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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Proceedings ArticleDOI
Feature selection using genetic algorithms for premature ventricular contraction classification
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TL;DR: This study focuses on the performance evaluation of selected features using genetic algorithms (GA) to the classification of PVC arrhythmia and showed that the proposed model achieved higher accuracy rates than those of other works on this topic.
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Low-Cost Security of IoT Sensor Nodes With Rakeness-Based Compressed Sensing: Statistical and Known-Plaintext Attacks
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A robust QRS detection and accurate R-peak identification algorithm for wearable ECG sensors
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Machine Learning for Real-Time Heart Disease Prediction
TL;DR: In this article, the authors proposed a novel methodology to extract ECG-related features and predict the type of ECG recorded in real time (less than 30 milliseconds) by leveraging a collection of almost 40,000 ECGs labeled by expert cardiologists across different hospitals and countries.
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