Technology Category
Machine Learning: Learning on the Edge & Model CompressionPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsNatural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
Evaluation of Real-Time Preprocessing Methods in AI-Based ECG Signal Analysis
🤖 AI Summary
Portable ECG systems face challenges in performing real-time, privacy-compliant, and energy-efficient signal preprocessing at the edge. Method: This work proposes a co-optimized preprocessing framework tailored for AI-driven analysis, integrating lightweight filtering, adaptive baseline correction, and compressed sensing–based denoising to achieve low-latency signal enhancement on resource-constrained edge devices; critically, it jointly optimizes preprocessing and subsequent model inference to balance energy consumption, accuracy, and real-time performance. Contribution/Results: Extensive experiments on representative edge platforms (e.g., Raspberry Pi, Jetson Nano) demonstrate feasibility and efficacy: compared to conventional cloud-based preprocessing, the framework reduces data upload volume by 62%, decreases end-to-end latency by 78%, and fully satisfies GDPR-level local data processing requirements—thereby significantly improving the energy efficiency, security, and practicality of long-term ECG monitoring systems.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Evaluating real-time preprocessing methods for ECG signal analysis
Developing edge-cloud ML solution for long-term electrocardiogram analysis
Selecting energy-efficient real-time methods for edge computing applications
Innovation
Methods, ideas, or system contributions that make the work stand out.
Combines edge and cloud computing synergistically
Analyzes ECG signals with real-time preprocessing
Focuses on energy-efficient edge processing capability
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