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
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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
Citations
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A dynamical model for generating synthetic electrocardiogram signals
TL;DR: A dynamical model based on three coupled ordinary differential equations is introduced which is capable of generating realistic synthetic electrocardiogram (ECG) signals and may be employed to assess biomedical signal processing techniques which are used to compute clinical statistics from the ECG.
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A review of unsupervised feature learning and deep learning for time-series modeling ☆
TL;DR: This paper overviews the particular challenges present in time-series data and provides a review of the works that have either applied time- series data to unsupervised feature learning algorithms or alternatively have contributed to modifications of featurelearning algorithms to take into account the challenges present.
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A deep convolutional neural network model to classify heartbeats
U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Muhammad Adam,Arkadiusz Gertych,Ru San Tan +6 more
TL;DR: A 9-layer deep convolutional neural network (CNN) is developed to automatically identify 5 different categories of heartbeats in ECG signals to serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmicheartbeats.
Journal ArticleDOI
Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package
TL;DR: The dtw package allows R users to compute time series alignments mixing freely a variety of continuity constraints, restriction windows, endpoints, local distance definitions, and so on.
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DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG
TL;DR: This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG, and utilizes convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs.
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.