ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks

We propose ENCASE to combine expert features and DNNs (Deep Neural Networks) together for ECG classification. We first explore and implement expert features from statistical area, signal processing area and medical area. Then, we build DNNs to automatically extract deep features. Besides, we propose a new algorithm to find the most representative wave (called centerwave) among long ECG record, and extract features from centerwave. Finally, we combine these features together and put them into ensemble classifiers. Experiment on 4-class ECG data classification reports 0.84 F1 score, which is much better than any of the single model.



  Add Datasets introduced or used in this paper

Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Time Series Classification Physionet 2017 Atrial Fibrillation ENCASE F1 (Hidden Test Set) 0.825 # 1
Arrhythmia Detection The PhysioNet Computing in Cardiology Challenge 2017 ResNet + Expert Features F1 (Hidden Test Set) 0.825 # 1