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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

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A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform.

Abhijit Bhattacharyya, Ram Bilas Pachori

IEEE Transactions on Bio-Medical Engineering

|January 17, 2017

Summary

This study introduces a new method for detecting epileptic seizures using multivariate electroencephalogram (EEG) signals. The approach achieves high accuracy in identifying seizure events in long-term EEG recordings.

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Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

Area of Science:

Signal Processing
Biomedical Engineering
Neurology

Background:

Epileptic seizure detection from electroencephalogram (EEG) signals is crucial for patient care.
Analyzing the multivariate oscillatory nature of EEG signals presents a complex challenge.

Purpose of the Study:

To investigate the multivariate oscillatory nature of EEG signals using adaptive frequency scales for improved epileptic seizure detection.
To develop and validate a novel method for enhanced EEG seizure detection.

Main Methods:

A multivariate extension of the empirical wavelet transform (EWT) was applied to analyze joint instantaneous amplitudes and frequencies in adaptive scales.
Features were extracted from multivariate EEG signal epochs and processed for seizure and seizure-free discrimination.
The method was evaluated on the CHB-MIT scalp EEG database using a moving-window analysis.

Main Results:

The proposed method achieved high performance metrics: 97.91% average sensitivity, 99.57% specificity, and 99.41% accuracy.
These results surpass those of previously compared state-of-the-art methods on the same database.
The method demonstrated efficient detection of long-duration epileptic seizure events in EEG recordings.

Conclusions:

The developed method effectively utilizes the time-frequency plane for multivariate signals.
Patient-specific models for EEG seizure detection were successfully built, enhancing diagnostic capabilities.