{"doi":"10.3389/fdgth.2022.1090854","title":"PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods","abstract":"There has been a growing interest in developing cuff-less blood pressure (BP) estimation methods to enable continuous BP monitoring from electrocardiogram (ECG) and/or photoplethysmogram (PPG) signals. The majority of these methods have been evaluated using publicly-available datasets, however, there exist significant discrepancies across studies with respect to the size, the number of subjects, and the applied pre-processing steps for the data that is eventually used for training and testing the models. Such differences make conducting performance comparison across models largely unfair, and mask the generalization capability of various BP estimation methods. To fill this important gap, this paper presents “PulseDB,” the largest cleaned dataset to date, for benchmarking BP estimation models that also fulfills the requirements of standardized testing protocols. PulseDB contains 1) 5,245,454 high-quality <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"IM1\"><mml:mn>10</mml:mn></mml:math> -s segments of ECG, PPG, and arterial BP (ABP) waveforms from 5,361 subjects retrieved from the MIMIC-III waveform database matched subset and the VitalDB database; 2) subjects’ identification and demographic information, that can be utilized as additional input features to improve the performance of BP estimation models, or to evaluate the generalizability of the models to data from unseen subjects; and 3) positions of the characteristic points of the ECG/PPG signals, making PulseDB directly usable for training deep learning models with minimal data pre-processing. Additionally, using this dataset, we conduct the first study to provide insights about the performance gap between calibration-based and calibration-free testing approaches for evaluating generalizability of the BP estimation models. We expect PulseDB, as a user-friendly, large, comprehensive and multi-functional dataset, to be used as a reliable source for the evaluation of cuff-less BP estimation methods.","journal":"Frontiers in Digital Health","year":2023,"id":317016,"datarank":2.332589530874089,"base_score":4.465908118654584,"endowment":4.465908118654584,"self_citation_contribution":0.6698862177981877,"citation_network_contribution":1.6627033130759012,"self_endowment_contribution":0.6698862177981877,"citer_contribution":1.6627033130759012,"corpus_percentile":90.87181867409298,"corpus_rank":1181,"citation_count":86,"citer_count":81,"citers_with_citation_signal":47,"citers_with_endowment":47,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.9167,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":66.6667,"fair_percentile":86.48731274839498,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":680236,"name":"Pedram Mohseni","orcid":"0000-0002-2849-4677","position":1,"is_corresponding":false},{"id":812563,"name":"Kevin L. Kilgore","orcid":"0000-0002-2642-9855","position":2,"is_corresponding":false},{"id":425820,"name":"Laleh Najafizadeh","orcid":"0000-0002-6658-4112","position":3,"is_corresponding":false},{"id":999160,"name":"Weinan Wang","orcid":"0000-0002-2412-4978","position":0,"is_corresponding":true}],"reference_count":52,"raw_metadata":null,"created_at":"2026-07-19T01:06:44.698773Z","pmid":"36844249","pmcid":"PMC9944565","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":50.0,"fair_a":62.5,"fair_i":40.0,"fair_r":66.6667,"fair_zscore":1.2752,"fair_rationale":{"fair_score":66.67,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":50.0,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"10.34740/KAGGLE/DS/2447469","grounded":false,"rationale":"The paper provides a DOI for the dataset via the Kaggle page reference. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (4/5 passes agreed)]","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"These subsets are available for download from Kaggle at (61), and can be reproduced from the script provided in the GitHub repository at (34).","grounded":false,"rationale":"Kaggle is named as a data repository holding the dataset. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (4/5 passes agreed)]","anchors":["RDA-F4-01M — FAIR Data Maturity Model: metadata is offered so it can be harvested and indexed (","NIH DMS Policy Element 4 (NOT-OD-21-014) — name the repository where data will be archived","NSTC Desirable Characteristics of Data Repositories (2022) — 'Long-Term Sustainability', 'Reten"],"scored":true,"signal":null},{"key":"f_data_availability_statement","label":"Data-availability statement","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.","grounded":false,"rationale":"The data availability statement points to the article and supplementary material, not to a repository record. 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[majority verdict 'no' (3/5 passes agreed)]","anchors":["RDA-R1.3-01M — 'Metadata complies with a community standard' (priority Essential)","RDA-R1.3-01D — 'Data complies with a community standard'","RDA-I2-01M — '(Meta)data use vocabularies that follow FAIR principles'"],"scored":false,"signal":null},{"key":"i_qualified_references","label":"Identifiers for the resources the data depend on","kind":"llm","weight":0.5,"fraction":1.0,"verdict":"yes","evidence":"10.13026/c2294b","grounded":true,"rationale":"The paper includes a DOI for the MIMIC-III Waveform Database Matched Subset, a source dataset. 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A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"10.34740/KAGGLE/DS/2447469","why":"The paper provides a DOI for the dataset via the Kaggle page reference. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (4/5 passes agreed)]","gain":8.33,"priority":"essential","scored":true},{"key":"f_repository_named","dimension":"F","label":"Named repository","action":"Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. 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Cite the neuroimaging repository accession (e.g. from OpenNeuro or NeuroVault) in the reference list.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"[Dataset] Wang W, Mohseni P, Kilgore KL, Najafizadeh L. PulseDB- Kaggle Page (2022). Available from: https://doi.org/10.34740/KAGGLE/DS/2447469","why":"The dataset appears as a reference-list entry with a DOI. [downgraded to 'partial' — no verifiable quote from the paper]","gain":4.17,"priority":"important","scored":true},{"key":"i_open_nonproprietary_format","dimension":"I","label":"Open file format","action":"Release the data in an open, community-standard format (CSV/TSV, JSON, HDF5, NetCDF, FASTQ, VCF, NIfTI…) instead of — or alongside — any proprietary or instrument-native format, and name the format in the paper. A dataset that needs a €2,000 licence to open is not reusable. 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It is the cheapest single thing that makes a dataset usable by someone who was not in the lab, and a table buried in the article does not travel with the data.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"PulseDB contains 1) 5,245,454 high-quality 10-s segments of ECG, PPG, and arterial BP (ABP) waveforms from 5,361 subjects retrieved from the MIMIC-III waveform database matched subset and the VitalDB database; 2) subjects’ identification and demographic information, that can be utilized as additional input features to improve the performance of BP estimation models, or to evaluate the generalizability of the models to data from unseen subjects; and 3) positions of the characteristic points of the ECG/PPG signals","why":"The data's content is described in prose within the article, but no separate documentation object is named as accompanying the data. 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[majority verdict 'no' (4/5 passes agreed)]","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Mint or cite a persistent identifier for the dataset — a repository DOI or an accession from a registered repository — and print it in the paper. A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the neuroimaging repository accession (e.g. from OpenNeuro or NeuroVault) in the reference list.","Release the data in an open, community-standard format (CSV/TSV, JSON, HDF5, NetCDF, FASTQ, VCF, NIfTI…) instead of — or alongside — any proprietary or instrument-native format, and name the format in the paper. A dataset that needs a €2,000 licence to open is not reusable. Prefer open neuroimaging formats such as NIfTI or BIDS.","Publish the analysis code in a public forge, archive a tagged release with a DOI (Zenodo/Software Heritage), and cite that DOI in the paper. NIH DMS Element 2 asks for the tools and code, not only the data — and 'available on request' is not a locator. Archive the analysis code in a versioned repository (GitHub + a Zenodo release DOI)."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"unpaywall_pdf"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"unpaywall_pdf","fair_has_llm":true,"fair_computed_at":"2026-07-20T11:07:34.031719Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}