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
Dense Convolutional Networks With Focal Loss and Image Generation for Electrocardiogram Classification
TL;DR: To handle the data imbalance problem characterizing the ECG data, the focal loss (FL) that is based on the idea of reshaping the standard cross-entropy loss such that it reduces the loss assigned to well-classified ECG beats is proposed.
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Decision Boundary-Based Anomaly Detection Model Using Improved AnoGAN From ECG Data
TL;DR: This study proposes the decision boundary-based Anomaly detection model using improved AnoGAN from ECG data that has better goodness-of-fit than AnOGAN and is evaluated on the basis of AUC and F-measure.
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Effective ECG beat classification using higher order statistic features and genetic feature selection
TL;DR: The results demonstrated that the proposed approach had the ability to distinguish ECG arrhythmias with acceptable classification accuracy and can be used to support the cardiologist in the detection of cardiac disorders.
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Analysis of Pan-Tompkins Algorithm Performance with Noisy ECG Signals
TL;DR: In this paper, the performance of the Pan-Tompkins algorithm was analyzed in extracting the QRS complex from standard ECG data that includes noise-stressed ECG signals.
Journal ArticleDOI
Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning
TL;DR: A supervised-learning approach is used to detect heartbeats and classify arrhythmias using a window-based feature definition that is suitable for execution within an asymmetric multicore embedded processor that provides a dedicated core for hardware assisted pattern matching.
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