Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

• Published in: Multimedia tools and applications Vol. 83; no. 33; pp. 80201 - 80223
• Main Authors: Dutta, Ashit Kumar, Raparthi, Mohan, Alsaadi, Mahmood, Bhatt, Mohammed Wasim, Dodda, Sarath Babu, C., Prashant G., Sandhu, Mukta, Patni, Jagdish Chandra
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2024; Springer Nature B.V
• Subjects: 1238: Recent Advances in Biometrics Based on Biomedical Information; Accuracy; Algorithms; Computer Communication Networks; Computer Science; Convulsions & seizures; Data Structures and Information Theory; Deep learning; Electroencephalography; Epilepsy; Machine learning; Multimedia Information Systems; Pattern recognition; Seizures; Signal detection; Special Purpose and Application-Based Systems; Bidirectional Long Short-Term Memory Network; Seizure Disorder; Biomedical traits; Electroencephalogram; Self-Attention Mechanism; Seizure Patterns
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-18918-1

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection.