A High-Accuracy and Energy-Efficient Zero-Shot-Retraining Seizure-Detection Processor with Hybrid-Feature-Driven Adaptive Processing and Learning-Based Adaptive Channel Selection

📋 论文概要

本文提出了一种高精度、高能效的零样本重训练癫痫检测处理器，通过混合特征驱动自适应机制解决了传统方法需要大量患者数据的问题，实现了无需患者历史数据即可准确检测癫痫发作。

💡 主要创新点

• 提出零样本重训练方法，无需患者历史数据即可实现高精度癫痫检测，大幅降低数据收集成本。
• 采用混合特征驱动自适应机制，动态调整特征提取和分类策略，在保持高准确率的同时提升能效。

🏷 关键词

📄 原文摘要

Yiming Xu1, Huajing Qin1, Yu Long1, Yuhong Zhou2, Zixuan Shen3, Liang Zhou1, Liang Chang1, Shanshan Liu1, Shuisheng Lin1, Chao Wang3, Jun Zhou1 University of Electronic Science and Technology of China, Chengdu, China West China Hospital of Sichuan University, Chengdu, China 3 Huazhong University of Science and Technology, Wuhan, China 1 2 Seizure-detection processors using machine learning have been proposed to detect the seizure onset of patients for alert or stimulation purposes [1-4]. Existing designs can achieve high accuracy when large amounts of seizure data from a patient is available for the training. However, unlike the collection of non-seizure data, the collection of seizure data with low occurrence requires patients to undergo time-consuming and costly hospitalization, which is difficult in practice. To address this issue, [5] proposed a zeroshot-retraining seizure-detection processor achieving relatively high accuracy without

👥 作者与机构

Jiahao Liu1, Xiao Liu1, Xu Wang1, Ziyi Xie1, Zirui Zhong1, Jiajing Fan1, Hui Qiu1,