# ECG arrhythmia classification using a 2-D convolutional neural network

arXiv: https://arxiv.org/abs/1804.06812 · HTML: https://mlsanity.com/paper/1804.06812/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2018.

## Built on by (most cited first, 17 of 17)

- [Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram](https://mlsanity.com/paper/2402.09450/) (2024)
- [Synthetic ECG Signal Generation using Probabilistic Diffusion Models](https://mlsanity.com/paper/2303.02475/) (2023)
- [Analysis of Augmentations for Contrastive ECG Representation Learning](https://mlsanity.com/paper/2206.07656/) (2022)
- [ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis](https://mlsanity.com/paper/2502.17475/) (2025)
- [Strengthening the Training of Convolutional Neural Networks By Using Walsh Matrix](https://mlsanity.com/paper/2104.00035/) (2021)
- [Optimizing Neural Network Scale for ECG Classification](https://mlsanity.com/paper/2308.12492/) (2023)
- [ECG beat classification using machine learning and pre-trained convolutional neural networks](https://mlsanity.com/paper/2207.06408/) (2022)
- [Compact Neural Network Algorithm for Electrocardiogram Classification](https://mlsanity.com/paper/2412.17852/) (2024)
- [Arrhythmia Classification from 12-Lead ECG Signals Using Convolutional and Transformer-Based Deep Learning Models](https://mlsanity.com/paper/2502.17887/) (2025)
- [VTBench: A Multimodal Framework for Time-Series Classification with Chart-Based Representations](https://mlsanity.com/paper/2604.27259/) (2026)
- [Two-stream Network for ECG Signal Classification](https://mlsanity.com/paper/2210.06293/) (2022)
- [Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity](https://mlsanity.com/paper/2303.11429/) (2023)
- [Remembering Everything Makes You Vulnerable: A Limelight on Machine Unlearning for Personalized Healthcare Sector](https://mlsanity.com/paper/2407.04589/) (2024)
- [Non-linear Analysis Based ECG Classification of Cardiovascular Disorders](https://mlsanity.com/paper/2408.01542/) (2024)
- [rECGnition_v1.0: Arrhythmia detection using cardiologist-inspired multi-modal architecture incorporating demographic attributes in ECG](https://mlsanity.com/paper/2410.18985/) (2024)
- [Knowledge Augmentation via Synthetic Data: A Framework for Real-World ECG Image Classification](https://mlsanity.com/paper/2507.21968/) (2025)
- [ArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systems](https://mlsanity.com/paper/2606.02256/) (2026)

## Abstract (arXiv)

In this paper, we propose an effective electrocardiogram (ECG) arrhythmia classification method using a deep two-dimensional convolutional neural network (CNN) which recently shows outstanding performance in the field of pattern recognition. Every ECG beat was transformed into a two-dimensional grayscale image as an input data for the CNN classifier. Optimization of the proposed CNN classifier includes various deep learning techniques such as batch normalization, data augmentation, Xavier initialization, and dropout. In addition, we compared our proposed classifier with two well-known CNN models; AlexNet and VGGNet. ECG recordings from the MIT-BIH arrhythmia database were used for the evaluation of the classifier. As a result, our classifier achieved 99.05% average accuracy with 97.85% average sensitivity. To precisely validate our CNN classifier, 10-fold cross-validation was performed at the evaluation which involves every ECG recording as a test data. Our experimental results have successfully validated that the proposed CNN classifier with the transformed ECG images can achieve excellent classification accuracy without any manual pre-processing of the ECG signals such as noise filtering, feature extraction, and feature reduction.