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
ECG beat classification using wavelets and SVM
Shameer Faziludeen,P. V. Sabiq +1 more
TL;DR: In this article, a wavelet decomposition using daubechies 4 wavelet is done to extract 25 features for each beat from wavelet analysis, namely - mean, variance, standard deviation, minimum and maximum of detail coefficients and approximation coefficients.
Journal ArticleDOI
Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms.
Kuan-Cheng Chang,Po-Hsin Hsieh,Mei-Yao Wu,Yu-Chen Wang,Yu-Chen Wang,Jan-Yow Chen,Fuu Jen Tsai,Edward S. C. Shih,Ming-Jing Hwang,Tzung-Chi Huang,Tzung-Chi Huang,Tzung-Chi Huang +11 more
TL;DR: The feasibility and effectiveness of the deep-learning LSTM model for interpreting 12 common heart rhythms according to 12-lead ECG signals are demonstrated and may have clinical relevance for the early diagnosis of cardiac rhythm disorders.
Journal ArticleDOI
Temporal convolutional autoencoder for unsupervised anomaly detection in time series
TL;DR: TCN-AE, a temporal convolutional network autoencoder based on dilated convolutions significantly outperforms several other unsupervised state-of-the-art anomaly detection algorithms and investigates the contribution of the individual enhancements and shows that each new ingredient improves the overall performance on the investigated benchmark.
Journal ArticleDOI
A comparison of three QRS detection algorithms over a public database
TL;DR: This work brings the community the source code of each algorithm and results of its validation over a public database, developed as a framework in order to permit the inclusion of new QRS detection algorithms and also its testing over different databases.
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Adaptive Motion Artifact Reduction Based on Empirical Wavelet Transform and Wavelet Thresholding for the Non-Contact ECG Monitoring Systems
TL;DR: An ECG motion artifact removal approach based on empirical wavelet transform (EWT) and wavelet thresholding (WT) is proposed and is feasible for reducing motion artifacts from ECG signals, whether from simulation ECGs signals or practical non-contact ECG monitoring systems.
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