Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features

By V. Srinivasan, Chikkannan Eswaran, and N. Sriraam. Journal of medical systems, 2005

454 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/c857c747a59f9064ae593734ac28f8fd84b927f6

How researchers see this paper

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

In Cellular and Molecular Neuroscience

1. Dimitrios I. Fotiadis
2. Alexandros T. Tzallas
3. Markos G. Tsipouras
4. Alejandro Pazos
5. Daniel Rivero
6. Ling Guo
7. Natarajan Sriraam
8. V. Srinivasan

In Cognitive Neuroscience

1. Usha R. Acharya
2. Dimitrios I. Fotiadis
3. Alexandros T. Tzallas
4. Markos G. Tsipouras
5. Subbhuraam Vinitha Sree
6. Jasjit S. Suri
7. Roshan Joy Martis
8. G. Swapna

Citations in the corpus

• Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks (2007, methodology): “It can be concluded from the results that, in general, EN performs better than PNN for almost for all the combinations of m, r, and N. The feedback configuration and the incremental training process associated with the EN may be considered as possiblereasonsforitssuperiorperformance.Theoveralldetect”
• EEG signals classification using the K-means clustering and a multilayer perceptron neural network model (2011, unknown): “Subasi (2007) found that ME and MLPNN models classified healthy segments and epileptic seizure segments with the accuracy of 94.”
• Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks (2007, background): “[14] (2005) Time & frequency domain features, recurrent neural network (RNN) Z, S 99.”
• Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis (2009, methodology): “Principal component analysis (PCA), using 1% threshold, was employed to reduce the dimension of the feature set [44] resulting in three to four features in all cases.”
• Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. (2010, methodology): “In the work of Srinivasan et al. (2005), five features from time-domain and frequency-domain were employed individually or jointly for classifying EEG signals.”
• Automated EEG analysis of epilepsy: A review (2013, background): “[103] Time & frequency domain features Elman network 99.”
• Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. (2010, background): “The definitions of these parameters are as (Srinivasan et al., 2005):”