PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TLDR
The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).Abstract:
—The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of He...read more
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Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
TL;DR: An algorithm is developed which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor and builds a dataset with more than 500 times the number of unique patients than previously studied corpora.
Journal ArticleDOI
ECG signal denoising and baseline wander correction based on the empirical mode decomposition
TL;DR: A new ECG enhancement method based on the recently developed empirical mode decomposition (EMD) that is able to remove both high-frequency noise and BW with minimum signal distortion and is validated through experiments on the MIT-BIH databases.
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The eICU Collaborative Research Database, a freely available multi-center database for critical care research
Tom J. Pollard,Alistair E. W. Johnson,Jesse D. Raffa,Leo Anthony Celi,Leo Anthony Celi,Roger G. Mark,Roger G. Mark,Omar Badawi,Omar Badawi,Omar Badawi +9 more
TL;DR: The eICU Collaborative Research Database as mentioned in this paper is a multi-center intensive care unit (ICU) database with high granularity data for over 200,000 admissions to ICUs monitored by e-ICU Programs across the United States.
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Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network
TL;DR: A convolutional neural network (CNN) technique to automatically detect the different ECG segments and can serve as an adjunct tool to assist clinicians in confirming their diagnosis is presented.
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Arrhythmia detection using deep convolutional neural network with long duration ECG signals.
TL;DR: A new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis based on a new 1D-Convolutional Neural Network model (1D-CNN).
References
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Journal ArticleDOI
Multifractality in human heartbeat dynamics
Plamen Ch. Ivanov,Plamen Ch. Ivanov,Luís A. Nunes Amaral,Luís A. Nunes Amaral,Ary L. Goldberger,Shlomo Havlin,Michael Rosenblum,Zbigniew R. Struzik,H. Eugene Stanley +8 more
TL;DR: In this paper, the authors investigate the possibility that time series generated by certain physiological control systems may be members of a special class of complex processes, termed multifractal, which require a large number of exponents to characterize their scaling properties.
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From Clocks to Chaos: The Rhythms of Life
TL;DR: One of the most interesting features of the book is that it makes a start at explaining "dynamical diseases" that are not the result of infection by pathogens but that stem from abnormalities in the timing of essential functions.
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Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside.
TL;DR: An introduction to some key aspects of non-linear dynamics and selected applications to physiology and medicine is provided.
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Predicting Survival in Heart Failure Case and Control Subjects by Use of Fully Automated Methods for Deriving Nonlinear and Conventional Indices of Heart Rate Dynamics
Kalon K.L. Ho,George B. Moody,Chung-Kang Peng,Joseph E. Mietus,Martin G. Larson,Daniel Levy,Ary L. Goldberger +6 more
TL;DR: It is demonstrated that HRV analysis of ambulatory ECG recordings based on fully automated methods can have prognostic value in a population-based study and that nonlinear HRV indices may contribute prognosticvalue to complement traditional HRV measures.
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Spatiotemporal evolution of ventricular fibrillation
Francis X. Witkowski,L. Joshua Leon,Patricia A. Penkoske,Wayne R. Giles,Mark L. Spano,William L. Ditto,Arthur T. Winfree +6 more
TL;DR: High spatial and temporal resolution mapping of optical transmembrane potentials can easily detect transiently erupting rotors during the early phase of ventricular fibrillation, characterized by a relatively high spatiotemporal cross-correlation.