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
ECG heartbeat classification based on an improved ResNet-18 model
TL;DR: Based on a convolutional neural network (CNN) approach, this paper proposed an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment.
Journal ArticleDOI
Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals.
TL;DR: A novel deep learning algorithm is proposed to improve accuracy and reduce training time by combining the convolutional neural network (CNN) with the bidirectional long short-term memory (BiLSTM) and this approach has not been investigated to date.
Journal ArticleDOI
Automatic arrhythmia recognition from electrocardiogram signals using different feature methods with long short-term memory network model
TL;DR: Various new feature extraction methods employing long short-term memory network (LSTM) model have been presented in this paper, which help in the detection of heart rhythms from electrocardiogram signals.
Journal ArticleDOI
An optimally designed digital differentiator based preprocessor for R-peak detection in electrocardiogram signal
TL;DR: The proposed IODD based QRS detection approach is validated on the first channel records of MIT/BIH Arrhythmia database (MBAD), QT database (QTDB), MIT/biH noise stress test database (NSTDB), atrial fibrillation termination challenge database (AFTDB, and MIT/ BIH ST change database (STDB) and ensures the accuracy of the proposed R-peak detection technique for a wide variety of QRS morphologies.
Journal ArticleDOI
Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications
Mohamed Hammad,Ahmed A. Abd El-Latif,Amir Hussain,Fathi E. Abd El-Samie,Brij B. Gupta,Hassan Ugail,Ahmed Sedik +6 more
TL;DR: In this article , a convolutional neural network (CNN) and ConvLSTM deep learning models (DLMs) are presented for automatic detection of arrhythmia for IoT applications, where the input ECG signals are represented in 2D format and then the obtained images are fed into the proposed DLMs for classification.
References
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Journal ArticleDOI
New method for heart studies.
TL;DR: Electrocardiography is proposed to be implemented by the use of long-period, continuous recording of heart potentials with a portable, self-contained instrument together with semiautomatic methods for the rapid analysis of the resulting voluminous data.
Proceedings ArticleDOI
The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it
George B. Moody,Roger G. Mark +1 more
TL;DR: A compact-disk ROM containing the Massachusetts Institute of Technology (MIT)-Boston's Beth Israel Hospital (BIH) Arrhythmia Database as well as a large number of supplementary recordings assembled for various research projects was produced.
Journal ArticleDOI
PhysioNet: a Web-based resource for the study of physiologic signals
TL;DR: What PhysioNet offers to researchers is discussed, some of the technology needed to support these functions are described, and observations gleaned from the organisation's first year of service are concluded.
Proceedings ArticleDOI
The European ST-T database: development, distribution and use
Antonio Taddei,Andrea Biagini,G. Distante,Michele Emdin,Mazzei Mg,P. Pisani,N. Roggero,Maurizio Varanini,Roger G. Mark,George B. Moody,L. Braaksma,C. Zeelenberg,Carlo Marchesi +12 more
TL;DR: The European project for the development of an ST-T annotated database originated from the 'Concerted Action' on ambulatory monitoring, set up by the European Community in 1985 and includes more than 200 ST segment and almost 300 T-wave changes.
Proceedings ArticleDOI
A long-term ST database for development and evaluation of ischemia detectors
Franc Jager,George B. Moody,Antonio Taddei,G. Antolic,Michele Emdin,A. Smrdel,B. Glavic,Carlo Marchesi,Roger G. Mark +8 more
TL;DR: The authors present the selection criteria for records, an annotation protocol with definitions of transient ST events, interactive graphic tools for manual and automatic annotating, and the annotation procedure.