LMPSeizNet: A Lightweight Multiscale Pyramid Convolutional Neural Network for Epileptic Seizure Detection on EEG Brain Signals

Epilepsy is a chronic disease and one of the most common neurological disorders worldwide. Electroencephalogram (EEG) signals are widely used to detect epileptic seizures, which provide specialists with essential information about the brain’s functioning. However, manual screening of EEG signals is laborious, time-consuming, and subjective. The rapid detection of epilepsy seizures is important to reduce the risk of seizure-related implications. The existing automatic machine learning techniques based on deep learning techniques are characterized by automatic extraction and selection of the features, leading to better performance and increasing the robustness of the systems. These methods do not consider the multiscale nature of EEG signals, eventually resulting in poor sensitivity. In addition, the complexity of deep models is relatively high, leading to overfitting issues. To overcome these problems, we proposed an efficient and lightweight multiscale convolutional neural network model (LMPSeizNet), which performs multiscale temporal and spatial analysis of an EEG trial to learn discriminative features relevant to epileptic seizure detection. To evaluate the proposed method, we employed 10-fold cross-validation and three evaluation metrics: accuracy, sensitivity, and specificity. The method achieved an accuracy of 97.42%, a sensitivity of 99.33%, and a specificity of 96.51% for inter-ictal and ictal classes outperforming the state-of-the-art methods. The analysis of the features and the decision-making of the method shows that it learns the features that clearly discriminate the two classes. It will serve as a useful tool for helping neurologists and epilepsy patients.