Journal ArticleDOI
Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals
TLDR
This paper investigates the representation ability of linear and nonlinear features and proposes a combination of such features in order to improve the classification of ECG data and is able to classify the N, S, V, F and U arrhythmia classes with high accuracy.About:
This article is published in Computer Methods and Programs in Biomedicine.The article was published on 2016-04-01. It has received 335 citations till now.read more
Citations
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Journal ArticleDOI
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).
Journal ArticleDOI
A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification
TL;DR: It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks and is an important approach that can be applied to similar signal processing problems.
Journal ArticleDOI
Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats
TL;DR: An automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branches block (RBBB) and atrial premature beats (APB), and premature ventricular contraction (PVC) on ECG signals.
Journal ArticleDOI
A survey on ECG analysis
Selcan Kaplan Berkaya,Alper Kursat Uysal,Efnan Sora Gunal,Semih Ergin,Serkan Gunal,M. Bilginer Gülmezoğlu +5 more
TL;DR: The literature on ECG analysis, mostly from the last decade, is comprehensively reviewed based on all of the major aspects mentioned above.
Journal ArticleDOI
ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network
TL;DR: It is validated that the proposed CNN classifier using ECG spectrograms as input can achieve improved classification accuracy without additional manual pre-processing of the ECG signals.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Journal ArticleDOI
A Real-Time QRS Detection Algorithm
Jiapu Pan,Willis J. Tompkins +1 more
TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Journal ArticleDOI
Heart Disease and Stroke Statistics—2006 Update A Report From the American Heart Association Statistics Committee and Stroke Statistics Subcommittee
Thomas Thom,Nancy Haase,Wayne D. Rosamond,Virginia J. Howard,John S. Rumsfeld,Teri A. Manolio,Zhi-Jie Zheng,Katherine M. Flegal,Christopher J. O'Donnell,Steven J. Kittner,Donald M. Lloyd-Jones,David C. Goff,Yuling Hong,Robert J. Adams,Gary Friday,Karen L. Furie,Philip B. Gorelick,Brett M. Kissela,John R. Marler,James B. Meigs,Véronique L. Roger,Stephen Sidney,Paul D. Sorlie,Julia Steinberger,Sylvia Wasserthiel-Smoller,Matthew Wilson,Philip A. Wolf +26 more
TL;DR: The American Heart Association works with the Centers for Disease Control and Prevention’s National Center for Health Statistics (CDC/NCHS), the National Heart, Lung, and Blood Institute (NHLBI, the National Institute of Neurological Disorders and Stroke (NINDS), and other government agencies to derive the annual statistics in this update.
Journal ArticleDOI
The impact of the MIT-BIH Arrhythmia Database
George B. Moody,Roger G. Mark +1 more
TL;DR: The history of the database, its contents, what is learned about database design and construction, and some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database are reviewed.