Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation.

Yiping Wang,Mowei Ouyang,Da Liu,Gongpeng Cao,Jinjie Guo,Guixia Kang,Guoguang Zhao,Yongzhi Shan,Zimo Liu,Yang Dai

doi:10.3390/brainsci11050615

Abstract

Surgical intervention or the control of drug-refractory epilepsy requires accurate analysis of invasive inspection intracranial EEG (iEEG) data. A multi-branch deep learning fusion model is proposed to identify epileptogenic signals from the epileptogenic area of the brain. The classical approach extracts multi-domain signal wave features to construct a time-series feature sequence and then abstracts it through the bi-directional long short-term memory attention machine (Bi-LSTM-AM) classifier. The deep learning approach uses raw time-series signals to build a one-dimensional convolutional neural network (1D-CNN) to achieve end-to-end deep feature extraction and signal detection. These two branches are integrated to obtain deep fusion features and results. Resampling is employed to split the imbalanced epileptogenic and non-epileptogenic samples into balanced subsets for clinical validation. The model is validated over two publicly available benchmark iEEG databases to verify its effectiveness on a private, large-scale, clinical stereo EEG database. The model achieves high sensitivity (97.78%), accuracy (97.60%), and specificity (97.42%) on the Bern–Barcelona database, surpassing the performance of existing state-of-the-art techniques. It is then demonstrated on a clinical dataset with an average intra-subject accuracy of 92.53% and cross-subject accuracy of 88.03%. The results suggest that the proposed method is a valuable and extremely robust approach to help researchers and clinicians develop an automated method to identify the source of iEEG signals.

Highlights

The goal of our study is to judge whether intracranial EEG (iEEG) originates from epileptogenic zones of the brain—that is, to achieve epileptogenic signal identification—which becomes a binary classification problem
The true positive count (TP) represents the number of epileptogenic signals correctly identified, the true negative count (TN) represents the number of non-epileptogenic signals correctly identified, the false positive count (FP) represents the number of signals falsely identified as epileptic signals, and the false negative count (FN) represents the number of signals falsely identified as non-epileptic signals
The experiment extracted multiple features across multiple domains selected in the Methods section and compared them with different machine learning algorithms or single models, such as the support vector machine (SVM) [35], logistic regression (LR) [36], extreme randomized tree (ERT) [37], deep neural network (DNN), 1D-convolutional neural networks (CNNs) and Bi-StackLSTM models [38]

Summary

Introduction

With the aim of the precise administration of the disease to achieve a better outcome, invasive inspection techniques are essential for the articulation of specific epileptogenic and nonepileptogenic signals [4]. Different intracranial EEG (iEEG) invasive inspection techniques, such as electrocorticography (ECoG) and stereo-electroencephalography (SEEG), have been established to locate epileptogenic focus [5]. IEEG signals provide anatomically precise information about the selective engagement of neuronal populations at the millimeter scale and the temporal dynamics of their engagement at the millisecond scale, and they play a dominant role in the discovery and detection of particular zones in the impacted brain [6]. SEEG is considered the “gold standard” method to evaluate the epileptogenic zones for electrodes implanted in the deep brain and can record interictal and ictal epileptic discharges for days before deciding the extent of resection [6,7,8,9].

Objectives

Methods

Results

Discussion

Conclusion