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
Arrhythmia Detection - A Machine Learning based Comparative Analysis with MIT-BIH ECG Data
TL;DR: This work compared five machine learning algorithms with three different features and it was observed that Artificial Neural Network with amplitude features gave best result with 99.59% accuracy which is comparable to state-of-the art methods.
Journal ArticleDOI
Robust multi-feature collective non-negative matrix factorization for ECG biometrics
TL;DR: Wang et al. as mentioned in this paper proposed a robust multi-feature collective non-negative matrix factorization (RMCNMF) model to handle noise and sample variation in ECG Biometrics.
Journal ArticleDOI
ULECGNet: An Ultra-Lightweight End-to-End ECG Classification Neural Network
TL;DR: An ultra-lightweight end-to-end ECG classification neural network that has extremely low computational complexity (∼8.2k parameters and ∼227k multiplication/addition operations) and can be squeezed into a low-cost microcontroller (MCU) such as MSP432 while achieving 99.1% overall classification accuracy was proposed in this paper .
Arrhythmia detection and classification using ECG and PPG techniques: a review
TL;DR: In this paper, a review of wearable sensors used in the literature and public databases available for the evaluation of results is presented, highlighting the limitations of the current techniques and pragmatic solutions to improvise the ongoing effort.
Journal ArticleDOI
LPClass: Lightweight Personalized Sensor Data Classification in Computational Social Systems
TL;DR: Wang et al. as mentioned in this paper proposed a lightweight personalized sensor data classification model, called LPClass, which embeds the shallow recurrent neural network as a kernel, which makes it lightweight enough to be deployed on edge devices.
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