对比多种实时心电图预处理方法，筛选适合边缘计算的高效方案。
Evaluation of Real-Time Preprocessing Methods in AI-Based ECG Signal Analysis
- 从能耗、实时性等维度评估边缘计算适用的预处理方法。
- 提出适用于长期心电图分析的轻量化预处理流程设计。
- 为隐私保护与低延迟的医疗AI系统提供技术选型参考。
便携式心电图设备的普及和对隐私合规、低功耗实时分析的需求，推动了数据采集端信号处理新方法的发展。在此背景下，边缘计算的重要性日益凸显，不仅降低延迟，还提升数据安全性。FACE项目旨在开发一种结合边缘与云计算优势的创新机器学习解决方案，用于分析长期心电图。本文针对该项目的需要，系统评估了多种心电图信号预处理步骤在边缘环境下的适用性。方法选择重点依据能量效率、处理能力及实时处理性能等指标。
原文摘要 · Abstract (English)
The increasing popularity of portable ECG systems and the growing demand for privacy-compliant, energy-efficient real-time analysis require new approaches to signal processing at the point of data acquisition. In this context, the edge domain is acquiring increasing importance, as it not only reduces latency times, but also enables an increased level of data security. The FACE project aims to develop an innovative machine learning solution for analysing long-term electrocardiograms that synergistically combines the strengths of edge and cloud computing. In this thesis, various pre-processing steps of ECG signals are analysed with regard to their applicability in the project. The selection of suitable methods in the edge area is based in particular on criteria such as energy efficiency, processing capability and real-time capability.
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