{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T17:05:47Z","timestamp":1784739947434,"version":"3.55.0"},"reference-count":31,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.compbiomed.2026.111824","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T16:35:14Z","timestamp":1782750914000},"page":"111824","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Assessing the robustness of evaluation metrics for synthetic ECG signal quality"],"prefix":"10.1016","volume":"213","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9482-4566","authenticated-orcid":false,"given":"Maria","family":"Russo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8488-256X","authenticated-orcid":false,"given":"In\u00eas","family":"Sousa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4478-2476","authenticated-orcid":false,"given":"Ricardo","family":"Santos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joana","family":"Rebelo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabiana","family":"Clemente","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4416-6902","authenticated-orcid":false,"given":"Gon\u00e7alo","family":"Ribeiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9","family":"Carreiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9445-4809","authenticated-orcid":false,"given":"Mar\u00edlia","family":"Barandas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compbiomed.2026.111824_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.cosrev.2023.100546","article-title":"Synthetic data generation: state of the art in health care domain","volume":"48","author":"Murtaza","year":"2023","journal-title":"Comput. Sci. Rev."},{"issue":"3","key":"10.1016\/j.compbiomed.2026.111824_bib0010","doi-asserted-by":"crossref","first-page":"305","DOI":"10.14778\/3632093.3632097","article-title":"TSGBench: time series generation benchmark","volume":"17","author":"Ang","year":"2023","journal-title":"Proc. VLDB Endow."},{"key":"10.1016\/j.compbiomed.2026.111824_bib0015","series-title":"Advances in Neural Information Processing Systems, 37","first-page":"129042","article-title":"TSGM: a flexible framework for generative modeling of synthetic time series","author":"Nikitin","year":"2024"},{"issue":"1","key":"10.1016\/j.compbiomed.2026.111824_bib0020","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/s40537-024-00924-7","article-title":"Evaluation is key: a survey on evaluation measures for synthetic time series","volume":"11","author":"Stenger","year":"2024","journal-title":"J. Big Data"},{"key":"10.1016\/j.compbiomed.2026.111824_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.110879","article-title":"Advancing electrocardiogram synthesis: analyzing key metrics for enhanced evaluation","volume":"196","author":"Wang","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2026.111824_bib0030","series-title":"Thirty-Seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track","article-title":"Synthcity: a benchmark framework for diverse use cases of tabular synthetic data","author":"Qian","year":"2023"},{"key":"10.1016\/j.compbiomed.2026.111824_bib0035","author":"Stenger"},{"issue":"1","key":"10.1016\/j.compbiomed.2026.111824_bib0040","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-020-0495-6","article-title":"PTB-XL, a large publicly available electrocardiography dataset","volume":"7","author":"Wagner","year":"2020","journal-title":"Sci. Data"},{"issue":"23","key":"10.1016\/j.compbiomed.2026.111824_bib0045","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"10.1016\/j.compbiomed.2026.111824_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109453","article-title":"Synthetic ECG signals generation: a scoping review","volume":"184","author":"Zanchi","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2026.111824_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107115","article-title":"Diffusion-based conditional ECG generation with structured state space models","volume":"163","author":"Alcaraz","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"10.1016\/j.compbiomed.2026.111824_bib0060","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-01295-2","article-title":"DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine","volume":"11","author":"Thambawita","year":"2021","journal-title":"Sci. Rep."},{"issue":"Suppl 5","key":"10.1016\/j.compbiomed.2026.111824_bib0065","doi-asserted-by":"crossref","first-page":"S453","DOI":"10.3103\/S0005105525701407","article-title":"Conditional electrocardiogram generation using hierarchical variational autoencoders","volume":"59","author":"Sviridov","year":"2025","journal-title":"Automatic Documentation and Mathematical Linguistics"},{"issue":"5","key":"10.1016\/j.compbiomed.2026.111824_bib0070","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/JBHI.2020.3022989","article-title":"Deep learning for ECG analysis: benchmarks and insights from PTB-XL","volume":"25","author":"Strodthoff","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.compbiomed.2026.111824_bib0075","article-title":"Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models","volume":"36","author":"Stein","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.compbiomed.2026.111824_bib0080","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1186\/s12874-020-00977-1","article-title":"Generation and evaluation of synthetic patient data","volume":"20","author":"Goncalves","year":"2020","journal-title":"BMC Med. Res. Methodol."