A difference attention ResNet-LSTM network for epileptic seizure detection using EEG signal. (May 2023)

• Record Type: Journal Article
• Authors: Qiu, Xuanjie; Yan, Fang; Liu, Haihong
• Is Part Of: Biomedical signal processing and control. Volume 83(2023)
• Publication Date: 2023-05
• Subjects: Epileptic seizure detection -- Electroencephalogram -- Deep learning -- ResNet -- LSTM; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2023.104652
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Epileptic seizures can affect the patient's physical function and cause irreversible damage to their brain. It is vital to detect epilepsy seizures in time and give patients antiepileptic medical treatment. Hybrid deep learning models, which combine convolutional neural network and recurrent neural network, have better epileptic seizure detection performance as they could simultaneously extract spatial and temporal features. However, the existing hybrid deep learning models still have the following two weaknesses. Firstly, they directly input the raw electroencephalogram signals, where the epilepsy seizure information is limited. Secondly, some characteristic information is extracted in the feature map, distracting the attention of deep learning model. To address these issues, this paper proposes a difference attention ResNet-LSTM network (DARLNet). The proposed model uses a residual neural network (ResNet) and a long short-term memory network (LSTM) to capture spatial correlations and temporal dependencies, respectively. Besides, a difference layer is developed to automatically mine additional epileptic seizure information. Moreover, the channel attention module is introduced to make the model focus on seizure-relevant information. Several groups of experiments are conducted to evaluate the performance of DARLNet based on the Bonn Electroencephalogram dataset, which verifies the superiority of DARLNet on the two-category and five-category epileptic seizure detection tasks. Highlights: DARLNet captures spatial correlations and temporal dependencies by ResNet and LSTM. DARLNet is able to automate and focus on developing information about seizures. Several experiments have demonstrated the superior performance of DARLNet.