发表机构
Magnes AG; School of Engineering, Physics and Mathematics, Northumbria University（Magnes AG; 诺森比亚大学工程、物理与数学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于小波模板和数据驱动提取的通用方法，用于从单通道含噪信号中去除已知形状的准周期性伪影，在神经病学应用中相比基线中位RMSE改善33%，优于自编码器且无需训练数据。
AI 中文摘要
本文提出了一种从单通道测量中估计和去除准周期性、类伪影干扰的通用方法。该方法基于小波模板和数据驱动的模板提取技术，从单通道含噪信号中提取模板。该方法在现代神经病学的一个示例应用中进行了测试，并与在理想条件下训练和部署的自编码器（作为参考系统）进行了比较。研究发现，与基线相比，所提出的方法产生的信号估计中位均方根误差改善了33%，比自编码器高出4%，同时依赖更少的假设和参数，且无需任何训练数据。
英文摘要
A general method for the estimation and removal of quasi-periodic, artifact-like disturbances from single channel measurements is presented. The method is based on a wavelet template and data-driven template extraction from single channel, noisy signals. The method is tested on an example application in modern neurology. The method is compared to an autoencoder, trained and deployed under idealized conditions, thus acting as reference system. It is found that the proposed method yields signal estimates with median root mean squared error improvement of 33% compared to the baseline, which is 4% more than the autoencoder, while relying on fewer assumptions and parameters, and without the need for any training data.
CommentsSubmitted to Healthcare Technology Letters
