# CPRSCA-ResNet: a novel ResNet-based model with Channel-Partitioned Resolution Spatial-Channel Attention for EEG-based seizure detection

## TL;DR

This paper introduces a new deep learning model for detecting seizures from EEG data, which improves accuracy and generalizability compared to existing methods.

## Contribution

The CPRSCA mechanism enhances ResNet-34 with channel-partitioned spatial-channel attention for more precise EEG feature representation.

## Key findings

- The CPRSCA-ResNet model achieved seizure detection accuracies up to 99.12% on public and hospital datasets.
- The model outperformed state-of-the-art baselines in both patient-dependent and patient-independent experiments.
- The CPRSCA mechanism improves generalizability and robustness for automated seizure detection.

## Abstract

Epilepsy is a common chronic neurological disorder caused by abnormal discharges of brain neurons, characterized by transient disturbances in consciousness, motor function, behavior, or sensation. Recurrent seizures severely impair patients’ cognitive and physiological functions and increase the risk of accidental injury and premature death. Currently, clinical diagnosis of epilepsy mainly relies on manual interpretation of electroencephalogram (EEG) recordings, but traditional methods are time-consuming, labor-intensive, and susceptible to noise interference, highlighting the urgent need for efficient and accurate automated detection models. To address this, a novel Channel-Partitioned Resolution Spatial-Channel Attention (CPRSCA) mechanism was proposed in this study, and a CPRSCA-ResNet automatic seizure detection model was developed based on the ResNet-34 architecture. By incorporating fine-grained channel partitioning, multi-scale feature fusion, and multi-dimensional attention mechanisms, the proposed approach significantly enhances the precise representation of complex EEG features. Patient-dependent and patient-independent seizure detection experiments were conducted on the public CHB-MIT dataset and two local hospital datasets (JHCH and JHMCHH). The results show that, in patient-dependent experiments, the proposed model achieved accuracies of 99.12 ± 2.09%, 96.88 ± 4.64%, and 98.84 ± 1.75% on the three datasets, while in patient-independent experiments, accuracies reached 78.71 ± 13.06%, 87.15 ± 15.32%, and 89.23 ± 7.87%, respectively. These metrics consistently outperform state-of-the-art baselines, confirming the effectiveness and generalizability of the CPRSCA mechanism for automatic seizure detection. In summary, the proposed method provides an efficient, robust, and highly generalizable technical solution for auxiliary clinical diagnosis of epilepsy, with the potential to substantially reduce the burden of manual EEG interpretation and improve the diagnostic efficiency for patients with epilepsy.