Lence A. auteur aut IRD Granese F. auteur aut IRD Fall Ahmad auteur aut IRD Hanczar B. auteur aut IRD Salem J.E. auteur aut IRD Zucker Jean-Daniel auteur aut IRD Prifti Edi auteur aut IRD text bookSection eng text/pdf born digital access 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. specialized 122INTAR Sun Y. ed. edt IRD Chierichetti F. ed. edt IRD Lauw H.W. ed. edt IRD Perlich C. ed. edt IRD Tok W. ed. edt IRD Tomkins A. ed. edt IRD 2359-2370 New York Association for Computing Machinery 2025 ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 31., Toronto (CAN), 2025/08/03-07 https://www.documentation.ird.fr/hor/fdi:010095087 10.1145/3690624.3709405 979-8-4007-1245-6 [F B010095087] https://www.documentation.ird.fr/hor/fdi:010095087 https://www.documentation.ird.fr/intranet/publi/2025-09/010095087.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2025-09-25 2025-12-15 fdi:010095087 fre