A Generative Modeling Approach to Limited Channel ECG Classification

TL;DR

This paper introduces a generative modeling framework using Seq2Seq to improve multi-channel ECG classification when limited channels are available, outperforming traditional RNNs in disease prediction tasks.

Contribution

It proposes a novel generative approach that generates missing ECG channels and enhances classification robustness, addressing limitations of discriminative models in limited-channel scenarios.

Findings

Outperforms standard RNNs in disease prediction on Physionet dataset

Utilizes unsupervised data for improved generalization

Provides robust metric spaces for discriminative learning

Abstract

Processing temporal sequences is central to a variety of applications in health care, and in particular multi-channel Electrocardiogram (ECG) is a highly prevalent diagnostic modality that relies on robust sequence modeling. While Recurrent Neural Networks (RNNs) have led to significant advances in automated diagnosis with time-series data, they perform poorly when models are trained using a limited set of channels. A crucial limitation of existing solutions is that they rely solely on discriminative models, which tend to generalize poorly in such scenarios. In order to combat this limitation, we develop a generative modeling approach to limited channel ECG classification. This approach first uses a Seq2Seq model to implicitly generate the missing channel information, and then uses the latent representation to perform the actual supervisory task. This decoupling enables the use of unsupervised data and also provides highly robust metric spaces for subsequent discriminative learning. Our experiments with the Physionet dataset clearly evidence the effectiveness of our approach over standard RNNs in disease prediction.