Key result
The multi-frame network model achieved an accuracy of 80.96% and an AUC of 0.87 on the CHB-MIT dataset, outperforming the CNN-LSTM model in predicting epileptic seizures.
Why the study?
Existing deep learning seizure prediction models rely on complicated pre-processing strategies, cannot directly process raw data in real time, and suffer from poor generalization due to single-frame limitations.
Absolute Event Rate: 80.96% vs 75.32%
A multi-frame network combining CNN and LSTM extracts both spatial and temporal features from raw EEG data, improving seizure prediction accuracy over single-frame models.
Current DL seizure models require complex pre-processing; leaves open feasibility of raw-EEG prediction for clinical use.
Introduction: Analysis and prediction of seizures by processing the EEG signals could assist doctors in accurate diagnosis and improve the quality of the patient's life with epilepsy. Nowadays, seizure prediction models based on deep learning have become one of the most popular topics in seizure studies, and many models have been presented. However, the prediction results are strongly related to the various complicated pre-processing strategies of models, and cannot be directly applied to raw data in real-time applications. Moreover, due to the inherent deficiencies in single-frame models and the non-stationary nature of EEG signals, the generalization ability of the existing model frameworks is generally poor. Methods: Therefore, we proposed an end-to-end seizure prediction model in this paper, where we designed a multi-frame network for automatic feature extraction and classification. Instance and sequence-based frames are proposed in our approach, which can help us simultaneously extract features of different modes for further classification. Moreover, complicated pre-processing steps are not included in our model, and the novel frames can be directly applied to the raw data. It should be noted that the approaches proposed in the paper can be easily used as the general model which has been validated and compared with existing model frames. Results: The experimental results showed that the multi-frame network proposed in this paper was superior to the existing model frame in accuracy, sensitivity, specificity, F1-score, and AUC in the classification performance of EEG signals. Discussion: Our results provided a new research idea for this field. Researchers can further integrate the idea of the multi-frame network into the state-of-the-art single-frame seizure prediction models and then achieve better results.
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Lu et al. (2022) studied Epilepsy (n=23). Multi-frame network model vs. CNN-LSTM model was evaluated on Accuracy of seizure prediction on the CHB-MIT dataset. The multi-frame network model achieved an accuracy of 80.96% and an AUC of 0.87 on the CHB-MIT dataset, outperforming the CNN-LSTM model in predicting epileptic seizures.
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