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.
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.
The difficulties of continuously monitoring cardiac patients in general hospital wards are still present because of the manual charting system and the slow clinical reaction to worsening physiological state. This paper outlines an edge- and fog-based Internet of Things (IoT) healthcare system to acquire, process, and prioritize the vital signs of patients in real time to minimize the alert latency and increase the time of clinical interventions. Wearable 12-lead ECG sensors transmit physiological measurements, such as heart rate, blood pressure, and oxygen saturation, to an intelligent edge service, where preprocessing, triage by threshold, and machine learning ECG classification are performed, and selective synchronization of physiological data with a cloud backend and data delivery to the clinician are made possible by a mobile application. The proposed architecture combines a ribbon-like streaming scheme, Flask-based gateway services, and Firebase Firestore to coordinate scalable mob/cloud with the help of multi-client data dissemination. To encompass borderline clinical deterioration, which is often unnoticed by conventional threshold systems, physiological parameters are classified into normal, alarming, emergency, and a new state, average. The Pan–Tompkins++ peak detector algorithm and multiple edge-resident classifiers, such as random forest, XGBoost, decision tree, naive Bayes, K-nearest neighbor, and support vector machine, are used to analyze the ECG waveforms. Experimental analysis of PhysioNet datasets and tests in real wards prove that the ensemble models can reach the highest possible ECG classification precision of 91.96 percent and snapshot-driven mobile alerts can decrease routine patient evaluation time by several minutes, to an average of 15.23 ± 2.71 s. These results suggest that edge-centric IoT systems can be appropriate in latency-critical hospital settings and that fog-based coordination is useful in next-generation smart healthcare systems.
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.