发表机构
Khulna University of Engineering & Technology（库尔纳工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出注意力增强双分支ConvNeXt-BiLSTM网络，融合时频与时间特征，在CHB-MIT数据上实现97.88%准确率，实现受试者无关的EEG癫痫自动检测。
AI 中文摘要
从头皮脑电图（EEG）进行自动癫痫检测具有挑战性，因为癫痫形态在不同患者间存在差异，且非癫痫样本数量远超癫痫样本。本文提出一种注意力增强的双分支网络，从同一EEG片段中联合学习时频和时间表示。连续小波变换将每个片段转换为尺度图，由ImageNet预训练的ConvNeXt-Tiny主干和挤压-激励注意力处理。并行地，双向长短期记忆网络后接多头自注意力对原始信号建模。两个特征向量被拼接并由加权多层感知机分类。实验使用CHB-MIT头皮EEG数据库中的14名受试者，在重叠分割之前进行受试者级划分。模型在十次跨受试者划分中获得97.88%的准确率和97.51%的F1分数，在14折留一受试者交叉验证中获得97.51%的准确率、96.59%的F1分数和98.02%的ROC曲线下面积。去除时间注意力导致最大的消融损失。模型需要2826万参数和4.56 GFLOPs，实测网络推理延迟为每片段4.64毫秒。
英文摘要
Automated seizure detection from scalp electroencephalography (EEG) is difficult because seizure morphology varies among patients and seizure samples are substantially outnumbered by non-seizure samples. This paper presents an attention-enhanced dual-branch network that jointly learns time--frequency and temporal representations from the same EEG segment. A continuous wavelet transform converts each segment into a scalogram processed by an ImageNet-pretrained ConvNeXt-Tiny backbone and squeeze-and-excitation attention. In parallel, a bidirectional long short-term memory network followed by multi-head self-attention models the raw signal. The two feature vectors are concatenated and classified by a weighted multilayer perceptron. Experiments use 14 subjects from the CHB-MIT scalp EEG database with subject-wise partitioning performed before overlapping segmentation. The model obtains $97.88\%$ accuracy and $97.51\%$ F1-score over ten across-subject splits, and $97.51\%$ accuracy, $96.59\%$ F1-score, and $98.02\%$ area under the ROC curve under 14-fold leave-one-subject-out validation. Removing temporal attention causes the largest ablation loss. The model requires 28.26 million parameters and 4.56 GFLOPs, with a measured network-only inference latency of 4.64 ms per segment.
CommentsSubmitted to IEEE COMPAS 2026. 5 pages, 2 figures, 5 tables
