  Cardiac disease is the leading cause of death in the US. Accurate heart
disease detection is of critical importance for timely medical treatment to
save patients' lives. Routine use of electrocardiogram (ECG) is the most common
method for physicians to assess the electrical activities of the heart and
detect possible abnormal cardiac conditions. Fully utilizing the ECG data for
reliable heart disease detection depends on developing effective analytical
models. In this paper, we propose a two-level hierarchical deep learning
framework with Generative Adversarial Network (GAN) for automatic diagnosis of
ECG signals. The first-level model is composed of a Memory-Augmented Deep
auto-Encoder with GAN (MadeGAN), which aims to differentiate abnormal signals
from normal ECGs for anomaly detection. The second-level learning aims at
robust multi-class classification for different arrhythmias identification,
which is achieved by integrating the transfer learning technique to transfer
knowledge from the first-level learning with the multi-branching architecture
to handle the data-lacking and imbalanced data issue. We evaluate the
performance of the proposed framework using real-world medical data from the
MIT-BIH arrhythmia database. Experimental results show that our proposed model
outperforms existing methods that are commonly used in current practice.
