Multi-label classification of reduced-lead ECGs using an interpretable deep convolutional neural network

Abstract

Abstract Objective. We propose a model that can perform multi-label classification on 26 cardiac abnormalities from reduced lead Electrocardiograms (ECGs) and interpret the model. Approach. PhysioNet/computing in cardiology (CinC) challenge 2021 datasets are used to train the model. All recordings shorter than 20 s are preprocessed by normalizing, resampling, and zero-padding. The frequency domains of the recordings are obtained by applying fast Fourier transform. The time domain and frequency domain of the signals are fed into two separate deep convolutional neural networks. The outputs of these networks are then concatenated and passed through a fully connected layer that outputs the probabilities of 26 classes. Data imbalance is addressed by using a threshold of 0.13 to the sigmoid output. The 2-lead model is tested under noise contamination based on the quality of the signal and interpreted using SHapley Additive exPlanations (SHAP). Main results. The proposed method obtained a challenge score of 0.55, 0.51, 0.56, 0.55, and 0.56, ranking 2nd, 5th, 3rd, 3rd, and 3rd out of 39 officially ranked teams on 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead hidden test datasets, respectively, in the PhysioNet/CinC challenge 2021. The model performs well under noise contamination with mean F1 scores of 0.53, 0.56 and 0.56 for the excellent, barely acceptable and unacceptable signals respectively. Analysis of the SHAP values of the 2-lead model verifies the performance of the model while providing insight into labeling inconsistencies and reasons for the poor performance of the model in some classes. Significance. We have proposed a model that can accurately identify 26 cardiac abnormalities using reduced lead ECGs that performs comparably with 12-lead ECGs and interpreted the model behavior. We demonstrate that the proposed model using only the limb leads performs with accuracy comparable to that using all 12 leads.