},{"key":"10.1016\/j.compbiomed.2026.111824_bib0085","article-title":"Modeling tabular data using conditional GAN","volume":"32","author":"Xu","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2026.111824_bib0090","series-title":"International Conference on Machine Learning","first-page":"17564","article-title":"Tabddpm: modelling tabular data with diffusion models","author":"Kotelnikov","year":"2023"},{"issue":"7","key":"10.1016\/j.compbiomed.2026.111824_bib0095","doi-asserted-by":"crossref","first-page":"4136","DOI":"10.3390\/app13074136","article-title":"Qualitative and quantitative evaluation of multivariate time-series synthetic data generated using MTS-tgan: a novel approach","volume":"13","author":"Yadav","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.compbiomed.2026.111824_bib0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.softx.2025.102256","article-title":"pyMDMA: multimodal data metrics for auditing real and synthetic datasets","volume":"31","author":"Fa\u00e7oco","year":"2025","journal-title":"Softwarex"},{"key":"10.1016\/j.compbiomed.2026.111824_bib0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.softx.2020.100456","article-title":"TSFEL: time series feature extraction library","volume":"11","author":"Barandas","year":"2020","journal-title":"Softwarex"},{"key":"10.1016\/j.compbiomed.2026.111824_bib0110","article-title":"Improved precision and recall metric for assessing generative models","volume":"32","author":"Kynk\u00e4\u00e4nniemi","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2026.111824_bib0115","series-title":"International Conference on Machine Learning","first-page":"7176","article-title":"Reliable fidelity and diversity metrics for generative models","author":"Naeem","year":"2020"},{"key":"10.1016\/j.compbiomed.2026.111824_bib0120","series-title":"International Conference on Machine Learning","first-page":"290","article-title":"How faithful is your synthetic data? Sample-level metrics for evaluating and auditing generative models","author":"Alaa","year":"2022"},{"key":"10.1016\/j.compbiomed.2026.111824_bib0125","series-title":"2024 Computing in Cardiology (CinC)","article-title":"A survey of augmentation techniques for enhancing ECG representation through self-supervised contrastive learning","author":"Dade","year":"2024"},{"issue":"11","key":"10.1016\/j.compbiomed.2026.111824_bib0130","doi-asserted-by":"crossref","first-page":"5237","DOI":"10.3390\/s23115237","article-title":"A systematic survey of data augmentation of ECG signals for AI applications","volume":"23","author":"Rahman","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.compbiomed.2026.111824_bib0135","series-title":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","first-page":"1004","article-title":"Efficient time series augmentation methods","author":"Liu","year":"2020"},{"key":"10.1016\/j.compbiomed.2026.111824_bib0140","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"2491","article-title":"Rhiots: a framework for evaluating hierarchical time series forecasting algorithms","author":"Roque","year":"2024"},{"issue":"6","key":"10.1016\/j.compbiomed.2026.111824_bib0145","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1016\/j.jelectrocard.2015.08.034","article-title":"Frequency content and characteristics of ventricular conduction","volume":"48","author":"Tereshchenko","year":"2015","journal-title":"J. Electrocardiol."},{"issue":"1","key":"10.1016\/j.compbiomed.2026.111824_bib0150","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1038\/s43586-024-00363-x","article-title":"Uniform manifold approximation and projection","volume":"4","author":"Healy","year":"2024","journal-title":"Nat. Rev. Methods Primers"},{"key":"10.1016\/j.compbiomed.2026.111824_bib0155","series-title":"Deep learning for ECG analysis: benchmarks and insights from PTB-XL","author":"Gitau","year":"2025"}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482526003884?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482526003884?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:47:26Z","timestamp":1784738846000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482526003884"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":31,"alternative-id":["S0010482526003884"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2026.111824","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Assessing the robustness of evaluation metrics for synthetic ECG signal quality","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2026.111824","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"111824"}}