A Novel Automated Empirical Mode Decomposition (EMD) Based Method and Spectral Feature Extraction for Epilepsy EEG Signals Classification

Abstract

The increasing incidence of epilepsy has led to the need for automatic systems that can provide accurate diagnoses in order to improve the life quality of people suffering from this neurological disorder. This paper proposes a method to automatically classify epilepsy types using EEG recordings from two databases. This approach uses the spectral power density of intrinsic mode functions (IMFs) that are obtained through the empirical mode decomposition (EMD) of EEG signals. The spectral power density of IMFs has been applied as features for the classification of focal and non-focal, as well as of focal and generalized EEG signals. The data are then classified using K-nearest Neighbor (KNN) and Naïve Bayes (NB) classifiers. The focal and non-focal data were classified with high accuracy, with KNN and NB classifiers achieving a maximum classification rate of 99.90% and 99.80%, respectively. Focal and generalized epilepsy data were classified with high rates of accuracy during wakefulness and sleep stages, with KNN achieving a maximum rate of 99.49% and NB achieving 99.20%. This method shows significant improvements in the classification of EEG signals in epilepsy compared to previous studies. It could potentially aid clinical decisions for epilepsy patients.

Enhanced Classification of Focal and Generalized Epilepsy Using EEMD and CEEMDAN Methods

“…The current study is conducted using our own database obtained in the EEG Epilepsy and Monitoring Center in Cluj-Napoca, Romania [18]. The database comprises interictal epileptiform EEG recordings obtained from 16 patients ranging in age from 7 to 66 years, diagnosed with focal and generalized epilepsy.…”

“…While the EEMD method effectively addresses the issue of mode mixing, it still has certain limitations. These include the presence of residual noise in the reconstructed signal and varying numbers of modes in different trials [18].…”

“…By extracting relevant features from the decomposed signals, a robust and accurate classification algorithm is developed. These approaches have the potential to overcome the limitations of existing methods and to improve the efficiency and reliability of epilepsy diagnosis [18]. The current research uses a property database consisting of interictal focal and generalized EEG epileptic signals recorded during the sleep state.…”

Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation

“…By applying advanced signal processing techniques, such as decomposition techniques, strong signals can be separated from the noise, allowing prediction methods to focus on determining correlations between signals with strong patterns rather than those heavily affected by the noise. This concept has often been applied to systems that require precise moments in noise environments such as electroencephalography ( Murariu, Dorobanţu & Tărniceriu, 2023 ) demonstrating great potential. Several decomposition techniques have been developed in recently such as empirical mode decomposition (EMD) ( Boudraa & Cexus, 2007 ) and ensemble empirical mode decomposition (EEMD) ( Wu & Huang, 2009 ).…”

Detection Method of Epileptic Seizures Using a Neural Network Model Based on Multimodal Dual-Stream Networks

“…Conventional methods usually require an artificially designed approach to feature extraction and selection [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Guangpeng et al [ 12 ] extracted the time–frequency feature maps of interval EEG signals.…”