Improved spiking neural networks for EEG classification and epilepsy and seizure detection

By Samanwoy Ghosh-Dastidar, Hojjat Adeli. Integr. Comput. Aided Eng., 2007

364 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/922c74cd57e0e709232c9e8a3f2b185de322b6c3

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Citations in the corpus

Deep Learning in Spiking Neural Networks (2018, neutral): “…Kannampuzha, & Neuschaefer -Rube, 2009; Liaw & Berger, 1998; Loiselle, Rouat, Pressnitzer, & Thorpe, 2005; Näger, Storck, &Deco, 2002; Panchev&Wermter, 2004; Tavanaei & Maida, 2017a;Wade, McDaid, Santos, & Sayers, 2010), andmedical diagnosis (Ghosh-Dastidar & Adeli, 2007; Kasabov et al., 2014).”
A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection (2009, methodology): “…training performance, SNNs have been used with various training algorithms such as backpropagation with momentum, QuickProp, resilient propagation (RProp), and Levenberg–Marquardt BP (Ghosh-Dastidar & Adeli, 2007; McKennoch, Liu, & Bushnell, 2006; Silva & Ruano, 2005; Xin & Embrechts, 2001).”
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, neutral): “Ghosh-Dastidar and Adeli (2007) [43] • Spike neural network learning algorithms Spiking neural network ACC: 92.5% Ghosh-Dastidar et al.”
Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. (2015, positive): “Adeliandassociatesadvancedtheideaofamulti-paradigm approachforautomatedEEG-baseddiagnosisofepilepsythrough adroitintegrationofwavelets,asignalprocessingtechnique, nonlineardynamics[6] [14] [23] [54] [100] [102]andchaostheory[19], andneuralnetworks,apatternrecognitionandclassification method[15,17,19”
Application of Recurrence Quantification Analysis for the Automated Identification of Epileptic EEG Signals (2011, neutral): “Ghosh-Dastidar et al.(24) Mixed-band feature space (9) Spiking Neural Network 92.”
A probabilistic neural network for earthquake magnitude prediction (2009, neutral): “…Sabesan, Tsakalis, & Iasemidis, 2007; Chen, Jiang, Wu, & Chen, 2007; Chiappalone, Vato, Berdondini, Koudelka, & Martinoia, 2007; Cutsuridis, 2007; Ghosh-Dastidar & Adeli, 2007; Guanella, Kiper, & Verschure, 2007; Iglesias & Villa, 2008; Mohan & Morasso, 2007; Montina, Mendoza, & Arecchi, 2007;…”
A comparative study of wavelet families for EEG signal classification (2011, background): “The sole purpose of feature extraction, is to extract salient characteristics from digitized data collected from the data acquisition phase [3] followed by classification based on the extracted features [4] [5] [6] [7] [8].”
Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting (2010, positive): “In the past decade, spiking neural networks have been successfully applied to various classiﬁcation tasks (Bohte et al., 2002; Ghosh-Dastidar & Adeli, 2007; G¨utig & Sompolinsky, 2006; Hopﬁeld & Brody, 2001; Maass et al., 2002; Maass, Natschlaeger, & Markram, 2004; Muresan, 2002).”
NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data (2014, neutral): “…the study of various brain states, conditions and diseases (Benuskova & Kasabov, 2007; Genes and Diseases) such as: epilepsy (Fiasché et al., 2012; Ghosh-Dastidar & Adeli, 2007; Mammone et al., 2012); schizophrenia; mental retardation; brain aging; Parkinson disease; clinical depression; stroke;…”
Automated EEG analysis of epilepsy: A review (2013, methodology): “Ghosh-Dastidar and Adeli [33] Mixed-band feature space Spiking neural network 92.”
Enhanced probabilistic neural network with local decision circles: A robust classifier (2010, neutral): “networks [30–32,69], neural dynamics models [7,8,”
An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems (2014, neutral): “To date, SNNs have been widely investigated in various aspects, such as fundamental issues like biologically plausible models(10,39,49,50,64,66,74,75) and training algorithms,(26,72,79) hardware/software implementation,(41,65) and wide applications.(11,27,45,69) The most attractive feature that SNNs”