Application of Recurrence Quantification Analysis for the Automated Identification of Epileptic EEG Signals

By Usha R. Acharya, Subbhuraam Vinitha Sree, Subhagata Chattopadhyay, Wenwei Yu, Peng Chuan Alvin Ang. International Journal of Neural Systems, 2011

317 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/21fca5328a2b0a1edcfac6f8b6740172a43f88ef

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Usha R. Acharya
2. Subbhuraam Vinitha Sree
3. Jasjit S. Suri
4. G. Swapna
5. Roshan Joy Martis
6. Hojjat Adeli
7. Shu Lih Oh
8. Yuki Hagiwara

In Cognitive Neuroscience

1. Usha R. Acharya
2. Yuki Hagiwara
3. Shu Lih Oh
4. Jen Hong Tan
5. Jasjit S. Suri
6. Subbhuraam Vinitha Sree
7. Roshan Joy Martis
8. G. Swapna

In Signal Processing

1. Usha R. Acharya
2. Yuki Hagiwara
3. Shu Lih Oh
4. Hojjat Adeli
5. Jasjit S. Suri
6. Jen Hong Tan
7. Subbhuraam Vinitha Sree
8. G. Swapna

Citations in the corpus

• Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, methodology): “(2011b) [47] • Recurrence quantification analysis SVM ACC: 95.”
• Automated EEG analysis of epilepsy: A review (2013, methodology): “[4] used RP for identifying normal, interictal, and ictal EEG signals.”