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.

翻译：随着便携式心电图系统的日益普及以及对符合隐私保护、高能效实时分析需求的不断增长，需要在数据采集端采用新的信号处理方法。在此背景下，边缘计算领域正变得愈发重要，因为它不仅能降低延迟时间，还能提升数据安全水平。FACE项目旨在开发一种创新的机器学习解决方案，用于分析长期心电图，该方案协同结合了边缘计算与云计算的优势。本论文针对项目中各种心电图信号预处理步骤的适用性进行了分析。在边缘计算领域选择合适的方法时，特别基于能效、处理能力和实时性等标准。