A neural-network-based detection of epilepsy

By V. Nigam, D. Graupe. Neurological Research, 2004

371 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1d2756166c2593ce196828ce480935a60f6e6285

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Citations in the corpus

Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features (2005, methodology): “The overall detection accuracy rate obtained in this case is about 99.6%, which is better than the overall detection accuracy of 98.4% obtained with the LAMSTAR neural network(8) using two time-domain features, namely, spike rhythmicity and relative spike amplitude as the inputs.”
Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. (2010, background): “Many types of features have been investigated based on spectral (Nigam and Graupe, 2004; Polat and Güneş, 2007) or wavelet features (Adeli et al.”
Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks (2007, methodology): “[15] (2004) Nonlinear preprocessing filter, diagnostic artificial neural network (LAMSTAR) Z, S 97.”
Application of Recurrence Quantification Analysis for the Automated Identification of Epileptic EEG Signals (2011, neutral): “al.(18) Non-linear preprocessing filterArtificial Neural Network 97.”
Multiclass Support Vector Machines for EEG-Signals Classification (2007, methodology): “…rates of the multiclass SVM and PNN presented for this application were found to be higher than that of the ANN model presented by Nigam and Graupe [26]. iv) During SVM training, most of the computational effort is spent on solving the quadratic programming problem in order to ﬁnd the support…”
Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks (2007, methodology): “In 2004, Nigam and Graupe [9] proposed a new neural network model called LAMSTAR network, and two time-domain attributes of EEG, namely, relative spike amplitude and spike rhythmicity have been used as inputs for the purpose of the detection of epilepsy.”
Deep Learning Based Approach for Bearing Fault Diagnosis (2017, methodology): “age retrieval (LAMSTAR) neural network has been applied in multiple fields such as image recognition [17], biomedical diagnosis [18]–[21], with solid result showing the ability of LAM-”
Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. (2005, methodology): “Thus, the accuracy rates of the ANFIS model presented for this application were found to be higher than that of the ANN model presented by Nigam and Graupe (2004). The classification accuracy, which is defined as the percentage ratio of the number of segments correctly classified to the total number”
Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. (2010, methodology): “Nigam and Graupe (2004) Non-linear preprocessing filter—diagnostic neural network Z–S 97.”
Automated EEG analysis of epilepsy: A review (2013, methodology): “Nigam and Graupe [71] Nonlinear pre-processing filter Diagnostic neural network 97.20 Kannathal et al. [59] Entropy measures Adaptive Neuro-Fuzzy Inference system (ANFIS) 92.22 Srinivasan et al. [103] Time & frequency domain features Elman network 99.60 Sadati et al. [99] DWT Adaptive neural fuzzy n”
EEG signal classification using PCA, ICA, LDA and support vector machines (2010, methodology): “Nigam and Graupe (2004) used the same EEG data sets (A and E) by using different feature extraction with ANN and the total classification accuracy of their model was 97.2%.”