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

Journal: Nan fang yi ke da xue xue bao = Journal of Southern Medical University

Published Date: Sep 20, 2024

Abstract

OBJECTIVE: We propose an autoencoder model based on a one-dimensional convolutional neural network (1DCNN) as the feature extraction network for efficient detection of epileptic EEG anomalies.

Authors

J Ou

School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
C Zhan

School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
F Yang

Department of Radiation Oncology, University of Washington Medical Center, Seattle, Washington 98195.

Keywords

Algorithms Electroencephalography Epilepsy Humans Neural Networks, Computer ROC Curve Seizures Signal Processing, Computer-Assisted

External Resources

View on PubMed Access via DOI PubMed (39505348)

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

Abstract

Authors

Keywords

External Resources

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