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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Biometric Authentication Using Noisy Electrocardiograms Acquired by Mobile Sensors
TL;DR: A cascading bandpass filter for noise cancellation and a radial basis function kernel-based support vector machine shows the best performance among nine classifiers through experimental comparisons for classification-based authentication are designed.
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Electrocardiogram signal denoising using non-local wavelet transform domain filtering
TL;DR: This study proposes a non- local wavelet transform domain ECG signal denoising method which exploits the correlations among both local and non-local samples of the signal.
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Artificial intelligence for the electrocardiogram.
Ana Mincholé,Blanca Rodriguez +1 more
TL;DR: Deep-learning algorithms can be applied to large datasets of electrocardiograms, are capable of identifying abnormal heart rhythms and mechanical dysfunction, and could aid healthcare decisions.
Proceedings ArticleDOI
A real-time compressed sensing-based personal electrocardiogram monitoring system
TL;DR: A novel real-time energy-aware ECG monitoring system based on the emerging compressed sensing (CS) signal acquisition/compression paradigm for WBSN applications is proposed and CS is demonstrated as an advantageous real- time and energy-efficient ECG compression technique.
Journal ArticleDOI
Heartbeat Classification Using Abstract Features From the Abductive Interpretation of the ECG
TL;DR: It is proved that automatic beat classification on ECG signals can be effectively solved with a pure knowledge-based approach, using an appropriate set of abstract features obtained from the interpretation of the physiological processes underlying the signal.
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