Heartbeat classification using disease-specific feature selection
By Zhancheng Zhang, Jun Dong, Xiaoqing Luo, Kup-Sze Thomas Choi, Xiaojun Wu. Comput. Biol. Medicine, 2014
334 citations recorded by the source corpus.
Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Pulmonary and Respiratory Medicine
View Paper: https://www.semanticscholar.org/paper/9eb3a02044b0dea60b409444acce1513f151435a
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
- Eduardo José da S. Luz
- Guillermo Cámara Chávez
- David Menotti
- William Robson Schwartz
- Abdulhamit Subasi
- M. Zhou
In Cognitive Neuroscience
- Farid Melgani
- Yakoub Bazi
- Eduardo José da S. Luz
- Guillermo Cámara Chávez
- David Menotti
- William Robson Schwartz
- Abdulhamit Subasi
- M. Zhou
In Pulmonary and Respiratory Medicine
- Farid Melgani
- Yakoub Bazi
- Eduardo José da S. Luz
- Guillermo Cámara Chávez
- David Menotti
- William Robson Schwartz
Citations in the corpus
- Deep learning approach for active classification of electrocardiogram signals (2016, methodology): “3 Despite these great efforts, it has been shown recently [14,27,64] that automatic methods do not perform well if the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for class labeling and results presentation are closely followed as a possible solution for ”
- A Systematic Review on Imbalanced Data Challenges in Machine Learning (2019, background): “Feature selection and extraction is found 79:17 (Zhang et al. 2014).”
- ECG-based heartbeat classification for arrhythmia detection: A survey (2016, background): “[35] RR-intervals, Combined SVM Acc = 86%; morphological features, SeN = 89%; +PN = 99%; ECG-intervals and segments SeS = 79%; +PS = 35%; SeV = 85%, +PV = 92%; Escalona-Moran et al.”