Automatic Seizure Detection and Prediction Based on Brain Connectivity Features and a CNNs Meet Transformers Classifier

• Published in: Brain sciences Vol. 13; no. 5; p. 820
• Main Authors: Tian, Ziwei, Hu, Bingliang, Si, Yang, Wang, Quan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.05.2023; MDPI
• Subjects: Accuracy; brain connectivity; Classification; CNNs meet transformers; Computational linguistics; Convulsions & seizures; EEG; Efficiency; Electric transformers; Electroencephalography; Epilepsy; epileptic state classification; Language processing; Natural language interfaces; Nervous system diseases; Neural networks; Physiology; Predictions; Seizures; Seizures (Medicine); support vector machine; China; brain connectivity; CNNs meet transformers; support vector machine; EEG; epileptic state classification
• ISSN: 2076-3425; 2076-3425
• DOI: 10.3390/brainsci13050820

(1) Background: Epilepsy is a neurological disorder that causes repeated seizures. Since electroencephalogram (EEG) patterns differ in different states (inter-ictal, pre-ictal, and ictal), a seizure can be detected and predicted by extracting various features. However, the brain connectivity network, a two-dimensional feature, is rarely studied. We aim to investigate its effectiveness for seizure detection and prediction. (2) Methods: Two time-window lengths, five frequency bands, and five connectivity measures were used to extract image-like features, which were fed into a support vector machine for the subject-specific model (SSM) and a convolutional neural networks meet transformers (CMT) classifier for the subject-independent model (SIM) and cross-subject model (CSM). Finally, feature selection and efficiency analyses were conducted. (3) Results: The classification results on the CHB-MIT dataset showed that a long window indicated better performance. The best detection accuracies of SSM, SIM, and CSM were 100.00, 99.98, and 99.27%, respectively. The highest prediction accuracies were 99.72, 99.38, and 86.17%, respectively. In addition, Pearson Correlation Coefficient and Phase Lock Value connectivity in the β and γ bands showed good performance and high efficiency. (4) Conclusions: The proposed brain connectivity features showed good reliability and practical value for automatic seizure detection and prediction, which expects to develop portable real-time monitoring equipment.