Transfer learning for ECG classification.

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Healthcare
• Cardiology

Background:

• Remote monitoring devices generate vast electrocardiogram (ECG) data for patients with heart arrhythmia.
• Physician interpretation of this extensive ECG data is time-consuming and presents a bottleneck.
• Automated ECG interpretation methods are crucial for efficient clinical decision-making.

Purpose of the Study:

• To develop reliable methods for automatic ECG interpretation using deep convolutional neural networks (CNNs).
• To address the challenge of limited annotated data for training CNNs in ECG classification.
• To leverage transfer learning to improve the performance and reduce annotation requirements for ECG analysis.

Main Methods:

• Utilized deep convolutional neural networks (CNNs) for raw ECG recording classification.
• Employed transfer learning by pretraining CNNs on a large public dataset of continuous raw ECG signals.
• Finetuned pretrained CNNs on a smaller dataset for Atrial Fibrillation classification, investigating both supervised and unsupervised pretraining.

Main Results:

• Pretraining CNNs improved performance on the target task (Atrial Fibrillation classification) by up to [Formula: see text].
• Transfer learning effectively reduced the number of required annotations to achieve comparable performance.
• Both supervised and unsupervised pretraining approaches demonstrated significant benefits.

Conclusions:

• Transfer learning is a viable and effective strategy for training CNNs for ECG classification, especially when annotated data is scarce.
• Pretraining on large, unlabeled ECG datasets can significantly enhance diagnostic accuracy for arrhythmias like Atrial Fibrillation.
• The proposed approach offers a scalable solution for automated ECG interpretation, supporting clinical workflows.