From High-SNR Radar Signal to ECG: A Transfer Learning Model with Cardio-Focusing Algorithm for Scenarios with Limited Data

TL;DR

This paper introduces a transfer learning approach with a cardio-focusing algorithm to accurately recover ECG signals from radar data in scenarios with limited training data, enhancing applicability in new environments.

Contribution

It proposes a novel cardio-focusing and transfer learning framework that requires minimal radar-ECG pairs for effective ECG recovery in data-scarce scenarios.

Findings

CFT dynamically tracks cardiac location.

RFcardi accurately recovers ECG with few training pairs.

Model performs well in limited-data environments.

Abstract

Electrocardiogram (ECG), as a crucial find-grained cardiac feature, has been successfully recovered from radar signals in the literature, but the performance heavily relies on the high-quality radar signal and numerous radar-ECG pairs for training, restricting the applications in new scenarios due to data scarcity. Therefore, this work will focus on radar-based ECG recovery in new scenarios with limited data and propose a cardio-focusing and -tracking (CFT) algorithm to precisely track the cardiac location to ensure an efficient acquisition of high-quality radar signals. Furthermore, a transfer learning model (RFcardi) is proposed to extract cardio-related information from the radar signal without ECG ground truth based on the intrinsic sparsity of cardiac features, and only a few synchronous radar-ECG pairs are required to fine-tune the pre-trained model for the ECG recovery. The experimental results reveal that the proposed CFT can dynamically identify the cardiac location, and the RFcardi model can effectively generate faithful ECG recoveries after using a small number of radar-ECG pairs for training. The code and dataset are available after the publication.