Classification of 12-lead ECGs Using Digital Biomarkers and Representation Learning

Abstract

Background: The 12-lead electrocardiogram (ECG) is a standard tool used in medical practice for identifying cardiac abnormalities. The 2020 PhysioNet/Computing in Cardiology Challenge addresses the topic of automated classification of 12-lead ECG. Methods: Two machine learning strategies were implemented: a feature engineering approach based on the engineering of physiological features (or 'digital biomarkers') and a deep learning approach. Two sets of features were engineered: (1) capturing the interval variation between consecutive heartbeats, commonly called heart rate variability (HRV) measures and (2) using morphological biomarkers (e.g. QT interval, QRS width). A total of 16 HRV and 97 morphological biomarkers were implemented in python for each lead. A random forest (RF) model was trained using 5-fold cross validation to optimize the model hyperparameters. For the deep learning approach, a residual neural network (ResNet) architecture was used. The RF and ResNet were also combined in an ensemble learning (EL). The dataset was divided into 80%-20% stratified training-test sets. Results: on the local test set we achieved a Challenge score of 0.65 using the FE approach, 0.52 using the DL approach and 0.66 using the EL approach. For technical reasons we did not manage to score our models on the Challenge hidden test set.