Seizure Detection Based on Lightweight Inverted Residual Attention Network.
Int J Neural Syst
; 34(8): 2450042, 2024 Aug.
Article
in En
| MEDLINE
| ID: mdl-38818805
ABSTRACT
Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models are often complex and time-consuming, highlighting the urgent need for lightweight seizure detection. Additionally, existing methods often neglect the key characteristic channels and spatial regions of electroencephalography (EEG) signals. To solve these issues, we propose a lightweight EEG-based seizure detection model named lightweight inverted residual attention network (LRAN). Specifically, we employ a four-stage inverted residual mobile block (iRMB) to effectively extract the hierarchical features from EEG. The convolutional block attention module (CBAM) is introduced to make the model focus on important feature channels and spatial information, thereby enhancing the discrimination of the learned features. Finally, convolution operations are used to capture local information and spatial relationships between features. We conduct intra-subject and inter-subject experiments on a publicly available dataset. Intra-subject experiments obtain 99.25% accuracy in segment-based detection and 0.36/h false detection rate (FDR) in event-based detection, respectively. Inter-subject experiments obtain 84.32% accuracy. Both sets of experiments maintain high classification accuracy with a low number of parameters, where the multiply accumulate operations (MACs) are 25.86[Formula see text]M and the number of parameters is 0.57[Formula see text]M.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Seizures
/
Neural Networks, Computer
/
Electroencephalography
Limits:
Humans
Language:
En
Journal:
Int J Neural Syst
/
Int. j. neural syst
/
International journal of neural systems
Journal subject:
ENGENHARIA BIOMEDICA
/
INFORMATICA MEDICA
Year:
2024
Document type:
Article
Country of publication:
Singapur