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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Electronics (Basel) Jg. 9; H. 1; S. 135
Hauptverfasser:	Nurmaini, Siti, Darmawahyuni, Annisa, Sakti Mukti, Akhmad Noviar, Rachmatullah, Muhammad Naufal, Firdaus, Firdaus, Tutuko, Bambang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.01.2020
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!