Why the study?
Continuous monitoring of cardiac patients in general hospital wards remains challenging due to manual charting systems and slow clinical responses to physiological deterioration.
Does an edge- and fog-based IoT healthcare system improve ECG classification precision and reduce alert latency in cardiac patients in hospital wards?
Population
PhysioNet datasets and patients in real wards
Comparison
Edge- and fog-based IoT monitoring system vs conventional threshold and manual charting systems
Design
System development and validation study
Key result
Edge-centric IoT system achieved 91.96% ECG classification accuracy and reduced patient evaluation time to 15.23 ± 2.71 seconds in real hospital wards.
Authors
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Supports rapid response in hospitalized cardiac patients; extends evidence for edge-centric IoT in clinical cardiology.
Does an edge- and fog-based IoT healthcare system improve ECG classification precision and reduce alert latency in cardiac patients in hospital wards?
An edge-centric IoT system with machine learning can achieve high ECG classification precision and significantly reduce alert latency in hospital settings.
Baig et al. (2026) studied this question. Edge-centric IoT system achieved 91.96% ECG classification accuracy and reduced patient evaluation time to 15.23 ± 2.71 seconds in real hospital wards.