Abstract
In this paper, the authors propose a new technique for the classification of seizures, non-seizures, and seizure-free EEG signals based on non-linear trajectories of EEG signals. The EEG signals are decomposed using the EMD technique to obtain intrinsic mode functions (IMFs). The phase space of these IMFs is then reconstructed using a novel technique of higher-order dimensions (3D, 4D, 5D, 6D, 7D, 8D, 9D, and 10D). The existing techniques of seizure detection have deployed 2D & 3D phase–space reconstruction only. The Euclidean distance of all higher-order PSR is used as a feature to classify seizures, non-seizures, and seizure-free EEG signals. The performance of the proposed method is analyzed on the Bonn University database in which 7D reconstructed phase space classification accuracy of 99.9% has been achieved both using Random Forest classifier and J48 decision tree.
Highlights
Epilepsy is an acute nervous disorder occurring due to episodes of neuronal discharges within the brain.Around 0.8% of the world population is suffering from epilepsy
The first four intrinsic mode functions (IMFs) are used for feature extraction in phase space
The 2D phase space is an elliptical pattern and the area of the ellipse is computed for all EEG signals
Summary
Epilepsy is an acute nervous disorder occurring due to episodes of neuronal discharges within the brain. There exist various non-linear techniques [3,4,5,6,7,8] mostly based on phase-space to detect seizures. The most common and highly explored methods based on non-linear signal analysis include principal component analysis, Lyapunov exponent, etc. In [10,11] the classification of ictal and seizure-free EEG signals using a second-order difference plot (SODP) has been used. The brain is the most complex system in the human body that has adaptive, nonlinear, and dynamic characteristics It can be considered as a self-organizing spatially embedded network at different temporal and spatial scales. The latest technique [15] on phase space elliptic density has used a meta-heuristic optimization method for the detection of epileptic EEG signals. In this paper phase space is reconstructed for seizure and non-seizure EEG signals using 2D, 3D,4D,5D ,6D,7D,8D,9D,10D phase space plots
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