A multi-view deep learning framework for EEG seizure detection achieved an average accuracy of 94.37% and an F1-score of 85.34% compared to nine baseline methods.
Does a multi-view deep learning framework improve EEG seizure detection accuracy compared to baseline methods in epilepsy patients?
A novel multi-view deep learning framework improves automatic EEG seizure detection accuracy to 94.37% compared to baseline methods.
The recent advances in pervasive sensing technologies have enabled us to monitor and analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to prevent serious outcomes caused by epileptic seizures. To avoid manual visual inspection from long-term EEG readings, automatic EEG seizure detection has garnered increasing attention among researchers. In this paper, we present a unified multi-view deep learning framework to capture brain abnormalities associated with seizures based on multi-channel scalp EEG signals. The proposed approach is an end-to-end model that is able to jointly learn multi-view features from both unsupervised multi-channel EEG reconstruction and supervised seizure detection via spectrogram representation. We construct a new autoencoder-based multi-view learning model by incorporating both inter and intra correlations of EEG channels to unleash the power of multi-channel information. By adding a channel-wise competition mechanism in the training phase, we propose a channel-aware seizure detection module to guide our multi-view structure to focus on important and relevant EEG channels. To validate the effectiveness of the proposed framework, extensive experiments against nine baselines, including both traditional handcrafted feature extraction and conventional deep learning methods, are carried out on a benchmark scalp EEG dataset. Experimental results show that the proposed model is able to achieve higher average accuracy and f1-score at 94.37% and 85.34%, respectively, using 5-fold subject-independent cross validation, demonstrating a powerful and effective method in the task of EEG seizure detection.
Yuan et al. (Mon,) conducted a other in Epileptic seizures. Multi-view deep learning framework vs. Nine baseline methods was evaluated on Average accuracy and F1-score for seizure detection. A multi-view deep learning framework for EEG seizure detection achieved an average accuracy of 94.37% and an F1-score of 85.34% compared to nine baseline methods.
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