SeizureNet-BiLSTM: A Hybrid Deep Learning Framework for Identifying Ictal and Interictal Phases

A neurological condition called epilepsy is characterized by intermittent, frequent seizures. Traditionally, seizure identification has depended on neurologists’ laborious visual examination of electroencephalogram (EEG) data. This article introduces a novel seizure detection model, SeizureNet-BiLSTM (Bidirectional Long Short-Term Memory), which integrates deep convolutional networks (ResNet-like structures) and bidirectional LSTMs to detect seizures across both ictal and inter-ictal phases. The process begins by extracting ictal and inter-ictal segments from raw multichannel EEG data, followed by feature extraction using Mean, Skewness, Kurtosis, Short-Time Fourier Transform (STFT), and Power-Spectral-Density (PSD). These features are then input into the suggested model for seizure detection. Experimental results validate a test accuracy of 99.66%, sensitivity of 98.49%, specificity of 98.49%, a false positive rate (FPR) of 0.02107, and F1-score of 97.94%. Tested on the public CHBMIT (Children’s Hospital Boston - Massachusetts Institute of Technology) dataset, the SeizureNet-BiLSTM proves to be a computationally efficient and effective approach for automated seizure detection.