Seizure Detection of EEG Signals Based on Multi-Channel Long- and Short-Term Memory-Like Spiking Neural Model.
Int J Neural Syst
; 34(10): 2450051, 2024 Oct.
Article
in En
| MEDLINE
| ID: mdl-39004932
ABSTRACT
Seizure is a common neurological disorder that usually manifests itself in recurring seizure, and these seizures can have a serious impact on a person's life and health. Therefore, early detection and diagnosis of seizure is crucial. In order to improve the efficiency of early detection and diagnosis of seizure, this paper proposes a new seizure detection method, which is based on discrete wavelet transform (DWT) and multi-channel long- and short-term memory-like spiking neural P (LSTM-SNP) model. First, the signal is decomposed into 5 levels by using DWT transform to obtain the features of the components at different frequencies, and a series of time-frequency features in wavelet coefficients are extracted. Then, these different features are used to train a multi-channel LSTM-SNP model and perform seizure detection. The proposed method achieves a high seizure detection accuracy on the CHB-MIT dataset 98.25% accuracy, 98.22% specificity and 97.59% sensitivity. This indicates that the proposed epilepsy detection method can show competitive detection performance.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Seizures
/
Neural Networks, Computer
/
Electroencephalography
/
Wavelet Analysis
Limits:
Humans
Language:
En
Journal:
Int J Neural Syst
Journal subject:
ENGENHARIA BIOMEDICA
/
INFORMATICA MEDICA
Year:
2024
Document type:
Article