Unsupervised Domain Adaptation for ECG Arrhythmia Classification.

Area of Science:

• Biomedical Signal Processing
• Artificial Intelligence in Medicine
• Cardiology

Background:

• Electrocardiograph (ECG) signals are vital for diagnosing cardiac arrhythmias.
• Deep learning models achieve high accuracy in intra-patient ECG analysis but struggle with inter-patient variability.
• Performance degradation in inter-patient ECG classification necessitates advanced adaptation techniques.

Purpose of the Study:

• To develop a novel unsupervised domain adaptation scheme for robust ECG heartbeat classification.
• To address the performance decline of deep learning models when applied to new patient populations.
• To improve the generalizability of ECG diagnostic algorithms without requiring labeled data from target domains.

Main Methods:

• Proposed a Multi-path Atrous Convolutional Network (MACN) as a robust baseline for ECG classification.
• Introduced Cluster-aligning loss to harmonize data distributions between source and target domains.
• Implemented Cluster-separating loss to enhance feature discriminability for improved classification.
• Utilized a short period of unlabeled data from new records for adaptation.

Main Results:

• The proposed unsupervised domain adaptation scheme significantly enhanced the baseline MACN model's performance.
• The method demonstrated effective alignment of training and test data distributions.
• Achieved competitive performance compared to existing state-of-the-art methods on the MIT-BIH database.
• Validated the effectiveness of the approach requiring only unlabeled data for adaptation.

Conclusions:

• The novel unsupervised domain adaptation scheme successfully tackles inter-patient variability in ECG signals.
• The combination of MACN, Cluster-aligning loss, and Cluster-separating loss offers a powerful solution for generalized ECG arrhythmia diagnosis.
• This approach provides a practical method for adapting ECG classification models to new clinical settings with minimal data requirements.