Models in this group take an ECG waveform, typically ten seconds at 500 Hz. Each table gives three hardware tiers — Minimum, the smallest configuration on which the model runs correctly; Medium, the usual production configuration; and High, a configuration sized for peak volume. The rate is what one server of that tier processes per hour. Use these figures for initial sizing only. Before production we benchmark your own data to confirm accuracy, latency, throughput and cost.
ECGFounder
Vendor: Harvard Medical School / Emory University, PKU Digital Health and collaborators
What it does: scores 150 diagnostic labels from one waveform, in both single-lead and 12-lead form — the broadest label coverage on this page, at AUROC around 0.95 internally and 0.90 externally.
Mean AUROC about 0.95 across 150 diagnostic labels on internal test, about 0.90 external (developer-reported).
HeartBEiT
Vendor: Icahn School of Medicine at Mount Sinai
What it does: reads the ECG as an image and beats conventional networks when labelled data is scarce, which is the usual situation for a specific local endpoint.
AUC 0.90–0.93 for LV dysfunction and hypertrophic cardiomyopathy at low label budgets; beats CNNs when training data is scarce (independent, npj Digital Medicine 2023).
ECG-FM
Vendor: University Health Network / Vector Institute, Bo Wang Lab
What it does: produces waveform embeddings that reach AUROC 0.83–0.94 across ejection fraction, arrhythmia and outcome tasks with only a linear probe on top — a few hundred labels rather than a few hundred thousand.
AUROC 0.83–0.94 across LVEF, arrhythmia and outcome tasks after linear probing (independent).
ECGFM-KED
Vendor: ECGFM-KED authors
What it does: brings external diagnostic knowledge into the embedding, for classification where the label set is defined clinically rather than statistically.
Task-dependent; evaluated on multiple ECG downstream tasks, no single universal accuracy.
HuBERT-ECG
Vendor: HuBERT-ECG authors
What it does: adapts self-supervised speech modelling to ECG, producing embeddings for downstream cardiac tasks.
Task-dependent; reported across multiple cardiac applications, no single universal accuracy.
ST-MEM
Vendor: ST-MEM authors
What it does: is a representation model for ECG, intended as the base layer under your own classifier.
Task-dependent; representation-learning results vary by downstream ECG dataset.
CLEF-Large
Vendor: Nokia Bell Labs
What it does: is the largest of three single-lead encoders from the same family — the one to use when wearable and patch data is your input and accuracy governs.
Task-dependent; evaluated on multiple single-lead ECG diagnostic benchmarks.
CLEF-Medium
Vendor: Nokia Bell Labs
What it does: trades a little accuracy for a smaller footprint, for single-lead work at volume.
Task-dependent; evaluated on multiple single-lead ECG diagnostic benchmarks.
CLEF-Small
Vendor: Nokia Bell Labs
What it does: is the lightest single-lead encoder in the family, cheap enough to run on continuous wearable streams.
Task-dependent; evaluated on multiple single-lead ECG diagnostic benchmarks.
MOMENT-1-base
Vendor: Auton Lab / Carnegie Mellon University
What it does: is a general time-series model rather than a cardiac one, which makes it useful when ECG is only part of a wider signal pipeline including anomaly detection and forecasting.
Task-dependent; general time-series foundation model evaluated on ECG classification among other tasks.