A review of feature extraction and performance evaluation in epileptic seizure detection using EEG

By Poomipat Boonyakitanont, Apiwat Lek-uthai, Krisnachai Chomtho, Jitkomut Songsiri. Biomedical Signal Processing and Control, 2019

345 citations recorded by the source corpus.

Since the manual detection of electrographic seizures in continuous electroencephalogram (EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop automatic seizure detection are diverse and ongoing. Machine learning approaches are intensely being applied to this problem due to their ability to classify seizure conditions from a large amount of data, and provide pre-screened results for neurologists. Several features, data transformations, and classifiers have been explored to analyze and classify seizures via EEG signals. In the literature, some jointly-applied features used in the classification may have shared similar contributions, making them redundant in the learning process. Therefore, this paper aims to comprehensively summarize feature descriptions and their interpretations in characterizing epileptic seizures using EEG signals, as well as to review classification performance metrics. To provide meaningful information of feature selection, we conducted an experiment to examine the quality of each feature independently. The Bayesian error and non-parametric probability distribution estimation were employed to determine the significance of the individual features. Moreover, a redundancy analysis using a correlation-based feature selection was applied. The results showed that the following features --variance, energy, nonlinear energy, and Shannon entropy computed on a raw EEG signal, as well as variance, energy, kurtosis, and line length calculated on wavelet coefficients-- were able to significantly capture the seizures. An improvement of 4.77--13.51% in the Bayesian error from the baseline was obtained.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d124841a68e2ef11bdccc52089d0c2b2ae883501

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In Cognitive Neuroscience

1. Rubén Morales-Menéndez
2. Xiaodi Huang
3. Mohammad Khubeb Siddiqui
4. Nasir Hussain

In Signal Processing

1. Li-Ming Zhao
2. Xiaodi Huang
3. Wei-Bang Jiang
4. Bao-Liang Lu

Citations in the corpus

• A review of epileptic seizure detection using machine learning classifiers (2020, methodology): “Whilst some researchers used many features [34, 79], the others applied a few features [31, 36, 108, 112, 137] for detecting the seizure.”
• Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI (2024, background): “As highly objective physiological signals, EEG has demonstrated remarkable potential in seizure epilepsy classification (Boonyakitanont et al., 2020), acute stress detection (Sharma et al., 2022), sleep stage classification (Aboalayon et al., 2016), motor imagery recognition (Amin et al., 2019),…”