An Optimized Spiking Neural Network With Weighted Feature Integration for Epilepsy Seizure Detection Using <scp>EEG</scp> Signal

ABSTRACT Epilepsy becomes the most hazardous neurological disorder affecting humans, and it leads to death if it is not treated on time. When designing the diagnostic model of seizure disease, the input source is requisite. Rather than imaging, the signal recordings are helpful in identifying the disorder. Among these, an EEG signal is accounted for as input and taken in the form of raw data used in the detection process. This EEG is a tool that assists in retrieving the data of neural activities of the brain. Prompted to design an automated model with an intelligent approach, resolving the existing issues or challenges facilitates better treatment for such affected patients. Earlier implemented methods involve classifying the disorder; still, it is burdened with manually crafted features that easily trap it into less performance. Moreover, the lack of generalization and categorization tends to mislead the identification process. Comparatively, deep learning is emerging with beneficial points in the medical industry to forecast human disorders. In today's context, the SNN acts as a pivotal role in seizure classification with spiking units. So, to tackle these issues, an automated SNN‐based epilepsy seizure detection model is implemented in this proposal. The goal of this research is to develop an efficient SNN model for epileptic seizure detection using EEGs, a complicated pattern recognition problem. At first, from the benchmark data sources, the EEG signals are garnered for subsequent processes and given to the feature extraction stage. In this stage, the temporal features are extracted from the RNN and the spatial features are extracted from the CNN. Moreover, the Path Signature features and the wave features are also extracted from the given EEG signals. Further, these features are fused with the support of optimal weights. Here, the IBBROA is utilized for optimizing the weights. Due to the functional similarity of biological activity, the Spike Network or STDP is considered during training of the network. So, the weighted fused features are subjected to ASNN‐LIF for detecting the seizure disorder. Here also, the same IBBROA algorithm is supported for optimizing the parameters in ASNN‐LIF. At last, a detailed research study is conducted to verify the effectiveness of the implemented epilepsy seizure detection model by contrasting numerous conventional detection techniques and optimization algorithms.