Key result
The proposed GHOA and CBNN based IoT-WSN ECG monitoring system achieved an accuracy of 82.72% for R-R feature selection, showing a 2.14% improvement over existing CNN techniques.
Why the study?
Wearable ECG monitoring using IoT and WSN offers advantages for heart attack patients, motivating the development of a system to record patient data, detect heart attacks, and prioritize medical attention.
Comparison
Proposed GHOA and CBNN system vs existing techniques like CNN
Authors
Loading...
Caution advised before clinical deployment; leaves open prospective validation of patient outcomes.
Effect estimate: 2.14% improvement
Absolute Event Rate: 82.72% vs 80.58%
The proposed IoT-WSN enabled ECG monitoring system using GHOA-CBNN improves arrhythmia classification accuracy and enables automated patient queue prioritization based on disease seriousness.
Kaur et al. (2022) studied Arrhythmia (n=47). Grasshopper Optimization Algorithm (GHOA) and Conjugate Based Neural Network (CBNN) vs. Convolutional Neural Network (CNN) was evaluated on Accuracy for R-R feature selection of ECG signals (2.14% improvement). The proposed GHOA and CBNN based IoT-WSN ECG monitoring system achieved an accuracy of 82.72% for R-R feature selection, showing a 2.14% improvement over existing CNN techniques.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: