Classification of 12-lead ECGs Using Gradient Boosting on Features Acquired With Domain-Specific and Domain-Agnostic Methods

Computing in cardiology, vol. 47

Abstract

This year, the objective of the PhysioNet/Computing in Cardiology challenge was the classification of 12-lead electrocardiograms (ECG).The approach presented in this paper consists of two parts, feature extraction and classification.The extracted features can be separated into domain-specific and domain-agnostic features, where domain-specific features are based on known ECG processing methods such as QRS-detectors.Domain-agnostic features are generated by wavelet transforms that take the raw 12-lead ECG as input.Additionally, a novel beat-tobeat correlation analysis is proposed to identify arrhythmia occurring among other healthy beats.These features are then combined and classified by gradient-boosted trees implemented in Python.To account for the complexity of the multi-label and multi-class problem definition, a Onevs-Rest scheme is utilized, where distinct classifiers for each class determine whether a sample belongs to said class.The resulting imbalance in training sets for each classifier was compensated for by giving the positive samples a higher weight.The classifiers were trained using the XGBoost gradient boosting system.The proposed classification scheme of the team "desafinado" received a score of 0.576 on the validation dataset and a score of 0.233 on the test set of the challenge (rank 19 of 41).

Authors 4

1. Durmus Umutcan Uguz Aachen Medical Information Technology (MedIT)

   RWTH Aachen University

   Affiliation as printed

   Medical Information Technology , Helmholtz-Institute for Biomedical Engineering , RWTH Aachen University , Aachen , Germany

   Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany

2. Felix Berief Aachen Medical Information Technology (MedIT)

   RWTH Aachen University

   Affiliation as printed

   Medical Information Technology , Helmholtz-Institute for Biomedical Engineering , RWTH Aachen University , Aachen , Germany

   Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany

3. Steffen K. Leonhardt Aachen Medical Information Technology (MedIT)

   RWTH Aachen University

   Affiliation as printed

   Medical Information Technology , Helmholtz-Institute for Biomedical Engineering , RWTH Aachen University , Aachen , Germany

   Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany

4. Christoph Hoog Antink Aachen Medical Information Technology (MedIT)

   RWTH Aachen University

   Affiliation as printed

   Medical Information Technology , Helmholtz-Institute for Biomedical Engineering , RWTH Aachen University , Aachen , Germany

   Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany