Optimization of ECG Classification by Means of Feature Selection

By Tanis Mar, Sebastian Zaunseder, Juan Pablo Martínez, Mariano Llamedo, Rüdiger Poll. IEEE Transactions on Biomedical Engineering, 2011

306 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5e403138d2db1ac25af1fe714fe04ac009c18659

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

1. Serkan Kiranyaz
2. Moncef Gabbouj
3. Turker Ince
4. M. Zhou
5. Taiyong Li
6. Onur Avcı
7. Osama Abdeljaber
8. Daniel J. Inman

In Control and Systems Engineering

1. Moncef Gabbouj
2. Serkan Kiranyaz
3. Turker Ince
4. M. Zhou

In Signal Processing

1. M. Zhou
2. Serkan Kiranyaz
3. Turker Ince
4. Moncef Gabbouj
5. Juan Pablo Martínez
6. Mariano Llamedo

Citations in the corpus

• Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks (2016, background): “In the ECG classification literature, a vast number of features, their combinations, and feature selection approaches have been proposed [20].”
• ECG Classification Using Wavelet Packet Entropy and Random Forests (2016, methodology): “The authors in [25] used the sequential forward floating search to get a feature subset and then MLP was applied to do the classification.”