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

arXiv: https://arxiv.org/abs/2210.11408 · HTML: https://mlsanity.com/paper/2210.11408/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 80 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [ECG Heartbeat Classification: A Deep Transferable Representation](https://mlsanity.com/paper/1805.00794/) (2018)
- [Physics-constrained Deep Learning for Robust Inverse ECG Modeling](https://mlsanity.com/paper/2107.12780/) (2021)
- [Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1805.09133/) (2018)
- [ECG Language Processing (ELP): a New Technique to Analyze ECG Signals](https://mlsanity.com/paper/2006.08841/) (2020)

## Built on by (most cited first, 15 of 15)

- [Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification](https://mlsanity.com/paper/2405.19363/) (2024)
- [Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey](https://mlsanity.com/paper/2401.13699/) (2024)
- [MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification](https://mlsanity.com/paper/2502.04515/) (2025)
- [Decentralized Attention Fails Centralized Signals: Rethinking Transformers for Medical Time Series](https://mlsanity.com/paper/2602.18473/) (2026)
- [MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records](https://mlsanity.com/paper/2407.00840/) (2024)
- [ECG classification using Deep CNN and Gramian Angular Field](https://mlsanity.com/paper/2308.02395/) (2023)
- [Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning](https://mlsanity.com/paper/2412.04067/) (2024)
- [BioMamba: Leveraging Spectro-Temporal Embedding in Bidirectional Mamba for Enhanced Biosignal Classification](https://mlsanity.com/paper/2503.11741/) (2025)
- [Geometry-aware Active Learning of Spatiotemporal Dynamic Systems](https://mlsanity.com/paper/2504.19012/) (2025)
- [A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis](https://mlsanity.com/paper/2507.12645/) (2025)
- [Cardioformer: Advancing AI in ECG Analysis with Multi-Granularity Patching and ResNet](https://mlsanity.com/paper/2505.05538/) (2025)
- [Physics-augmented Multi-task Gaussian Process for Modeling Spatiotemporal Dynamics](https://mlsanity.com/paper/2510.13601/) (2025)
- [Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification](https://mlsanity.com/paper/2506.00337/) (2025)
- [Unsupervised Identification and Replay-based Detection (UIRD) for New Category Anomaly Detection in ECG Signal](https://mlsanity.com/paper/2509.01512/) (2025)
- [Multi-Task LLM with LoRA Fine-Tuning for Automated Cancer Staging and Biomarker Extraction](https://mlsanity.com/paper/2604.13328/) (2026)

## Abstract (arXiv)

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.