Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs

TL;DR

This paper introduces a hybrid CNN and factor graph-based algorithm for efficient, real-time epileptic seizure detection that outperforms previous methods in accuracy and computational efficiency.

Contribution

It presents a novel hybrid model combining CNNs with factor graph inference, achieving high accuracy and low complexity for seizure detection.

Findings

Achieves up to 5% absolute performance improvement over previous methods.

Demonstrates strong generalization on the CHB-MIT dataset with leave-4-patientout evaluation.

Reduces computational complexity, enabling real-time application.

Abstract

We propose a computationally efficient algorithm for seizure detection. Instead of using a purely data-driven approach, we develop a hybrid model-based/data-driven method, combining convolutional neural networks with factor graph inference. On the CHB-MIT dataset, we demonstrate that the proposed method can generalize well in a 6 fold leave-4-patientout evaluation. Moreover, it is shown that our algorithm can achieve as much as 5% absolute improvement in performance compared to previous data-driven methods. This is achieved while the computational complexity of the proposed technique is a fraction of the complexity of prior work, making it suitable for real-time seizure detection.