A power-efficient layered MIoT framework for real-time ECG anomaly detection and sensor fault classification based on hierarchical THECF and hybrid intelligent models.

This study proposes a novel layered architecture for the automated detection and classification of cardiac abnormalities from ECG signals within Medical Internet of Things (MIoT) environments. The primary objective is to reduce network transmission energy consumption by performing signal processing at the edge layer and forwarding only abnormal data to higher layers for precise classification. In the first stage, a lightweight hybrid generative adversarial network (HGAN) is deployed on edge devices. This model incorporates support vector machine (SVM) and decision tree (DT) mechanisms within the HGAN framework to enable efficient detection of abnormal ECG signals. This method exploits the complementary advantages of SVM for effective decision boundary separation and DT for transparent and interpretable decision rules within a GAN-based framework, enabling real-time anomaly detection with minimal computational cost. Within the proposed architecture, only the identified anomalous signals are transmitted to upper IoT layers, resulting in an estimated energy reduction of 85.2% compared to benchmark edge-based systems. In the subsequent stage, the detected abnormal ECG signals are transformed into discriminative two-dimensional representations corresponding to different classes using a novel differential signal-to-image conversion technique. These images are then classified into seven arrhythmia categories (e.g., atrial fibrillation, ventricular tachycardia, etc.) using a Convolutional Neural Network (CNN). The training processes of both stages are optimized using the Starfish Optimization Algorithm (SFOA), a meta-heuristic optimization technique, applied to the MIT-BIH arrhythmia database. This optimization strategy improves model parameter tuning, leading to faster convergence and enhanced generalization capability. Experimental evaluations on the benchmark dataset demonstrate that the first-stage abnormality detection achieves accuracy of 99.7996%, recall of 99.88%, and F1-score of 99.83%. In the second stage, the arrhythmia classification module attains accuracy of 99.56%, recall of 98.97%, and F1-score of 99.23%. Overall, the proposed architecture exhibits superior real-time performance, significant energy efficiency, and strong compatibility with IoT-based wearable sensors, offering an effective and privacy-aware solution for continuous cardiac health monitoring.