A Generative Modeling Approach to Limited Channel ECG Classification
This addresses a critical limitation in automated ECG diagnosis for healthcare applications, though it is incremental as it builds on existing sequence modeling techniques.
The paper tackles the problem of poor generalization in limited-channel ECG classification by proposing a generative modeling approach that uses a Seq2Seq model to generate missing channel information, achieving improved disease prediction on the Physionet dataset.
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.