# Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach

arXiv: https://arxiv.org/abs/1812.07421 · HTML: https://mlsanity.com/paper/1812.07421/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2018.

## Built on by (most cited first, 15 of 15)

- [Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports](https://mlsanity.com/paper/2303.09395/) (2023)
- [ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning](https://mlsanity.com/paper/2306.06340/) (2023)
- [ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2107.07677/) (2021)
- [End-to-End Signal Classification in Signed Cumulative Distribution Transform Space](https://mlsanity.com/paper/2205.00348/) (2022)
- [LLM-ABBA: Understanding time series via symbolic approximation](https://mlsanity.com/paper/2411.18506/) (2024)
- [IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification](https://mlsanity.com/paper/2204.05116/) (2022)
- [ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique](https://mlsanity.com/paper/2206.14200/) (2022)
- [Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals](https://mlsanity.com/paper/2402.09474/) (2024)
- [xLSTM-ECG: Multi-label ECG Classification via Feature Fusion with xLSTM](https://mlsanity.com/paper/2504.16101/) (2025)
- [A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection](https://mlsanity.com/paper/2102.01319/) (2021)
- [Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG Data](https://mlsanity.com/paper/2205.02803/) (2022)
- [Leveraging Visibility Graphs for Enhanced Arrhythmia Classification with Graph Convolutional Networks](https://mlsanity.com/paper/2404.15367/) (2024)
- [SGEMAS: A Self-Growing Ephemeral Multi-Agent System for Unsupervised Online Anomaly Detection via Entropic Homeostasis](https://mlsanity.com/paper/2512.14708/) (2025)
- [A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection](https://mlsanity.com/paper/2102.03538/) (2021)
- [ECG Classification System for Arrhythmia Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/2303.03660/) (2023)

## Abstract (arXiv)

Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmias. While there have been remarkable improvements in cardiac arrhythmia classification methods, they still cannot offer acceptable performance in detecting different heart conditions, especially when dealing with imbalanced datasets. In this paper, we propose a solution to address this limitation of current classification approaches by developing an automatic heartbeat classification method using deep convolutional neural networks and sequence to sequence models. We evaluated the proposed method on the MIT-BIH arrhythmia database, considering the intra-patient and inter-patient paradigms, and the AAMI EC57 standard. The evaluation results for both paradigms show that our method achieves the best performance in the literature (a positive predictive value of 96.46% and sensitivity of 100% for the category S, and a positive predictive value of 98.68% and sensitivity of 97.40% for the category F for the intra-patient scheme; a positive predictive value of 92.57% and sensitivity of 88.94% for the category S, and a positive predictive value of 99.50% and sensitivity of 99.94% for the category V for the inter-patient scheme.). The source code is available at https://github.com/SajadMo/ECG-Heartbeat-Classification-seq2seq-model.