Neonatal seizure detection from minimized EEG channels using polynomial compact support distribution and VGG19-BiLSTM hybrid model

Neonatal seizures pose a significant risk to infants and can have severe consequences if not detected and treated promptly. Electroencephalography (EEG) recordings are commonly used for monitoring and analyzing brain activity in newborns suffering from seizures. However, visual inspection of EEG signals can be error-prone and time-consuming, which can delay intervention. Deep learning algorithms, such as CNNs, have shown promise in classifying EEG signals for neonatal seizure detection. However, the multidimensionality of EEG recordings presents a challenge for using time-frequency representations (TFRs) as input for deep learning models. To address this challenge, recent research has explored alternative approaches, such as using a subset of the EEG channels that convey essential information about the brain's activity. In this context, this paper proposes a novel approach for neonatal seizure detection using a minimized number of EEG channels and a TFD based on kernels with compact support. The proposed method employs a VGG19-BiLSTM mixed model to extract spatial and temporal features from the input data. We evaluate the effectiveness of the proposed approach on a publicly available Helsinki dataset. The obtained results demonstrate that this approach has the potential to significantly enhance the precision and expediency of neonatal seizure detection, potentially leading to more effective diagnosis and treatment outcomes.