Technological Vanguard: the outstanding performance of the LTY-CNN model for the early prediction of epileptic seizures

作者：Yang Yang, Tianyun Luan, Zhangjun Yu, Min Zhang, Fengtian Li, Xing Chen, Fei Gao, Zhijun Zhang · 发表于：Journal of Translational Medicine · 年份：2024 · DOI：10.1186/s12967-024-04945-x · 被引用次数：10 · 研究领域：EEG and Brain-Computer Interfaces、Epilepsy research and treatment、Machine Learning in Healthcare

BACKGROUND: Epilepsy is a common neurological disorder that affects approximately 60 million people worldwide. Characterized by unpredictable neural electrical activity abnormalities, it results in seizures with varying intensity levels. Electroencephalography (EEG), as a crucial technology for monitoring and predicting epileptic seizures, plays an essential role in improving the quality of life for people with epilepsy. METHOD: This study introduces an innovative deep learning model, a lightweight triscale yielding convolutional neural network" (LTY-CNN), that is specifically designed for EEG signal analysis. The model integrates a parallel convolutional structure with a multihead attention mechanism to capture complex EEG signal features across multiple scales and enhance the efficiency achieved when processing time series data. The lightweight design of the LTY-CNN enables it to maintain high performance in environments with limited computational resources while preserving the interpretability and maintainability of the model. RESULTS: In tests conducted on the SWEC-ETHZ and CHB-MIT datasets, the LTY-CNN demonstrated outstanding performance. On the SWEC-ETHZ dataset, the LTY-CNN achieved an accuracy of 99.9%, an area under the receiver operating characteristic curve (AUROC) of 0.99, a sensitivity of 99.9%, and a specificity of 98.8%. Furthermore, on the CHB-MIT dataset, it recorded an accuracy of 99%, an AUROC of 0.932, a sensitivity of 99.1%, and a specificity of 93.2%. T...