Automated Detection of Abnormal EEGs in Epilepsy With a Compact and Efficient CNN Model

• URL: http://arxiv.org/abs/2105.10358v1
• Date: Fri, 21 May 2021 16:52:56 GMT
• Title: Automated Detection of Abnormal EEGs in Epilepsy With a Compact and Efficient CNN Model
• Authors: Taku Shoji, Noboru Yoshida, Toshihisa Tanaka
• Abstract summary: This paper describes the development of a novel class of compact and efficient convolutional neural networks (CNNs) for detecting abnormal time intervals and electrodes in EEGs for epilepsy. Unlike the EEGNet, the proposed model, mEEGNet, has the same number of electrode inputs and outputs to detect abnormalities. Results showed that the mEEGNet detected abnormal EEGs with the area under the curve, F1-values, and sensitivity equivalent to or higher than those of existing CNNs.
• Score: 9.152759278163954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electroencephalography (EEG) is essential for the diagnosis of epilepsy, but it requires expertise and experience to identify abnormalities. It is thus crucial to develop automated models for the detection of abnormal EEGs related to epilepsy. This paper describes the development of a novel class of compact and efficient convolutional neural networks (CNNs) for detecting abnormal time intervals and electrodes in EEGs for epilepsy. The designed model is inspired by a CNN developed for brain-computer interfacing called multichannel EEGNet (mEEGNet). Unlike the EEGNet, the proposed model, mEEGNet, has the same number of electrode inputs and outputs to detect abnormalities. The mEEGNet was evaluated with a clinical dataset consisting of 29 cases of juvenile and childhood absence epilepsy labeled by a clinical expert. The labels were given to paroxysmal discharges visually observed in both ictal (seizure) and interictal (nonseizure) intervals. Results showed that the mEEGNet detected abnormal EEGs with the area under the curve, F1-values, and sensitivity equivalent to or higher than those of existing CNNs. Moreover, the number of parameters is much smaller than other CNN models. To our knowledge, the dataset of absence epilepsy validated with machine learning through this research is the largest in the literature.

Related papers

• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications [3.031375888004876]
  Two main issues challenge the existing DL-based modeling methods for EEG. High variability between subjects and low signal-to-noise ratio make it difficult to ensure a good quality in the EEG data. We propose two variational autoencoder models, namely vEEGNet-ver3 and hvEEGNet, to target the problem of high-fidelity EEG reconstruction.
  arXiv  Detail & Related papers  (2023-11-20T15:36:31Z)
• BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning [21.689503325383253]
  We propose the first data-driven study to detect epileptic waves in a real-world SEEG dataset. In clinical practice, epileptic wave activities are considered to propagate between different regions in the brain. The question of how to extract an exact epileptogenic network for each patient remains an open problem in the field of neuroscience.
  arXiv  Detail & Related papers  (2023-06-15T08:29:10Z)
• MP-SeizNet: A Multi-Path CNN Bi-LSTM Network for Seizure-Type Classification Using EEG [2.1915057426589746]
  Seizure type identification is essential for the treatment and management of epileptic patients. We present a novel multi-path seizure-type classification deep learning network (MP-SeizNet) MP-SeizNet consists of a convolutional neural network (CNN) and a bidirectional long short-term memory neural network (Bi-LSTM) with an attention mechanism.
  arXiv  Detail & Related papers  (2022-11-09T01:07:20Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography [51.45515911920534]
  A task-oriented self-supervised learning approach is proposed to train a more effective anomaly detector. A specific two branch convolutional neural network with larger kernels is designed as the feature extractor. The effectively designed and trained feature extractor has shown to be able to extract better feature representations from EEGs.
  arXiv  Detail & Related papers  (2022-07-04T13:15:08Z)
• EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network [0.21756081703275995]
  This paper presents a novel graph convolutional neural network (GCNN)-based approach for improving the diagnosis of neurological diseases using scalp-electroencephalograms (EEGs) We present EEG-GCNN, a novel GCNN model for EEG data that captures both the spatial and functional connectivity between the scalp electrodes. We demonstrate that EEG-GCNN significantly outperforms the human baseline and classical machine learning (ML) baselines, with an AUC of 0.90.
  arXiv  Detail & Related papers  (2020-11-17T20:25:28Z)
• A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition [51.203579838210885]
  We propose a transferable attention neural network (TANN) for EEG emotion recognition. TANN learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively. This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator.
  arXiv  Detail & Related papers  (2020-09-21T02:42:30Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)
• Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the help of Bayesian Uncertainty Modelling [77.34726150561087]
  We propose a statistical method based on the Bayesian approximation error approach to compensate for source imaging errors due to the unknown skull conductivity. Results indicate clear improvements in the source localization accuracy and feasible skull conductivity estimates.
  arXiv  Detail & Related papers  (2020-01-31T21:33:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.