Peking University (Hong et al.)
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.
Architecture
Hybrid
Ensemble combining an AdaBoost classifier trained on ~150 hand-engineered time/frequency/template ECG features with a deep convolutional neural network trained directly on the raw single-lead waveform
Added to catalog
2026-08-12
GPL 3.0
License for model weights only. Associated code may be licensed seperately, check code source for specific terms.
Short single-lead ECG recordings (9-61s) labeled as normal, AF, other rhythm, or noisy.
Four-class rhythm classification (normal sinus rhythm, atrial fibrillation, other rhythm, or too noisy to classify) from a single-lead ECG recording