# Deep Learning in Cardiology

arXiv: https://arxiv.org/abs/1902.11122 · HTML: https://mlsanity.com/paper/1902.11122/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2019.

## Built on by (most cited first, 8 of 8)

- [Review of multimodal machine learning approaches in healthcare](https://mlsanity.com/paper/2402.02460/) (2024)
- [Fitting Auditory Filterbanks with Multiresolution Neural Networks](https://mlsanity.com/paper/2307.13821/) (2023)
- [Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network](https://mlsanity.com/paper/2206.10592/) (2022)
- [RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms](https://mlsanity.com/paper/2111.02569/) (2021)
- [MVMTnet: A Multi-variate Multi-modal Transformer for Multi-class Classification of Cardiac Irregularities Using ECG Waveforms and Clinical Notes](https://mlsanity.com/paper/2302.11021/) (2023)
- [Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation](https://mlsanity.com/paper/2302.02289/) (2023)
- [Automatic Feature Extraction for Heartbeat Anomaly Detection](https://mlsanity.com/paper/2102.12289/) (2021)
- [Deep artificial neural network for prediction of atrial fibrillation through the analysis of 12-leads standard ECG](https://mlsanity.com/paper/2202.05676/) (2022)

## Abstract (arXiv)

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.