# Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography

arXiv: https://arxiv.org/abs/2204.07253 · HTML: https://mlsanity.com/paper/2204.07253/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 14 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Subspace Support Vector Data Description](https://mlsanity.com/paper/1802.03989/) (2018)
- [Early Detection of Myocardial Infarction in Low-Quality Echocardiography](https://mlsanity.com/paper/2010.02281/) (2020)
- [Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection](https://mlsanity.com/paper/2008.04615/) (2020)
- [Multimodal Subspace Support Vector Data Description](https://mlsanity.com/paper/1904.07698/) (2019)
- [Ellipsoidal Subspace Support Vector Data Description](https://mlsanity.com/paper/2003.09504/) (2020)
- [Boosting rare benthic macroinvertebrates taxa identification with one-class classification](https://mlsanity.com/paper/2002.10420/) (2020)
- [Early Myocardial Infarction Detection over Multi-view Echocardiography](https://mlsanity.com/paper/2111.05790/) (2021)

## Built on by (most cited first, 9 of 9)

- [Newton Method-based Subspace Support Vector Data Description](https://mlsanity.com/paper/2309.13960/) (2023)
- [Credit Card Fraud Detection with Subspace Learning-based One-Class Classification](https://mlsanity.com/paper/2309.14880/) (2023)
- [One-Class Classification for Intrusion Detection on Vehicular Networks](https://mlsanity.com/paper/2309.14134/) (2023)
- [SAF-Net: Self-Attention Fusion Network for Myocardial Infarction Detection using Multi-View Echocardiography](https://mlsanity.com/paper/2309.15520/) (2023)
- [Trustworthiness of mathbbX Users: A One-Class Classification Approach](https://mlsanity.com/paper/2402.02066/) (2024)
- [Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification](https://mlsanity.com/paper/2402.06530/) (2024)
- [Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description](https://mlsanity.com/paper/2412.10792/) (2024)
- [Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach](https://mlsanity.com/paper/2502.15793/) (2025)
- [To Vaccinate or not to Vaccinate? Analyzing mathbbX Power over the Pandemic](https://mlsanity.com/paper/2503.02563/) (2025)

## Abstract (arXiv)

Myocardial infarction (MI) is the leading cause of mortality and morbidity in the world. Early therapeutics of MI can ensure the prevention of further myocardial necrosis. Echocardiography is the fundamental imaging technique that can reveal the earliest sign of MI. However, the scarcity of echocardiographic datasets for the MI detection is the major issue for training data-driven classification algorithms. In this study, we propose a framework for early detection of MI over multi-view echocardiography that leverages one-class classification (OCC) techniques. The OCC techniques are used to train a model for detecting a specific target class using instances from that particular category only. We investigated the usage of uni-modal and multi-modal one-class classification techniques in the proposed framework using the HMC-QU dataset that includes apical 4-chamber (A4C) and apical 2-chamber (A2C) views in a total of 260 echocardiography recordings. Experimental results show that the multi-modal approach achieves a sensitivity level of 85.23% and F1-Score of 80.21%.