# Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

arXiv: https://arxiv.org/abs/2005.06902 · HTML: https://mlsanity.com/paper/2005.06902/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 5 of 5)

- [Leveraging Statistical Shape Priors in GAN-based ECG Synthesis](https://mlsanity.com/paper/2211.02626/) (2022)
- [Analysis of Digitalized ECG Signals Based on Artificial Intelligence and Spectral Analysis Methods Specialized in ARVC](https://mlsanity.com/paper/2203.00504/) (2022)
- [Towards plausibility in time series counterfactual explanations](https://mlsanity.com/paper/2603.08349/) (2026)
- [Heart Rate Classification in ECG Signals Using Machine Learning and Deep Learning](https://mlsanity.com/paper/2506.06349/) (2025)
- [WIPSNet: Deep Learning for Paediatric Wheeze Detection from Overnight Impedance Pneumography](https://mlsanity.com/paper/2610.00398/) (2026)

## Abstract (arXiv)

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.