Ensemble classifier combining hand-engineered expert features with a deep convolutional neural network for classifying cardiac rhythm from a single-lead ECG recording into normal sinus rhythm, atrial fibrillation, another rhythm, or too noisy to classify. A large set of expert features (from time-, frequency-, and template-based analysis) is fed into a gradient-boosted tree classifier (AdaBoost), and its output is combined with a separate deep CNN operating directly on the raw waveform; combining both feature families measurably outperformed either alone. ENCASE won 1st place in the PhysioNet/Computing in Cardiology Challenge 2017 (single-lead AF classification) with an overall F1 score of 0.83 on the official hidden test set, and remains a widely cited example of combining classical signal-processing features with deep representations for ECG classification.

Multi-task model that jointly assesses signal quality and detects atrial fibrillation from wrist-worn wearable photoplethysmography (PPG), pretrained on roughly one million simulated unlabeled signals before fine-tuning on labeled wearable data. Uses a 1D CNN with separate output heads for signal quality and arrhythmia detection. Developed by Stanford's Ashley Lab.

Multi-task 12-lead ECG model with output heads for incident atrial-fibrillation risk (as a survival curve), incident mortality risk, prevalent AF classification, sex classification, and age regression. Built on a 1D CNN over the raw waveform, and developed by the Broad Institute's ML4H group as a successor to their ECG-AI model published in Circulation. Trained on ECGs from UK Biobank and Massachusetts General Hospital, neither of which is publicly released.

Multimodal ECG model that pairs a 1D ConvNeXtV2 signal encoder with a BioLinkBERT text encoder, trained with a joint contrastive-and-captioning objective using LLM-generated descriptions of ECG demographics and waveform patterns in place of raw clinical reports. Validated on arrhythmia diagnosis and ECG-based subject identification, reaching an AUROC of 0.938 fine-tuned and 0.812 zero-shot on PTB-XL diagnostic classification. Developed at Rice University.

Reference benchmark suite for the PTB-XL ECG dataset, providing pretrained xresnet1d, InceptionTime, LSTM, and CPC-pretrained models for 71-label, diagnostic, sub-diagnostic, and super-diagnostic classification of ECG findings. Widely used as a standardized baseline for comparing new ECG classification methods. Developed by Strodthoff et al. at Fraunhofer HHI.