Epilepsy EEG classification method based on supervised locality preserving canonical correlation analysis

Seizure Detection of EEG Signals Based on Multi-Channel Long- and Short-Term Memory-Like Spiking Neural Model

Seizure is a common neurological disorder that usually manifests itself in recurring seizure, and these seizures can have a serious impact on a person's life and health. Therefore, early detection and diagnosis of seizure is crucial. In order to improve the efficiency of early detection and diagnosis of seizure, this paper proposes a new seizure detection method, which is based on discrete wavelet transform (DWT) and multi-channel long- and short-term memory-like spiking neural P (LSTM-SNP) model. First, the signal is decomposed into 5 levels by using DWT transform to obtain the features of the components at different frequencies, and a series of time-frequency features in wavelet coefficients are extracted. Then, these different features are used to train a multi-channel LSTM-SNP model and perform seizure detection. The proposed method achieves a high seizure detection accuracy on the CHB-MIT dataset: 98.25% accuracy, 98.22% specificity and 97.59% sensitivity. This indicates that the proposed epilepsy detection method can show competitive detection performance.

Epileptic Seizure Detection and Prediction in EEGs Using Power Spectra Density Parameterization

Power spectrum analysis is one of the effective tools for classifying epileptic signals based on electroencephalography (EEG) recordings. However, the conflation of periodic and aperiodic components within the EEG may presents an obstacle to epilepsy detection or prediction. In this paper, we explored the significance of the periodic and aperiodic components of the EEG power spectrum for the detection and prediction of epilepsy respectively. We use a power spectrum density parameterization method to separate the periodic and aperiodic components of the signals, and validate their roles in epilepsy detection and prediction on two public datasets. The average classification accuracy of the periodic and aperiodic components for 10 clinical tasks on the Bonn EEG database were 73.9% and 96.68%, respectively, and increases to 98.88% when combined. For 22 patients on the CHB-MIT Long-term EEG database, the combined features achieve an average detection accuracy of 99.95% and successfully predict all seizures with low false prediction rates. We conclude that both the periodic and aperiodic components of the EEG power spectrum contributed to discriminating different stages of epilepsy, but the aperiodic neural activity played a decisive role in classification. This discovery has significant implications for diagnosing epileptic seizures and providing personalized brain activity information to improve the accuracy and efficiency of epilepsy detection.

Epileptic detection in single and multi-lead EEG signals using persistent homology based on bi-directional weighted visibility graphs

Epilepsy is a widespread neurological disorder, and its recurrence and suddenness are making automatic detection of seizure an urgent necessity. For this purpose, this paper performs topological data analysis (TDA) of electroencephalographic (EEG) signals by the medium of graphs to explore the potential brain activity information they contain. Through our innovative method, we first map the time series of epileptic EEGs into bi-directional weighted visibility graphs (BWVGs), which give more comprehensive reflections of the signals compared to previous existing structures. Traditional graph-theoretic measurements are generally partial and mainly consider differences or correlations in vertices or edges, whereas persistent homology (PH), the essential part of TDA, provides an alternative way of thinking by quantifying the topology structure of the graphs and analyzing the evolution of these topological properties with scale changes. Therefore, we analyze the PH for BWVGs and then obtain the two indicators of persistence and birth-death for homology groups to reflect the topology of the mapping graphs of EEG signals and reveal the discrepancies in brain dynamics. Furthermore, we adopt neural networks (NNs) for the automatic detection of epileptic signals and successfully achieve a classification accuracy of 99.67% when distinguishing among three different sets of EEG signals from seizure, seizure-free, and healthy subjects. In addition, to accommodate multi-leads, we propose a classifier that incorporates graph structure to distinguish seizure and seizure-free EEG signals. The classification accuracies of the two subjects used in the classifier are as high as 99.23% and 94.76%, respectively, indicating that our proposed model is useful for the analysis of EEG signals.