# ECG-Based Heart Arrhythmia Diagnosis Through Attentional Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2108.10226 · HTML: https://mlsanity.com/paper/2108.10226/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks](https://mlsanity.com/paper/1707.01836/) (2017)
- [Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning](https://mlsanity.com/paper/1907.13359/) (2019)
- [MindID: Person Identification from Brain Waves through Attention-based Recurrent Neural Network](https://mlsanity.com/paper/1711.06149/) (2017)

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

- [Self-Supervised Learning for Time Series: Contrastive or Generative?](https://mlsanity.com/paper/2403.09809/) (2024)
- [LLM-ABBA: Understanding time series via symbolic approximation](https://mlsanity.com/paper/2411.18506/) (2024)
- [LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network](https://mlsanity.com/paper/2204.08000/) (2022)

## Abstract (arXiv)

Electrocardiography (ECG) signal is a highly applied measurement for individual heart condition, and much effort have been endeavored towards automatic heart arrhythmia diagnosis based on machine learning. However, traditional machine learning models require large investment of time and effort for raw data preprocessing and feature extraction, as well as challenged by poor classification performance. Here, we propose a novel deep learning model, named Attention-Based Convolutional Neural Networks (ABCNN) that taking advantage of CNN and multi-head attention, to directly work on the raw ECG signals and automatically extract the informative dependencies for accurate arrhythmia detection. To evaluate the proposed approach, we conduct extensive experiments over a benchmark ECG dataset. Our main task is to find the arrhythmia from normal heartbeats and, at the meantime, accurately recognize the heart diseases from five arrhythmia types. We also provide convergence analysis of ABCNN and intuitively show the meaningfulness of extracted representation through visualization. The experimental results show that the proposed ABCNN outperforms the widely used baselines, which puts one step closer to intelligent heart disease diagnosis system.