# ECG classification using Deep CNN and Gramian Angular Field

arXiv: https://arxiv.org/abs/2308.02395 · HTML: https://mlsanity.com/paper/2308.02395/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 1 of 1)

- [Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals](https://mlsanity.com/paper/2210.11408/) (2022)

## Built on by (most cited first, 3 of 3)

- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors](https://mlsanity.com/paper/2411.11764/) (2024)
- [Federated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devices](https://mlsanity.com/paper/2511.03753/) (2025)

## Abstract (arXiv)

This paper study provides a novel contribution to the field of signal processing and DL for ECG signal analysis by introducing a new feature representation method for ECG signals. The proposed method is based on transforming time frequency 1D vectors into 2D images using Gramian Angular Field transform. Moving on, the classification of the transformed ECG signals is performed using Convolutional Neural Networks (CNN). The obtained results show a classification accuracy of 97.47% and 98.65% for anomaly detection. Accordingly, in addition to improving the classification performance compared to the state-of-the-art, the feature representation helps identify and visualize temporal patterns in the ECG signal, such as changes in heart rate, rhythm, and morphology, which may not be apparent in the original signal. This has significant implications in the diagnosis and treatment of cardiovascular diseases and detection of anomalies.