Key result
NSGA-OGCN model improves ECG-based arrhythmia detection over existing methods in IoT environments.
Why the study?
Artificially classifying and identifying CVD can be complex because manual inspection of ECG signals is labour-intensive, creating a need for automated monitoring approaches to detect arrhythmia in an IoT environment.
Does the NSGA-OGCN model improve arrhythmia detection and classification using ECG signals in an IoT environment?
Does the NSGA-OGCN model improve arrhythmia detection and classification using ECG signals in an IoT environment?
The proposed NSGA-OGCN model offers an improved automated approach for detecting and classifying arrhythmias from ECG signals in IoT environments.
May advance IoT-based arrhythmia detection; leaves open clinical utility pending prospective validation.
Recently, a massive development in the control of IoT is witnessed which offers a lot in the healthcare approach as it enables constant patient observation and then there is a condition for advanced automated monitoring approaches for classification of heart attacks. An Electrocardiogram (ECG) can be extremely employed for estimating Cardiovascular Disease (CVD) that is outcome further explores heart conditions and detection of CVD. The process is fast, easy, and non-invasive. However, artificially classifying and identifying CVD can be complex, as manually inspecting the ECG signals is labour-intensive. This study presents Non-Dominated Sorting Genetic Algorithm with Optimal Graph Convolutional Network (NSGA-OGCN) model for arrhythmia detection. The intention of the NSGA-OGCN technique is the proper detection and classification of arrhythmia in the IoT atmosphere. Here, the NSGA-OGCN technique uses NSGA technique for optimal selection of feature subsets. For ECG signal detection, the GCN model is used. Finally, Adamax optimizer is utilized for the optimal hyperparameter tuning of the GCN model. The simulation results of the NSGA-OGCN methodology are examined on benchmark datasets. Comprehensive comparison analysis reported the improved accomplishment of the NSGA-OGCN methodology in terms of different measures.
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Jeyavathana et al. (2023) studied Arrhythmia. Non-Dominated Sorting Genetic Algorithm with Optimal Graph Convolutional Network (NSGA-OGCN) model vs. Other models was evaluated on Arrhythmia detection and classification performance. The proposed NSGA-OGCN model for ECG-based arrhythmia detection in an IoT environment demonstrated improved classification performance compared to existing methods on benchmark datasets.
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