Self-DANA: A Resource-Efficient Channel-Adaptive Self-Supervised Approach for ECG Foundation Models

TL;DR

Self-DANA is a resource-efficient, self-supervised method that adapts ECG foundation models to reduced-channel configurations, improving efficiency and robustness with novel augmentation techniques.

Contribution

It introduces Self-DANA, a novel approach for adapting ECG foundation models to fewer channels, and proposes Random Lead Selection for more robust pre-training.

Findings

Significantly reduces CPU and GPU memory usage.

Achieves state-of-the-art performance on reduced-channel ECG tasks.

Enhances resource efficiency by up to 69.3% in peak CPU memory.

Abstract

Foundation Models (FMs) are large-scale machine learning models trained on extensive, diverse datasets that can be adapted to a wide range of downstream tasks with minimal fine-tuning. In the last two years, interest in FMs has also grown for applications in the cardiological field to analyze the electrocardiogram (ECG) signals. One of the key properties of FMs is their transferability to a wide range of downstream scenarios. With the spread of wearable and portable devices, keen interest in learning from reduced-channel configurations has arisen. However, the adaptation of ECG FMs to downstream scenarios with fewer available channels still has to be properly investigated. In this work, we propose Self-DANA, a novel, easy-to-integrate solution that makes self-supervised architectures adaptable to a reduced number of input channels, ensuring resource efficiency and high performance. We also introduce Random Lead Selection, a novel augmentation technique to pre-train models in a more robust and channel-agnostic way. Our experimental results on five reduced-channel configurations demonstrate that Self-DANA significantly enhances resource efficiency while reaching state-of-the-art performance. It requires up to 69.3% less peak CPU memory, 34.4% less peak GPU memory, about 17% less average epoch CPU time, and about 24% less average epoch GPU time.