Patient specific seizure prediction for time intervals using TQWT and deep learning

Seizures are sudden activations of part or all of the brain, which are the primary symptoms of epilepsy. Epileptic seizures are characterized by their sudden and unpredictable nature and pose significant risks to patients’ daily lives. In many patients with epilepsy, specific symptoms are observed for a short period before a seizure occurs. Research is ongoing to develop technology that can predict seizures by detecting these symptomatic signals. In this paper, we used the CHB-MIT database, which contains more than 150 seizures in approximately 1,000 hours of EEG data collected from 23 children with intractable seizures. Tunable Q-factor Wavelet Transform (TQWT), a specific type of wavelet transform, was applied to the data to predict seizures by identifying inter-ictal and pre-ictal states using a relatively simple deep learning classifier. Each classification was performed individually for each patient, and seizure prediction during a specific period was achieved using k-fold cross-validation, a technique commonly employed in deep learning. By combining the results, the period with the highest probability of seizure occurrence for each patient was designated the specific warning period. Ultimately, it was possible to predict seizures 15 minutes in advance, achieving an average sensitivity of 0.97, an F1 score of 0.90, and a false discovery rate (FDR) of 0.13 for all patients. Additionally, a specific warning period was established for each patient, ranging from a minimum of 2.5 minutes to a maximum of 15 minutes. .