发表机构
Leiden Institute of Advanced Computer Science; Leiden University（莱顿先进计算机科学研究所; 莱顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对可穿戴和植入设备检测癫痫的难题，以一维卷积神经网络为基线，采用参数转移、通道剪枝、INT8 量化三种策略，实现减少模型大小、降低能耗、加快速度，为癫痫检测提供互补效率方向。
AI 中文摘要
癫痫的连续脑电图监测受可穿戴和植入设备有限的功率和内存预算限制。深度神经网络虽能高精度检测癫痫，但计算成本和模型大小使其难以在此类平台部署。本文以 CHB - MIT 头皮脑电图数据集上的单个一维卷积神经网络癫痫检测器为基线，研究三种受大脑启发的效率策略：通过参数转移将卷积神经网络转换为脉冲神经网络；脑电图通道剪枝结合 2:4 结构化权重稀疏性；使用基于 FX 和 ONNX 的工作流程进行 INT8 量化，包括量化感知训练和算子融合。量化后的卷积神经网络变体减少存储模型大小，降低推理能量消耗，加快 CPU 延迟，同时保留并在一种情况下略微提高 AUC。剪枝后的卷积神经网络减少输入通道和非零权重数量，精度略有下降，脉冲神经网络转换提供具有稀疏时间活动的脉冲实现。这些实验为癫痫检测确定了三个互补的效率方向。
英文摘要
Continuous EEG monitoring for epilepsy is constrained by the limited power and memory budgets of wearable and implantable devices. Deep neural networks can detect seizures with high accuracy, but their computational cost and model size make them difficult to deploy on such platforms. In this work we use a single 1D CNN seizure detector on the CHB-MIT scalp EEG dataset as a common baseline, and then investigate three brain-inspired efficiency strategies: (i) conversion of the CNN into a spiking neural network (SNN) via parameter transfer, (ii) EEG channel pruning combined with 2:4 structured weight sparsity, and (iii) INT8 quantization using FX- and ONNX-based workflows, including quantization-aware training and operator fusion. The quantized CNN variants reduce stored model size from 1.63 MB to 0.44 MB, lower estimated energy per inference by up to 64%, and achieve as much as 2.8 times speedup in CPU latency while preserving, and in one case slightly improving, AUC. The pruned CNN halves the number of input channels and non-zero weights with only a modest accuracy drop, and the SNN conversion provides a spiking implementation with sparse temporal activity. Together, these experiments characterize three complementary efficiency directions for seizure detection.
