# Deep Neural Networks Generalization and Fine-Tuning for 12-lead ECG Classification

arXiv: https://arxiv.org/abs/2305.18592 · HTML: https://mlsanity.com/paper/2305.18592/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Self-supervised ECG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2002.03898/) (2020)
- [Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review](https://mlsanity.com/paper/2001.01550/) (2019)
- [MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG](https://mlsanity.com/paper/2008.07263/) (2020)

## Built on by (most cited first, 4 of 4)

- [SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generation](https://mlsanity.com/paper/2407.11108/) (2024)
- [ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine-tuning](https://mlsanity.com/paper/2503.13570/) (2025)
- [Self-Trained Model for ECG Complex Delineation](https://mlsanity.com/paper/2406.02711/) (2024)
- [S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG](https://mlsanity.com/paper/2509.03066/) (2025)

## Abstract (arXiv)

Numerous studies are aimed at diagnosing heart diseases based on 12-lead electrocardiographic (ECG) records using deep learning methods. These studies usually use specific datasets that differ in size and parameters, such as patient metadata, number of doctors annotating ECGs, types of devices for ECG recording, data preprocessing techniques, etc. It is well-known that high-quality deep neural networks trained on one ECG dataset do not necessarily perform well on another dataset or clinical settings. In this paper, we propose a methodology to improve the quality of heart disease prediction regardless of the dataset by training neural networks on a variety of datasets with further fine-tuning for the specific dataset. To show its applicability, we train different neural networks on a large private dataset TIS containing various ECG records from multiple hospitals and on a relatively small public dataset PTB-XL. We demonstrate that training the networks on a large dataset and fine-tuning it on a small dataset from another source outperforms the networks trained only on one small dataset. We also show how the ability of a deep neural networks to generalize allows to improve classification quality of more diseases.