

    Left ventricular hypertrophy (LVH) in course of hypertension has been linked to the development of atrial fibrillation (AF) and other supraventricular arrhythmias (SVA). The aim was to discover the predictors of electrical instability which could identify hypertensives with the risk of SVA and to investigate the role of different types of left ventricle (LV) geometry on the risk of arrhythmias. The second aim was to evaluate two types of filtering of signal-averaging ECG in the prediction of cardiac arrhythmias in hypertensive patients. Statistical analysis including generalized additive models (GAM) were performed. Maximum heart rate (HRmax), duration of P-wave in Butterworth filtered atrial SAECG (hfP_S), envelope of the last 10 ms P-wave vector magnitude (obw10_S) and left atrial internal area (LAar) were identified as the model of SVA predictors.

  • Wireless Telecardiological Monitoring System for the Homecare Area

    In following the directive "ambulant medical treatment is preferable to in hospital treatment" the home care area is becoming more and more important in western societies. In this paper a system for monitoring cardiological risk patients is presented. Special attention was paid to an automatic detection of life-threatening events and the initiation of immediate help

  • Ventricular response during atrial fibrillation: Evaluation of exercise and flecainide effects

    Ventricular response (VR) during atrial fibrillation (AF) is a complex process, which is modulated by the autonomic nervous system (ANS). The purpose of this study was to characterize VR to both exercise as ANS stimulus and flecainide which is also supposed to have effects on the ANS. RR series were analyzed by means of time domain parameters and non-linear methods. In 15 patients (10 male, aged 55 plusmn 13 years) with persistent AF (mean AF duration 13 plusmn24 months), time domain parameters and indexes assessing the predictability of the time series, (regularity index, approximate entropy and an index for linear predictability) were evaluated. VR during exercise resulted modulated by adrenergic stimulation. Flecainide exhibited vagolytic activity especially during exercise. Thus, monitoring of exercise- induced and antiarrhythmic drug effects on ANS is possible with parameters derived from time domain and non-linear analysis.

  • The atrial fibrillation episodes alarm algorithm for a remote monitoring system

    The algorithm for detection of atrial fibrillations in real-time mode is described. The algorithm was proposed for monitoring of the health status of the patients with atrial fibrillation. The complex of parameters characterizing the fact of rising of atrial were estimated and considered. The MIT BIH AF Database and clinical database of the Almazov National Medical Research Centre were used for testing of the proposed algorithm. The sensitivity, specificity and accuracy of the algorithm are 93%, 95% and 94%, respectively.

  • Laplacian interpolation on triangulated tissue surface models

    3D triangulated meshes are used ubiquitously in biomedicine to model tissue surfaces, owing to their geometric flexibility and non-parametric nature. One often needs to interpolate scalar functions over such meshes, either because the interpolant itself provides the solution to a problem, a visualization of the interpolant on top of the mesh is desired, or, quite commonly, both. A common example is the production of electroanatomical maps of the left atrium in the study, diagnosis, and treatment of atrial fibrillation. One appealing method that has been used is Laplacian interpolation because of its unique property of maximizing smoothness. However, the only documented method to date to adopt Laplacian interpolation to 3D triangulated meshes uses matrix formulations that may impose undesirable constraints. In this paper, we present a simple, novel numerical method for Laplacian interpolation on 3D triangulated surfaces. It converges quickly, requires relatively few computations compared to a matrix solution, and can be easily implemented. The method was successfully used in clinical studies of atrial fibrillation, and can be adopted to many problem domains involving 3D meshes.

  • Atrial fibrillation classification using QRS complex features and LSTM

    Classification of Atrial Fibrillation from diverse electrocardiographic (ECG) signals is the challenging objective of the 2017 Physionet Challenge. We suggest a Long Short Term Memory (LSTM) network, which learns patterns directly from pre-computed QRS complex features that classifies ECG signals. Although our architecture is considered deep, it only consists of 1791 parameters. The result is an accurate, lightweight solution that classifies ECG records as Normal, Atrial fibrillation, Other or Too noisy with final challenge score of 0.78.

  • A method for determining local activation direction in the atrium

    Presents a method for determining local 3-dimensional activation directions in human atria. The authors describe the measuring catheter used, the filtering of the raw signals, the detection of electrograms, and finally the calculation of the resultant vector which estimates local activation direction. This technique is then used to demonstrate that transient "linking" of endocardial activation during atrial fibrillation exhibits sudden, not gradual, transitional behavior.<<ETX>>

  • Classification of paroxysmal atrial fibrillation patients with SD1, SD2 and CCM features

    Advances in medical technology allowed the possibility of avoiding arrhythmias with early diagnosis. Then, it is important to classify ill and healthy people correctly. In this work, the effect of Poincare plot measures (SD1 and SD2) and complex correlation measure (CCM) on the classification of subjects with and without paroxysmal atrial fibrillation (PAF) is examined. SD1, SD2 and CCM features, which were obtained from five minute RR series, were classified with k nearest neighbor classifier. The results show that using these three parameters together gives more successful results.



Standards related to Atrial fibrillation

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