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

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Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural network.

Chunsheng Li¹, Shiyue Liu¹, Zeyu Wang^1,2

¹Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang, China.

Frontiers in Physiology

|January 23, 2023

Summary

This summary is machine-generated.

Complex-valued phase-amplitude coupling (CV-PAC) enhances epilepsy localization by integrating coupling strength and phase information. This novel approach, utilizing complex-valued convolutional neural networks (CV-CNN), significantly improves the classification of epileptic brain activity.

Keywords:

SEEG complex-valued convolutional neural network complex-valued phase-amplitude coupling epilepsy epileptogenic zone

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A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

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Published on: May 20, 2016

Area of Science:

Neuroscience
Computational Neuroscience
Biomedical Engineering

Background:

Phase-amplitude coupling (PAC) is a key biomarker for localizing epileptogenic tissue, but distinguishing pathological from normal PAC is challenging.
Traditional PAC analysis often focuses on coupling strength, neglecting crucial phase information.
Existing methods struggle to differentiate between normal and pathological PAC, limiting diagnostic accuracy.

Purpose of the Study:

To introduce a novel complex-valued phase-amplitude coupling (CV-PAC) method for improved classification of epileptic PAC.
To integrate both coupling strength and phase information of low-frequency oscillations (LFOs) into a unified measure.
To evaluate the performance of CV-PAC combined with a complex-valued convolutional neural network (CV-CNN) for epilepsy diagnosis.

Main Methods:

Development of complex-valued phase-amplitude coupling (CV-PAC) to capture both amplitude and phase dynamics.
Application of a complex-valued convolutional neural network (CV-CNN) for classifying epileptic CV-PAC.
Analysis of stereo-electroencephalography (SEEG) data from nine epilepsy patients using leave-one-out cross-validation.

Main Results:

The CV-PAC/CV-CNN approach achieved a high area-under-curve (AUC) of 0.92 for classifying epileptic PAC.
This performance surpassed traditional methods, including standard CNNs (AUC=0.89), SVM (AUC=0.80), and Random Forest (AUC=0.88).
Significant differences in delta and alpha band phases were observed between epileptic and normal CV-PAC, highlighting the importance of phase information.

Conclusions:

Complex-valued phase-amplitude coupling (CV-PAC) effectively incorporates phase information, crucial for enhancing PAC analysis.
The CV-PAC/CV-CNN framework offers a promising, accurate method for identifying epileptic brain activity.
This approach holds potential for improving surgical intervention planning in intractable epilepsy cases.