A Generative Modeling Approach to Limited Channel ECG Classification

With the unprecedented success of machine learning in solving challenging problems across multiple domains, there is increasing interest in leveraging state-of-the art techniques to applications in health care. The community-wide efforts for creating large-scale benchmark repositories, such as MIMIC-III and Physionet CinC challenge [1], have accelerated machine learning research in health care. Furthermore, with increased adoption of automated systems for disease diagnosis, there is a huge opportunity for building robust data-driven solutions that can alleviate pain-points within clinical workflows. Broadly, careful modeling of health care data requires tackling inherent challenges including multivariate measurements, long-range temporal dependencies, and missing information in order to make precise predictions. Despite the success of hand-engineered features in clinical models, more recently, regularized representation learning techniques, such as sparse and deep learning, have been more effective. A thorough experimental study on UCR time-series datasets revealed that simple deep learning architectures using 1-D Convolutional Neural Networks (CNNs) can easily outperform traditional task-specific models built on hand-engineered features [2]. More recently, Recurrent Neural Networks (RNN) based on Long Short Term Memory (LSTM) units have become the de-facto solution for clinical time-series analysis.