Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism

2025-01-17

AI Summary

To address automatic classification of epileptic seizure types, this paper proposes an end-to-end model integrating a one-dimensional convolutional neural network (1D-CNN) with a multi-head self-attention mechanism—marking the first deep synergistic integration of these architectures for temporal modeling. The 1D-CNN efficiently extracts local time-frequency features from electroencephalogram (EEG) signals, while the multi-head attention mechanism captures long-range temporal dependencies, substantially enhancing discriminative capability for non-stationary EEG data. Sliding-window preprocessing is employed to preserve temporal continuity. Evaluated on the CHB-MIT scalp EEG dataset, the model achieves a classification accuracy of 99.2%, outperforming conventional CNN- and RNN-based baselines by over 3.5 percentage points. With an inference latency of less than 80 ms per sample, the framework satisfies clinical requirements for real-time auxiliary diagnosis.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Epilepsy is a prevalent neurological disorder globally, impacting around 50 million people cite{WHO_epilepsy_50million}. Epileptic seizures result from sudden abnormal electrical activity in the brain, which can be read as sudden and significant changes in the EEG signal of the brain. The signal can vary in severity and frequency, which results in loss of consciousness and muscle contractions for a short period of time cite{epilepsyfoundation_myoclonic}. Individuals with epilepsy often face significant employment challenges due to safety concerns in certain work environments. Many jobs that involve working at heights, operating heavy machinery, or in other potentially hazardous settings may be restricted for people with seizure disorders. This certainly limits job options and economic opportunities for those living with epilepsy.

Problem

• Epileptic Seizure Classification
• Clinical Diagnosis Assistance
• Quality of Life Improvement

Innovation

• Hybrid Supercomputer Learning Model
• 1D-CNN
• Multiview Approach