ECG-based heartbeat classification for arrhythmia detection
TLDR
This work surveys the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used.About:
This article is published in Computer Methods and Programs in Biomedicine.The article was published on 2016-04-01 and is currently open access. It has received 635 citations till now. The article focuses on the topics: Heartbeat.read more
Citations
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Journal ArticleDOI
Arrhythmia detection using deep convolutional neural network with long duration ECG signals.
TL;DR: A new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis based on a new 1D-Convolutional Neural Network model (1D-CNN).
Journal ArticleDOI
ECG Classification Using Wavelet Packet Entropy and Random Forests
Taiyong Li,Min Zhou +1 more
TL;DR: This paper proposes a method to classify ECG signals using wavelet packet entropy (WPE) and random forests (RF) following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations and the inter-patient scheme, and shows that WPE and RF is promising for ECG classification.
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A survey on ECG analysis
Selcan Kaplan Berkaya,Alper Kursat Uysal,Efnan Sora Gunal,Semih Ergin,Serkan Gunal,M. Bilginer Gülmezoğlu +5 more
TL;DR: The literature on ECG analysis, mostly from the last decade, is comprehensively reviewed based on all of the major aspects mentioned above.
References
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