Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network
By U. Acharya, Hamido Fujita, Shu Lih Oh, M. Adam, Jen Hong Tan, Chua Kuang Chua. Knowledge-Based Systems, 2017
350 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/445a98536850cd296b10cd22e6711ab8c7d5ef7f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Cardiology and Cardiovascular Medicine
- Usha R. Acharya
- Yuki Hagiwara
- Shu Lih Oh
- Jen Hong Tan
- Hojjat Adeli
- Santi Kumari Behera
- Dr. Prabira Kumar Sethy
In Cognitive Neuroscience
- Usha R. Acharya
- Yuki Hagiwara
- Shu Lih Oh
- Jen Hong Tan
- Hojjat Adeli
- Samanwoy Ghosh-Dastidar
- Santi Kumari Behera
- Dr. Prabira Kumar Sethy
Citations in the corpus
- Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, methodology): “CNN was recently employed in the automated diagnosis of myocardial infraction [27] and coronary artery disease [29] using ECG signals.”