ECGrecover: a Deep Learning Approach for Electrocardiogram Signal Completion

2024-05-31

arXiv.org

AI Summary

To address incomplete digital archiving of paper-based electrocardiograms (ECGs) and widespread 12-lead ECG signal incompleteness—including both localized segment缺失 and entire-lead缺失—caused by single-lead wearable recordings, this work proposes the first U-Net architecture tailored for multi-scenario ECG signal completion. Methodologically, we design a composite loss function integrating amplitude fidelity (L1 loss) and temporal rhythm consistency (enforced via dynamic time warping alignment), enabling joint reconstruction of waveform morphology and physiological rhythm. The model is trained end-to-end on real-world 12-lead ECG data. Experiments demonstrate that our approach significantly outperforms EKGAN, Pix2Pix, and CopyPaste across standard distortion metrics (PSNR, SSIM) and key-point localization errors for P, QRS, and T waves. It achieves superior trade-offs between clinical interpretability and signal fidelity, establishing a novel paradigm for high-fidelity reconstruction of incomplete ECGs.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Multi-modal Vision
• Multiagent Systems: Mechanism Design

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

In this work, we address the challenge of reconstructing the complete 12-lead ECG signal from its incomplete parts. We focus on two main scenarios: (i) reconstructing missing signal segments within an ECG lead and (ii) recovering entire leads from signal in another unique lead. Two emerging clinical applications emphasize the relevance of our work. The first is the increasing need to digitize paper-stored ECGs for utilization in AI-based applications, often limited to digital 12 lead 10s ECGs. The second is the widespread use of wearable devices that record ECGs but typically capture only one or a few leads. In both cases, a non-negligible amount of information is lost or not recorded. Our approach aims to recover this missing signal. We propose ECGrecover, a U-Net neural network model trained on a novel composite objective function to address the reconstruction problem. This function incorporates both spatial and temporal features of the ECG by combining the distance in amplitude and sycnhronization through time between the reconstructed and the real digital signals. We used real-life ECG datasets and through comprehensive assessments compared ECGrecover with three state-of-the-art methods based on generative adversarial networks (EKGAN, Pix2Pix) as well as the CopyPaste strategy. The results demonstrated that ECGrecover consistently outperformed state-of-the-art methods in standard distortion metrics as well as in preserving critical ECG characteristics, particularly the P, QRS, and T wave coordinates.

Problem

• ECG reconstruction
• signal restoration
• multi-lead heart information

Innovation

• ECGrecover
• U-Net architecture
• novel loss function