Construction of an epileptic seizure prediction model using a semi-supervised method of generative adversarial and long short term memory network combined with Stockwell transform.
10.12122/j.issn.1673-4254.2023.01.03
- Author:
Jia Hui LIAO
1
;
Ha Yi LI
1
;
Chang An ZHAN
1
;
Feng YANG
1
Author Information
1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
- Publication Type:Journal Article
- Keywords:
Stockwell transform;
bi-directional long short term memory network;
epileptic seizure prediction;
generative adversarial network;
scalp EEG
- MeSH:
Humans;
Memory, Short-Term;
Seizures/diagnosis*;
Electroencephalography
- From:
Journal of Southern Medical University
2023;43(1):17-28
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To propose a semi-supervised epileptic seizure prediction model (ST-WGAN-GP-Bi-LSTM) to enhance the prediction performance by improving time-frequency analysis of electroencephalogram (EEG) signals, enhancing the stability of the unsupervised feature learning model and improving the design of back-end classifier.
METHODS:Stockwell transform (ST) of the epileptic EEG signals was performed to locate the time-frequency information by adaptive adjustment of the resolution and retaining the absolute phase to obtain the time-frequency inputs. When there was no overlap between the generated data distribution and the real EEG data distribution, to avoid failure of feature learning due to a constant JS divergence, Wasserstein GAN was used as a feature learning model, and the cost function based on EM distance and gradient penalty strategy was adopted to constrain the unsupervised training process to allow the generation of a high-order feature extractor. A temporal prediction model was finally constructed based on a bi-directional long short term memory network (Bi-LSTM), and the classification performance was improved by obtaining the temporal correlation between high-order time-frequency features. The CHB-MIT scalp EEG dataset was used to validate the proposed patient-specific seizure prediction method.
RESULTS:The AUC, sensitivity, and specificity of the proposed method reached 90.40%, 83.62%, and 86.69%, respectively. Compared with the existing semi-supervised methods, the propose method improved the original performance by 17.77%, 15.41%, and 53.66%. The performance of this method was comparable to that of a supervised prediction model based on CNN.
CONCLUSION:The utilization of ST, WGAN-GP, and Bi-LSTM effectively improves the prediction performance of the semi-supervised deep learning model, which can be used for optimization of unsupervised feature extraction in epileptic seizure prediction.
