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Cardiac disease is the leading cause of death in the US. Accurate heartdisease detection is of critical importance for timely medical treatment tosave patients' lives. Routine use of electrocardiogram (ECG) is the most commonmethod for physicians to assess the electrical activities of the heart anddetect possible abnormal cardiac conditions. Fully utilizing the ECG data forreliable heart disease detection depends on developing effective analyticalmodels. In this paper, we propose a two-level hierarchical deep learningframework with Generative Adversarial Network (GAN) for automatic diagnosis ofECG signals. The first-level model is composed of a Memory-Augmented Deepauto-Encoder with GAN (MadeGAN), which aims to differentiate abnormal signalsfrom normal ECGs for anomaly detection. The second-level learning aims atrobust multi-class classification for different arrhythmias identification,which is achieved by integrating the transfer learning technique to transferknowledge from the first-level learning with the multi-branching architectureto handle the data-lacking and imbalanced data issue. We evaluate theperformance of the proposed framework using real-world medical data from theMIT-BIH arrhythmia database. Experimental results show that our proposed modeloutperforms existing methods that are commonly used in current practice.

Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals