Abstract：
Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models are often complex and time-consuming, highlighting the urgent need for lightweight seizure detection. Additionally, existing methods often neglect the key characteristic channels and spatial regions of electroencephalography (EEG) signals. To solve these issues, we propose a lightweight EEG-based seizure detection model named lightweight inverted residual attention network (LRAN). Specifically, we employ a four-stage inverted residual mobile block (iRMB) to effectively extract the hierarchical features from EEG. The convolutional block attention module (CBAM) is introduced to make the model focus on important feature channels and spatial information, thereby enhancing the discrimination of the learned features. Finally, convolution operations are used to capture local information and spatial relationships between features. We conduct intra-subject and inter-subject experiments on a publicly available dataset. Intra-subject experiments obtain 99.25% accuracy in segment-based detection and 0.36/h false detection rate (FDR) in event-based detection, respectively. Inter-subject experiments obtain 84.32% accuracy. Both sets of experiments maintain high classification accuracy with a low number of parameters, where the multiply accumulate operations (MACs) are 25.86[Formula: see text]M and the number of parameters is 0.57[Formula: see text]M.
摘要：
及时准确的癫痫发作检测对癫痫患者的诊断和治疗具有重要意义。现有的癫痫发作检测模型通常复杂且耗时，突出了轻量级癫痫检测的迫切需要。此外,现有的方法往往忽略了脑电图(EEG)信号的关键特征通道和空间区域。为了解决这些问题,我们提出了一种轻量级的基于脑电图的癫痫发作检测模型，称为轻量级的反向残余注意网络（LRAN）。具体来说,我们采用四级倒置残差移动块(iRMB)有效地从脑电图中提取层次特征.引入卷积块注意力模块(CBAM),使模型集中于重要的特征通道和空间信息,从而增强对学习特征的辨别。最后,卷积运算用于捕获局部信息和特征之间的空间关系。我们在公开可用的数据集上进行受试者内和受试者间的实验。受试者内部实验在基于片段的检测中获得99.25％的准确率，在基于事件的检测中获得0.36/h的误检率（FDR），分别。受试者间实验获得84.32%的准确率。两组实验都以较低的参数数量保持较高的分类精度，其中乘法累加运算（MAC）为25.86[公式：请参见文本]M，参数数量为0.57[公式：请参见文本]M。
