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
Diagnosis of cardiac arrhythmia using kernel difference weighted KNN classifier
TL;DR: Experimental results on the UCI cardiac arrhythmia database indicate that, KDF-WKNN is superior to the nearest neighbor classifier, and is very competitive while compared with several state-of-the-art methods in terms of classification accuracy.
Journal ArticleDOI
An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm.
TL;DR: An adaptive and time-efficient R-peak detection algorithm for ECG processing was proposed using wavelet multiresolution analysis to enhance the ECG signal representation and local maximums were calculated by the first-order forward differential approach to locate the R-peaks.
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ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform
TL;DR: Simulation results indicate that the modified unified threshold and wavelet hyperbolic threshold de-noising method is robust for all kinds of noises with varying degrees of input noise, providing a high quality clean signal.
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Machine Learning Approach to Detect Cardiac Arrhythmias in ECG Signals: A Survey
TL;DR: This work presents a systematic review of the current state-of-the-art methods used to detect cardiac arrhythmia using on ECG signals, which includes the signal decomposition, feature extraction and machine learning approaches used for automatic detection and decision making process.
Journal ArticleDOI
Automatic pattern recognition of ECG signals using entropy-based adaptive dimensionality reduction and clustering
Hong He,Yonghong Tan +1 more
TL;DR: A new entropy-based principal component analysis approach (EPCA) is developed in this paper which can automatically select the best number of principal components of the PCA and a novel fuzzy-entropy based c-means clustering algorithm (FECM) which can adaptively identify the optimal number of clusters based on the ECM.
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