Key result
Hybrid LSTM-GNN detects ECG anomalies with ~90% accuracy across internal and external cohorts.
Why the study?
The Internet of Medical Things requires physiological monitoring systems that balance temporal signal dynamics, relational structure, and computational efficiency for binary ECG screening.
Does a lightweight hybrid LSTM–GNN framework with an ECG-derived proxy-context node improve binary ECG anomaly screening accuracy and reduce false-alarm rates in public arrhythmia databases?
Does a lightweight hybrid LSTM–GNN framework with an ECG-derived proxy-context node improve binary ECG anomaly screening accuracy and reduce false-alarm rates in public arrhythmia databases?
This computational study demonstrates that a compact 8,066-parameter hybrid LSTM–GNN model can perform reproducible binary ECG anomaly screening across public databases with sub-millisecond CPU latency, though clinical utility and physical edge-device deployment remain unvalidated.
The Internet of Medical Things requires physiological monitoring systems that balance temporal signal dynamics, relational structure, and computational efficiency. This study presents a lightweight hybrid LSTM–GNN framework for binary ECG screening, integrating explicit graph message passing, temporal modelling, and an ECG-derived proxy-context node. The framework was evaluated on 47 MIT-BIH records using strict record-wise splitting, preprocessing designed to avoid information leakage, and a fixed 0.50 decision threshold across five random seeds. In addition, an external cross-database evaluation was conducted on the INCART database without fine-tuning or threshold tuning, using a harmonized binary label mapping. On MIT-BIH, the Full Hybrid condition achieved the highest mean Accuracy (0.8991 ± 0.0363) and F1-score (0.8121 ± 0.0573), together with the lowest false-alarm rate (0.1129 ± 0.0523) among the evaluated conditions. On the external INCART database, the Full Hybrid condition achieved a mean Accuracy of 0.8801 ± 0.0151 and ROC-AUC of 0.9065 ± 0.0227 under the fixed external-test protocol. The ablation analysis indicated measurable contributions from the combined representations, while the context-permutation analysis showed that the proxy context should not be interpreted as an independent modality. In addition, software-level runtime profiling using ONNX Runtime demonstrated software-level portability characteristics and runtime efficiency on a standard CPU environment, with 8066 parameters, an approximately 36.1-kB model file, and a median CPU latency of 0.076 ms. The reported variability summarizes performance variation across five random seeds reflecting training stability, and should not be interpreted as patient-level inferential confidence intervals. These findings support reproducible, lightweight cross-database evaluation under the specified harmonization protocol, while limiting the claims to software-level computational feasibility rather than physical edge deployment, independent multimodal sensing, autonomous adaptive behavior, or established clinical utility.
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Mohamed Mostafa AbdulAli (2026) studied Cardiac arrhythmia / ECG anomaly screening (n=47). Full Hybrid LSTM-GNN framework with context-aware risk calibration vs. Baseline GNN and ablation conditions (no-context GNN, proxy-context GNN, no-context LSTM-GNN) was evaluated on Binary ECG anomaly screening accuracy and F1-score on the MIT-BIH dataset. The Full Hybrid LSTM-GNN framework achieved a mean accuracy of 0.8991 and F1-score of 0.8121 on MIT-BIH, with 0.8801 accuracy and 0.9065 ROC-AUC on external INCART validation.
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