A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning

Electrocardiography (ECG) signal recognition is one of the popular research topics for machine learning. In this paper, a novel transformation called tower graph transformation is proposed to classify ECG signals with high accuracy rates. It employs a tower graph, which uses minimum, maximum and ave...

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Vydáno v:	Journal of ambient intelligence and humanized computing Ročník 14; číslo 2; s. 711 - 725
Hlavní autoři:	Subasi, Abdulhamit, Dogan, Sengul, Tuncer, Turker
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023 Springer Nature B.V
Témata:	Accuracy Artificial Intelligence Artificial neural networks Automation Cardiac arrhythmia Cardiac stress tests Classification Computational Intelligence Deep learning Electrocardiography Engineering Feature extraction Feature selection Fourier transforms Heart Machine learning Methods Neural networks Original Research Robotics and Automation Signal classification Signal processing Transformations User Interfaces and Human Computer Interaction Wavelet transforms Deep learning Electrocardiography (ECG) Tower graph Hexadecimal local adaptive binary pattern
ISSN:	1868-5137, 1868-5145
On-line přístup:	Získat plný text
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