Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

Electrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several types of heart disorders. In this study, a novel hybrid approach of deep neural network combined with linear and nonlinear features extracted from ECG and heart rate variability (HRV) is proposed for ECG multi-c...

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Veröffentlicht in:	Neural computing & applications Jg. 34; H. 11; S. 8755 - 8775
Hauptverfasser:	Eltrass, Ahmed S., Tayel, Mazhar B., Ammar, Abeer I.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.06.2022 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Arrhythmia Artificial Intelligence Artificial neural networks Chaos theory Classification Classifiers Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computing time Data Mining and Knowledge Discovery Deep learning Diagnosis Discriminant analysis Electrocardiography Feature extraction Gabor transformation Heart rate Image Processing and Computer Vision Machine learning Neural networks Original Article Probability and Statistics in Computer Science And Heart Rate Variability (HRV) Deep Learning (DL) Electrocardiogram (ECG) Convolutional Neural Network (CNN)
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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