MCAN: Cross-domain self-supervised attention network based on multiscale EEG feature learning for epileptic seizure detection by Tingxuan Hong instant download

MCAN: Cross-domain self-supervised attention network based on multiscale EEG feature learning for epileptic seizure detection by Tingxuan Hong instant download

Epilepsy is a common neurological disorder that severely affects patient safety and quality of life. Electroencephalography (EEG) is crucial for detecting epileptic seizures. However, the manual annotation of seizuresduring long-term EEG monitoring is labor-intensive, error-prone, and highly dependent on clinical expertise.Furthermore, real-world EEG-based seizure detection is also challenging due to distribution shifts across subjectsand datasets, along with the significant class imbalance between seizure and normal segments. To address thesechallenges, we propose a cross-domain hybrid self-supervised attention network (MCAN) for the automatic detection of seizures. The network offers the following key contributions: Firstly, a cross-domain hybrid self-supervisedlearning strategy is designed to capture the temporal dynamics of EEG signals while simultaneously preservingthe spatial distributions across electrodes and the spectral characteristics of neural oscillations. Secondly, a multiscale feature learning module is developed to model the hierarchical spatiotemporal dynamics of EEG signalsthrough diverse receptive fields, thereby reducing the model’s dependency on subject-specific features. Thirdly,we propose a self-attention mechanism guided by a sparse electrode adjacency matrix to effectively captureseizure-related neuronal synchrony. Extensive experiments were conducted using real-world clinical epilepsymonitoring datasets and three publicly available datasets to evaluate the performance of our MCAN. The resultsdemonstrate that MCAN consistently outperforms baseline methods in seizure detection across multiple datasets.Notably, it achieves an area under the receiver operating characteristic curve of 0.914 and an F1-score of 0.709,highlighting its potential for seizure monitoring applications.