Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification

The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia. However, the ECG signal is prone to contamination by different kinds of noise. Such noise may cause deformation on the ECG heartbeat waveform, leading to cardiologists’ mislabeling or misinterpreting heartbeats...

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Bibliographic Details
Published in:	Electronics (Basel) Vol. 9; no. 1; p. 135
Main Authors:	Nurmaini, Siti, Darmawahyuni, Annisa, Sakti Mukti, Akhmad Noviar, Rachmatullah, Muhammad Naufal, Firdaus, Firdaus, Tutuko, Bambang
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.01.2020
Subjects:	Arrhythmia Artificial neural networks Automation Bias Classification Deep learning Discriminant analysis Electrocardiography Feature extraction Machine learning Neural networks Noise reduction Principal components analysis Representations Signal processing Signal to noise ratio Training Waveforms Wavelet transforms
ISSN:	2079-9292, 2079-9292
Online Access:	Get full text
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