Skip to main content

Table 1 Feature extraction methods and features used on EEG signal dataset

From: A review of epileptic seizure detection using machine learning classifiers

Feature extraction methods Relevant features
Time-domain features Mean, variance, mode, median, skewness, kurtosis, max, min, zero crossing, line length, energy, power, Shannon entropy, sample entropy, approximate, entropy, fuzzy entropy, hurst exponent, standard deviation
Frequency-domain features Spectral power, spectral entropy, energy, peak frequency, median frequency
Time–frequency-domain features Line length, min, max, Shannon entropy, approximate entropy, standard deviation, energy, median, root mean square
Discrete Wavelet Transformation (DWT) Bounded variation, coefficients, energy, entropy, relative bounded, variation, relative power, relative scale energy, variance, standard deviation
Continuous Wavelet Transformation (CWT) Energy’s standard deviation, energy, coefficient z-score, entropy,
Fourier Transformation (FT) Median frequency, power, peak frequency, spectral entropy power, spectral edge frequency, total spectral power