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
A 1.02- μ W STT-MRAM-Based DNN ECG Arrhythmia Monitoring SoC With Leakage-Based Delay MAC Unit
Kyoung-Rog Lee,Ji-Hoon Kim,Changhyeon Kim,Donghyeon Han,Juhyoung Lee,Jinsu Lee,Hongsik Jeong,Hoi-Jun Yoo +7 more
TL;DR: A low-power STT-MRAM-based mixed-mode electrocardiogram (ECG) arrhythmia monitoring SoC that enables deep neural network (DNN) inference to achieve higher area efficiency, lower power consumption without external memory access is proposed.
Journal ArticleDOI
On the reliability of a novel MODWT-based hybrid ARIMA-artificial intelligence approach to forecast daily Snow Depth (Case study: The western part of the Rocky Mountains in the U.S.A)
TL;DR: The hybrid ARIMA-artificial intelligence models were found to provide more accurate results than the standalone ARimA and AI models and confirmed the priority of wavelet-based models over standalone ones.
Proceedings ArticleDOI
Arrhythmia disease classification using Artificial Neural Network model
TL;DR: From careful and exhaustive experimentation, the conclusion is reached that proposed classifier gives best classification results in terms of classification accuracy of 100 % for classes 1 and 2, 98.72%, 97.4%, 94.25%, 92.1% and 10 respectively.
Journal ArticleDOI
A Novel Incremental and Interactive Method for Actual Heartbeat Classification With Limited Additional Labeled Samples
TL;DR: Wang et al. as discussed by the authors proposed an active dual-scale residual convolutional long short-term memory neural network to classify heartbeats interactively and automatically, which used only 5.14% of the additional data set and improved the model accuracy and macro F1 score.
Journal ArticleDOI
Supervised learning as an inverse problem based on non-smooth loss function
TL;DR: A new technique of approximation based on the reformulation of the associated inverse problem into a minimizing one of a slanting cost functional is proposed, which leads to an efficient numerical algorithm allowing us to solve supervised learning problem in the most general framework.
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