# A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram

arXiv: https://arxiv.org/abs/2204.13917 · HTML: https://mlsanity.com/paper/2204.13917/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 29 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Domain Generalization with MixStyle](https://mlsanity.com/paper/2104.02008/) (2021)
- [A Network Perspective on Stratification of Multi-Label Data](https://mlsanity.com/paper/1704.08756/) (2017)

## Built on by (most cited first, 1 of 1)

- [Atrial Fibrillation Detection with Arbitrary Leads via a Codebook-Based Reconstruction-Classification Framework](https://mlsanity.com/paper/2608.18451/) (2026)

## Abstract (arXiv)

Electrocardiogram(ECG) is commonly used to detect cardiac irregularities such as atrial fibrillation, bradycardia, and other irregular complexes. While previous studies have achieved great accomplishment classifying these irregularities with standard 12-lead ECGs, there existed limited evidence demonstrating the utility of reduced-lead ECGs in capturing a wide-range of diagnostic information. In addition, classification model's generalizability across multiple recording sources also remained uncovered. As part of the PhysioNet Computing in Cardiology Challenge 2021, our team HaoWan AIeC, proposed Mixed-Domain Self-Attention Resnet (MDARsn) to identify cardiac abnormalities from reduced-lead ECG. Our classifiers received scores of 0.602, 0.593, 0.597, 0.591, and 0.589 (ranked 54th, 37th, 38th, 38th, and 39th) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden validation set with the evaluation metric defined by the challenge.