Domain-Adaptive Arrhythmia Classification Using a Hybrid Transformer on Wearable Heart Signals

TL;DR

This paper introduces a hybrid transformer model that combines ECG morphology and HRV features, employing domain adaptation techniques to improve arrhythmia classification across different data sources, including wearable devices.

Contribution

The study presents a novel hybrid transformer architecture with domain adaptation via MMD, enhancing generalization of arrhythmia detection models to wearable device data.

Findings

Achieved 95% F1-macro on unseen wearable data

Model's performance drops only 2% compared to seen-domain evaluation

Effectively mitigated domain shifts using MMD-based feature alignment

Abstract

Cardiovascular disease remains the leading cause of death globally, underscoring the need for effective, accessible monitoring solutions, particularly through wearable devices that enable continuous, real-time tracking of heart rhythms in home settings. However, deploying deep learning models trained on clinical electrocardiogram (ECG) datasets to wearable devices remains challenging, as differences in recording equipment, signal quality, and patient populations introduce domain shifts that degrade model performance. We propose a hybrid transformer model that processes continuous ECG signals alongside seven heart rate variability (HRV) features, where the raw signal path captures beat-level morphological patterns and the HRV path encodes rhythm regularity statistics, allowing the model to jointly leverage complementary information from both representations. To enhance the model's ability to generalize across domains, we employ representation learning techniques, including Maximum Mean Discrepancy (MMD), a non-parametric kernel-based metric that quantifies the distance between feature distributions of different domains, to align feature distributions between source and target domains, addressing the challenge of domain shifts between public datasets and wearable device data. By leveraging five public ECG datasets for training, the model learns robust, generalized representations that mitigate domain-specific biases. When tested on wearable device data with an unseen domain, the model achieved an F1-macro 95% and balanced accuracy of 96.15%. These results demonstrate minimal performance degradation, with only a 2% drop in F1-macro compared to seen-domain evaluation, highlighting the model's generalization capabilities and its potential for reliable, real-time heart monitoring applications in home and ambulatory settings.