M-NIG: mobile network information gain for EEG-based epileptic seizure prediction.

Epilepsy is one of the most common cerebral diseases, the development of which can be divided into four states: interictal state, preictal state, ictal state and postictal state. Hunting for critical states is of great significance to predict seizures. This study seeks to establish a general-purpose method for epileptic seizure prediction by constructing individual-specific correlation networks between multi-channel EEG signals. In this paper, we present the mobile network information gain (M-NIG) method by transforming floating time series datasets into stable network information gain, which reduces the impact of data noise, thereby improving the robustness and effectiveness of the algorithm. The method not only efficiently predicts seizures but also detects their DNB channels. The proposed method attains an average of 97.40% accuracy, 94.32% sensitivity, 97.48% specificity, and FPR = 0.024/h on 22 patients from the public CHB-MIT scalp EEG database, which outperforms most state-of-the-art articles. Additionally, it achieves an average of 95.70% accuracy, 100.00% sensitivity, 95.56% specificity, and FPR = 0.044/h on a dataset collected at Taian Maternity and Child Health Hospital, which outperforms most state-of-the-art articles in terms of sensitivity, accuracy, and FPR. Our experiments show that the parameters of sliding window and the number of nearest neighbor of k-nearest neighbor (KNN) are important factors affecting prediction performance.