Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals
By Can Ye, B. V. Vijaya Kumar, Miguel Tavares Coimbra. IEEE Transactions on Biomedical Engineering, 2012
488 citations recorded by the source corpus.
Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Signal Processing
View Paper: https://www.semanticscholar.org/paper/f97d71251f13cc3ea3d5dc58770fd784ccfaef45
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cardiology and Cardiovascular Medicine
- Farid Melgani
- Yakoub Bazi
- Xiaoqing Luo
- Jun Dong
- Kup-Sze Thomas Choi
- Zhancheng Zhang
- Xiaojun Wu
- Abdulhamit Subasi
In Cognitive Neuroscience
- Farid Melgani
- Yakoub Bazi
- Xiaoqing Luo
- Jun Dong
- Kup-Sze Thomas Choi
- Zhancheng Zhang
- Xiaojun Wu
- Abdulhamit Subasi
In Signal Processing
Citations in the corpus
- Heartbeat classification using disease-specific feature selection (2014, methodology): “The work can be categorized into two classification paradigms, namely, “intra-patient” and “inter-patient”, or also known as “classoriented” and “subject-oriented” respectively [8].”
- Deep learning approach for active classification of electrocardiogram signals (2016, methodology): “Then feature reduction techniques such as principal component analysis (PCA), independent component analysis (ICA), and linear discriminant analysis (LDA) are usually applied to reduce the dimensionality of the feature representation [42,62,63].”