Analysis of EEG records in an epileptic patient using wavelet transform.

By Hojjat Adeli, Ziqin Zhou, Nahid Dadmehr. Journal of Neuroscience Methods, 2003

1,196 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/fea7a7b79f434c2c47dc5b3bf9160b055a6e042b

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Citations in the corpus

EEG signal classification using PCA, ICA, LDA and support vector machines (2010, methodology): “Some relative weaknesses in this literature are the lack of extensive testing on baseline data free from seizures, the lack of technically rigorous validation and quantification of algorithm performance in many studies (Adeli et al., 2003; D’Alessandro et al., 2003; Subasi, 2006, 2007).”
Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. (2010, background): “The attractive feature ofWT is that it provides accurate frequency information at low frequencies and accurate time information at high frequencies (Adeli et al., 2003).”
Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals (2015, background): “In the literature, many studies are presented to analyze Alzheimer’s disease [1], attention-deficit/hyperactivity disorder (ADHD) [2], autism [3], autistic spectrum disorder [4], alcoholism [5], epilepsy [6,7], depth of anesthesia [8,9], etc.”
EEG signals classification using the K-means clustering and a multilayer perceptron neural network model (2011, methodology): “In this context, the EEG recordings measured in seizure-free intervals from the epilepsy patients are considered as important components for the diagnosis or prediction process (Adeli et al., 2003; Subasi, 2005a, 2007).”
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017, background): “[7], automated EEG-based seizure detection and epilepsy diagnosis has been the subject of significant research.”
A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy (2007, methodology): “886855 such as spikes [2], [3] which may be precursors to seizures;”
Mixed-Band Wavelet-Chaos-Neural Network Methodology for Epilepsy and Epileptic Seizure Detection (2007, methodology): “Recent attempts to detect seizures from EEG analysis employ two different approaches: 1) examination of the waveforms in the interictal EEG to find events (markers) or changes in neuronal activity such as spikes [ 3 ], [4] which may be precursors to seizures and 2) analysis of the nonlinear spatio-t”
Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short-Time Fourier Transform (2015, methodology): “[6], [7] applied Daubechies wavelet transform to analyze the epileptic seizure pattern in five frequency subbands: delta (0–4 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta (13–30 Hz), and gamma (30-60 Hz).”
EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks (2006, background): “[3] gave an overview of the discrete wavelet transform (DWT) developed for recognising and quantifying spikes, sharp waves and spike-waves.”
EEG signal classification using wavelet feature extraction and a mixture of expert model (2007, neutral): “(Adeli et al., 2003; Subasi, 2005a).”
Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection (2008, neutral): “shape which is not detected consistently by spike detection algorithms reported in the literature [2], [3].”
Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. (2015, neutral): “[6] for diagnosis of the absence seizure more than a decade ago.”