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 |