Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer

Huang, Leen1; Zhou, Keying2,3,4; Chen, Siyang1; Chen, Yanzhao2,3,4; Zhang, Jinxin1

2024-06-01

10.1186/s12938-024-01244-w

BIOMEDICAL ENGINEERING ONLINE   影响因子和分区

1475-925X

Background Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios.Method To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection.Results The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy.Conclusion The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.

Convolutional neural network EEG Epileptic seizure Pediatric epilepsy Attention mechanism

SCI

英语

其他

Natural Science Foundation of Guangdong Province, China[2022A1515011237]

Engineering

Engineering, Biomedical

WOS:001236549200001

BMC

Web of Science

1.Sun Yat Sen Univ, Sch Publ Hlth, Dept Med Stat, Guangzhou 510080, Guangdong, Peoples R China
2.Shenzhen Peoples Hosp, Dept Pediat, Shenzhen 518020, Guangdong, Peoples R China
3.Jinan Univ, Clin Med Coll 2, Dept Pediat, Shenzhen 518020, Guangdong, Peoples R China
4.Southern Univ Sci & Technol, Affiliated Hosp 1, Dept Pediat, Shenzhen 518020, Guangdong, Peoples R China

Huang, Leen,Zhou, Keying,Chen, Siyang,et al. Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer[J]. BIOMEDICAL ENGINEERING ONLINE,2024,23(1).

Huang, Leen,Zhou, Keying,Chen, Siyang,Chen, Yanzhao,&Zhang, Jinxin.(2024).Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer.BIOMEDICAL ENGINEERING ONLINE,23(1).

Huang, Leen,et al."Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer".BIOMEDICAL ENGINEERING ONLINE 23.1(2024).