Uncertainty-Aware Domain Adaptation for ECG Classification | JoVE Visualize

Area of Science:

Cardiology
Artificial Intelligence
Machine Learning

Background:

Automated electrocardiogram (ECG) classification aids cardiovascular diagnoses but struggles with data variations across different clinical settings (domains).
Real-world deployment of machine learning (ML) models is hindered by domain shift, where data differs from training conditions.
Unsupervised domain adaptation (UDA) using readily available unlabeled target data can overcome these limitations.

Purpose of the Study:

To investigate the efficacy of uncertainty objectives in improving unsupervised domain adaptation for automated arrhythmia classification on new ECG datasets.
To develop and validate novel UDA methods that leverage uncertainty quantification within a Dirichlet Prior Network framework.
To enhance the clinical applicability of ML models for cardiovascular diagnostics by addressing domain shift challenges.

Main Methods:

Applied a Dirichlet Prior Network to ECG data from the MIT-BIH and St. Petersburg INCART arrhythmia datasets.
Implemented two uncertainty-based UDA approaches: minimizing target domain uncertainty for alignment and aligning source/target class prediction uncertainty.
Evaluated model performance using F1 scores for ternary arrhythmia classification and compared against baseline and state-of-the-art methods.

Main Results:

The first approach, minimizing target domain uncertainty, improved the baseline target F1 score by 7% for ternary arrhythmia classification.
The second approach, aligning class prediction uncertainty, enhanced state-of-the-art domain adaptation performance by 3%.
Both methods demonstrated improved heartbeat classification accuracy on ECGs from different hospitals, indicating successful domain adaptation.

Conclusions:

Uncertainty objectives offer a promising strategy for improving the robustness and generalizability of automated arrhythmia classification models.
The proposed UDA methods effectively address domain shift in ECG data, facilitating wider clinical adoption of AI-powered diagnostic tools.
Leveraging unlabeled target domain data with uncertainty quantification is crucial for real-world clinical machine learning applications in cardiology.