[An autoencoder model based on one-dimensional neural network for epileptic EEG anomaly detection].

The autoencoder model based one-dimensional convolutional neural network can effectively detect abnormal EEG signals in epileptic seizure.

Authors

Jianjun Ou (ORCID: https://orcid.org/0000-0002-0611-7715)
Chang’an A. Zhan (ORCID: https://orcid.org/0000-0001-8017-1886)
Fangyuan Yang (ORCID: https://orcid.org/0000-0003-2496-7446)

Institutions

Southern Medical University (CN)

Publication Details

Journal: PubMed
Published: 2024-09-20
DOI: https://doi.org/10.12122/j.issn.1673-4254.2024.09.20
Primary Topic: Brain Tumor Detection and Classification
Type: article

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article

[An autoencoder model based on one-dimensional neural network for epileptic EEG anomaly detection].

Jianjun Ou, Chang’an A. Zhan, Fangyuan Yang

Sep 20, 2024

PubMed

Brain Tumor Detection and Classification

article

[An autoencoder model based on one-dimensional neural network for epileptic EEG anomaly detection].

Jianjun Ou, Chang’an A. Zhan, Fangyuan Yang

September 20, 2024article en

Abstract

The autoencoder model based one-dimensional convolutional neural network can effectively detect abnormal EEG signals in epileptic seizure.

PubMedVol. 44(9)

Southern Medical University (CN)

Brain Tumor Detection and Classification

DOI Open Access PDF OpenAlex

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[An autoencoder model based on one-dimensional neural network for epileptic EEG anomaly detection]. — Jianjun Ou, Chang’an A. Zhan, et al. · PubMed (2024) | TGRS Research Map | TGRS