Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform

By Kemal Polat, Salih Günes. Applied Mathematics and Computation, 2007

706 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/17a1f003c90bbc51d6b726d398193ca416717890

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Citations in the corpus

AI in Health: State of the Art, Challenges, and Future Directions (2019, background): “, Fourier or wavelet transform [46-48]), and then build analysis algorithms on top of these vectors.”
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG (2019, methodology): “31% [PG07] Peak amplitude, peak frequency PSD (ARMA) + GMM Sen = 70.”
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review (2020, methodology): “One can used either parametric or non-parametric methods to estimate the power spectrum using FT [27].”
Binary PSO with mutation operator for feature selection using decision tree applied to spam detection (2014, methodology): “Those pruning algorithms based on posterior calculations such as BIC (MDL) and MEP are faster [22], but they sometimes produce too big or small trees that yield poor generalization errors [23].”
Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short-Time Fourier Transform (2015, methodology): “Besides, due to consideration of all coefficients in Welch PSD-based method, a large-dimensional feature vector is obtained in contrast with our method which uses only 32 + 5 features.”
Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. (2010, methodology): “Polat and Güneş (2007) Fast Fourier transform—decision tree Z–S 98.”
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.”
Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis (2009, background): “These measures can then be combined with a classifier [19], [21], [24]–[26], [30], [33] to identify the occurrence of seizures.”
A review of epileptic seizure detection using machine learning classifiers (2020, neutral): “Kemal and Saleh [120] used a C5.”