Analysis of EEG Signals for Detection of Epileptic Seizure Using Hybrid Feature Set

Abstract

Epileptic Seizures occur as a result of certain electrical action in the brain. This makes the patient behave abnormally for a limited amount of time. The electrical activity can be measured with the help electrodes attached to different areas of the scalp to capture the EEG signals. Usually, the signals from the aforementioned device are interpreted by the specialists who specialize in this very thing but their detection is susceptible to errors which prove fatal in some cases. This paper provides an automated system which will detect epileptic seizure without involving an expert opinion. The proposed system goes through a four step process i.e. pre-processing, where the data is organized to suit the system processing and noise is removed. Then temporal and spectral feature extraction is performed. The system then applies the feature selection procedure to extract best set of features which are finally passed to the next phase for classification of EEG signals as normal or abnormal. The suggested system is established on a publicly open dataset and provides an average accuracy of 86.93 %.

Peri‐ictal and non‐seizure EEG event detection using generated metadata

“…They have used consecutive 1 seconds window length for 23 channel of CHB‐MIT EEG. Gill et al (2015) have used 1000 samples in each EEG segment and calculated entropy, mean, harmonic mean, range, inter quartile range, and mean absolute deviation, central moments, skewness, kurtosis, percentile values, and gradient points from each segment of data. They obtained an average accuracy, sensitivity and specificity of 86.93%, 86.26% and 87.58% for different patients.…”

“…Their analysis shows significant challenges faced while comparing between short seizure and seizure-like EEG activities. They have used consecutive 1 seconds window length for 23 channel of CHB-MIT EEG Gill et al (2015). have used 1000 samples in each EEG segment and calculated entropy, mean, harmonic mean, range, inter quartile range, and mean absolute deviation, central moments, skewness, kurtosis, percentile values, and gradient points from each segment of data.…”

Patient-dependent epilepsy seizure detection using random forest classification over one-dimension transformed EEG data

“…A good system response depends on the combination of the internal selected algorithms. Concerning feature selection, the majority of alternatives relies, at least partially, on spectral features: STFT, wavelet transform, or discrete Fourier transform (Ahmad et al, 2015;Gill et al, 2015;Tsiouris et al, 2015;Das et al, 2016;Orosco et al, 2016). Also, the dimension reduction using PCA or an improved algorithm based on it proved to reach accuracies close to 100% (Zhao et al, 2016).…”

“…The present research combined both processing types using STFT over biosignals and then PCA on the time-spectrum data. The no- (Gill et al, 2015), linear discriminators or neural networks (Orosco et al, 2016).…”

“…A rst distinction between the approaches is the specic domain of the EEG data on which they have focused: Time series, frequency data, or spectro-temporal signals (Ahmad et al, 2015;Das et al, 2015). Another signicant dierence is the set of features which each method obtained in its analysis: Some studies utilized statistical, chaos theory, or information theory parameters (Ahmad et al, 2015;Das et al, 2015;Gill et al, 2015), while other eorts applied data transformations such as singular value decomposition (SVD) or principal components analysis (PCA) (Zhao et al, 2016). A third prominent divergence among the previously proposed methods is the level of articial intelligence dependency to perform a decision.…”