Why the study?
Does the Lightweight Reliability-Aware Internet of Medical Things Electrocardiogram Network (LR-IoMT-ECGNet) improve ECG classification performance and robustness compared to clean-trained CNN and ResNet-1D baselines in simulated degraded remote ECG monitoring records?
Does the Lightweight Reliability-Aware Internet of Medical Things Electrocardiogram Network (LR-IoMT-ECGNet) improve ECG classification performance and robustness compared to clean-trained CNN and ResNet-1D baselines in simulated degraded remote ECG monitoring records?
A compact, degradation-aware deep learning framework with selective decision support substantially preserves ECG classification specificity and overall performance under severe signal degradation compared to standard baseline models.
May enhance remote ECG reliability in IoMT; leaves open prospective clinical validation before adoption.
Remote electrocardiogram (ECG) monitoring in Internet of Medical Things (IoMT) systems is vulnerable to sensor noise, missing leads, packet loss, and temporal dropout. The proposed Lightweight Reliability-Aware Internet of Medical Things Electrocardiogram Network (LR-IoMT-ECGNet) is a lightweight reliability-aware framework that couples degradation-aware representation learning with selective decision support. Physikalisch-Technische Bundesanstalt eXtended Large (PTB-XL) electrocardiography dataset version 1.0.3 was used with official folds for training (n = 17,418), validation (n = 2183), and independent testing (n = 2198). Robustness was evaluated under clean, mild, moderate, and severe simulated degradation. A hybrid gate combining predictive confidence and signal reliability was tuned exclusively on validation data and locked before test evaluation. The 24,429-parameter model achieved an area under the receiver operating characteristic curve (AUC) of 0.9188 and F1-score of 0.8449 under clean conditions and an AUC of 0.8683, sensitivity of 0.7753, specificity of 0.8004, and F1-score of 0.8089 under severe degradation. By contrast, clean-trained CNN and ResNet-1D baselines showed near-zero severe specificity (0.0252 and 0.0143). On mixed-quality test data, the hybrid gate retained 90.2% of records and achieved accuracy of 0.8412, sensitivity of 0.8026, specificity of 0.8973, and F1-score of 0.8569 among accepted cases. Confidence-only referral was marginally stronger on some metrics, indicating that predictive confidence is the dominant selective signal while input reliability provides complementary information. The results support a compact, degradation-aware selective ECG framework for edge- or gateway-assisted monitoring, while external wearable validation remains necessary.
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Bilgehan et al. (2026) studied Remote electrocardiogram (ECG) monitoring under clean and degraded signal conditions (n=21,799). LR-IoMT-ECGNet (Lightweight Reliability-Aware Internet of Medical Things Electrocardiogram Network) vs. Clean-trained CNN and ResNet-1D baselines was evaluated on Diagnostic classification performance (AUC, F1-score, sensitivity, specificity, accuracy) under clean and simulated degraded conditions.
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