Key result
Offline, explainable ECG framework matches GPU benchmarks on CPU with ~0.93 AUROC.
Why the study?
Automated ECG interpretation often relies on cloud deep-learning services that compromise data sovereignty, auditability, and robustness for accuracy, motivating development of an offline, explainable framework.
Answers are grounded in this paper and cite their sources.
An offline, explainable, tri-modal framework for ECG interpretation achieves competitive diagnostic accuracy with auditable findings, demonstrating the feasibility of edge inference without compromising data sovereignty.
OBJECTIVE: Automated electrocardiogram (ECG) interpretation is increasingly delivered as cloud deep-learning services, trading data sovereignty, auditability and robustness for accuracy. This study tests whether an offline, explainable framework can remove that trade-off. Approach. Three independent interpreters feed a decision-fusion layer: a deterministic engine of 26 executable clinical criteria, each finding linked to a highlighted signal segment and a literature source; a compact one-dimensional residual network trained on PTB-XL and run on-device via the Open Neural Network Exchange runtime; and an optional agentic vision-language model reading a rendered ECG image behind a security screen with human-in-the-loop escalation. Format-agnostic ingestion, lead-aware capability tiering and a three-tier signal-quality gate precede interpretation. Evaluation covers diagnostic accuracy, on-device latency, noise resilience, modality ablation and zero-shot external validation. Main results. On the PTB-XL test fold (n = 2198) the network reaches a macro-averaged area under the receiver operating characteristic curve (AUROC) of 0.932 (95% confidence interval (CI) 0.918-0.943), matching published GPU baselines, while the complete pipeline runs in 108 ± 25 ms on a CPU with no network access. Performance degrades gracefully to a signal-to-noise ratio of 5 dB (AUROC 0.951) as the quality gate rejects uninterpretable signals, and rules and network agree only weakly (Cohen's kappa 0.26), indicating complementary errors. Applied without retraining to an independent cohort (Chapman-Shaoxing, n = 1599) the model retains a macro-AUROC of 0.898. In a small pilot (25 records; 30 rule/network disagreements) the general-purpose agentic model was the weakest adjudicator (accuracy 0.267, 95% CI 0.142-0.444; exact McNemar test versus rules, p = 0.09). Significance. Edge inference, intrinsic explainability and governed multi-modal fusion give competitive accuracy with auditable findings. The agentic layer is positioned for governance rather than diagnosis, motivating ECG-specialized models. The open-source system is a research prototype, not a medical device.
No takes yet. Share an insight, caveat, or question.
Mohammad Mahbubur Rahman Khan Mamun (2026) studied Electrocardiogram (ECG) interpretation (n=3,822). Tri-modal, offline, explainable framework for 12-lead ECG interpretation vs. GPU baselines was evaluated on Macro-averaged area under the receiver operating characteristic curve (AUROC) (AUROC 0.932, 95% CI 0.918-0.943). An offline, explainable framework for 12-lead ECG interpretation achieved a macro-averaged AUROC of 0.932 (95% CI 0.918-0.943), matching published GPU baselines while running on a CPU.
Synapse has enriched 10 related papers. Consider them for comparative context: