Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection

By Samanwoy Ghosh-Dastidar, Hojjat Adeli, Nahid Dadmehr. IEEE Transactions on Biomedical Engineering, 2008

476 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/97a7bf9563f8e4c30b051dd1e77c04eff5374766

How researchers see this paper

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

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. Mehran Ahmadlou
8. Anahita Adeli

In Signal Processing

1. Usha R. Acharya
2. Yuki Hagiwara
3. Mehran Ahmadlou
4. Anahita Adeli
5. Shu Lih Oh
6. Hojjat Adeli
7. Jasjit S. Suri
8. Jen Hong Tan

Citations in the corpus

• AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, unknown): “In an attempt to reduce data dimensionality or to learn in a data-driven fashion features, Principal and Independent ComponentAnalyseshavebeenusedand[93]alsothesomewhat related(withsomeassumptions)K-meansalgorithm[94].”
• New diagnostic EEG markers of the Alzheimer’s disease using visibility graph (2010, methodology): “The main goal of applying PCA to an input data set and using the outcome of the PCA analysis as input to a classifier is dimensional reduction of the input space without compromising the accuracy of the classification (Ghosh-Dastidar et al. 2008).”
• Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, background): “[38] propose a principal component analysis-enhance classifier for robust and reliable categorization of EEG signals into ictal, interictal, and healthy categories.”
• Application of Recurrence Quantification Analysis for the Automated Identification of Epileptic EEG Signals (2011, background): “al.(37) Mixed-band feature space -Radial basis function neural network (RBFNN) 96.”
• Application of Non-Linear and Wavelet Based Features for the Automated Identification of Epileptic EEG signals (2012, background): “GhoshDastida r et al.(29) Mixed-band feature space (9) Radial Basis Function Neural Network 96.”
• Enhanced probabilistic neural network with local decision circles: A robust classifier (2010, unknown): “Since the development of back propagation (BP) algorithm in the 1980’s [33,45,67] many attempts have been made to develop more efficient and robust neural networks and training algorithms.”