Journal ArticleDOI
The impact of the MIT-BIH Arrhythmia Database
George B. Moody,Roger G. Mark +1 more
TLDR
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.Abstract:
The MIT-BIH Arrhythmia Database was the first generally available set of standard test material for evaluation of arrhythmia detectors, and it has been used for that purpose as well as for basic research into cardiac dynamics at about 500 sites worldwide since 1980. It has lived a far longer life than any of its creators ever expected. Together with the American Heart Association Database, it played an interesting role in stimulating manufacturers of arrhythmia analyzers to compete on the basis of objectively measurable performance, and much of the current appreciation of the value of common databases, both for basic research and for medical device development and evaluation, can be attributed to this experience. In this article, we briefly review the history of the database, describe its contents, discuss what we have learned about database design and construction, and take a look at some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database.read more
Citations
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Journal ArticleDOI
2017 ISHNE-HRS expert consensus statement on ambulatory ECG and external cardiac monitoring/telemetry.
Jonathan S. Steinberg,Niraj Varma,Iwona Cygankiewicz,Peter F. Aziz,Paweł Balsam,Adrian Baranchuk,Daniel J. Cantillon,Polychronis Dilaveris,Sergio J. Dubner,Nabil El-Sherif,Jaroslaw Krol,Małgorzata Kurpesa,Maria Teresa La Rovere,S. Suave Lobodzinski,Emanuela T. Locati,Suneet Mittal,Brian Olshansky,Ewa Piotrowicz,Leslie A. Saxon,Peter Stone,Larisa G. Tereshchenko,Larisa G. Tereshchenko,Gioia Turitto,Neil J. Wimmer,Richard L. Verrier,Wojciech Zareba,Ryszard Piotrowicz +26 more
TL;DR: The details in this document provide background and framework from which to apply AECG techniques in clinical practice, as well as clinical research.
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Clustering of electrocardiograph signals in computer-aided Holter analysis
TL;DR: A complete process is proposed to obtain the significant beats present within a signal, with a reasonable computational cost, so cardiologists will only have to examine a small but fully representative subset of beats, making this method a very useful tool for medical decision support systems.
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A novel multi-module neural network system for imbalanced heartbeats classification
TL;DR: Comparisons with several state-of-the-art methods using standard criteria on three datasets demonstrate the superiority of MMNNS for improving detection of heartbeats and addressing imbalance in ECG heartbe beats classification.
Journal ArticleDOI
A Real-Time Arrhythmia Heartbeats Classification Algorithm Using Parallel Delta Modulations and Rotated Linear-Kernel Support Vector Machines
TL;DR: A patient dependent rotated linear-kernel support vector machine classifier that combines the global and local classifiers, with three types of feature vectors extracted directly from the Delta modulated bit-streams is proposed.
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Non-Adaptive Methods for Fetal ECG Signal Processing: A Review and Appraisal.
TL;DR: It is limiting that a different non-adaptive method works well for each type of signal, but independent component analysis, principal component analysis and wavelet transforms are the most commonly published methods of signal processing and have good accuracy and speed of algorithms.
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