{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T11:34:14Z","timestamp":1793100854378,"version":"4.5.0"},"reference-count":19,"publisher":"IOP Publishing","issue":"10","license":[{"start":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T00:00:00Z","timestamp":1793059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T00:00:00Z","timestamp":1793059200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T00:00:00Z","timestamp":1793059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T00:00:00Z","timestamp":1793059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"funder":[{"name":"Natural Sciences and Engineering Research Council of Canada","award":["Discovery Grant"],"award-info":[{"award-number":["Discovery Grant"]}],"id":[{"id":"https:\/\/ror.org\/01h531d29","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2026,10,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . To develop an automated and customizable tool for physiological data extraction and validation that reduces reliance on manual assessment and improves efficiency and consistency in large physiological datasets.\n                    <jats:italic>Approach<\/jats:italic>\n                    . We developed PhysioMerge, a software platform designed to automate physiological signal extraction and analysis from recording systems such as LabChart. The tool implements user-defined validation checks to identify and flag anomalous signals while preserving valid data segments. PhysioMerge also supports waveform morphology analysis through extraction of features including peak prominence, curvature, and slopes. In addition, the platform enables flexible segmentation by time, intervention, or study phase and supports batch processing for large-scale datasets. PhysioMerge supports time based analysis and cycle based analysis (cardiac, respiratory, etc).\n                    <jats:italic>Main Results<\/jats:italic>\n                    . PhysioMerge streamlines physiological data processing by reducing the need for subjective manual validation of breaths and cardiac cycles. The platform reduced the time investment by 65x, and had comparable results to human evaluators. The platform enables more consistent signal validation, efficient handling of complex datasets, and high-throughput analysis across multiple recordings. Advanced morphology extraction provides complementary quantitative measures beyond conventional physiological analyses.\n                    <jats:italic>Significance<\/jats:italic>\n                    . By automating validation and feature extraction workflows, PhysioMerge reduces variability associated with human judgment and fatigue while improving reproducibility and scalability in physiological research. The platform provides researchers with a flexible framework for consistent and rapid analysis of large physiological datasets. Physiological research relies on accurate data extraction from recording systems such as LabChart. While these systems provide essential tools for data collection, processing large and complex datasets can remain time consuming and often requires manual validation of breaths or cardiac cycles. Manual validation introduces variability due to human judgment, fatigue, and the inability to reprocess large datasets efficiently. To streamline this process, we present PhysioMerge, an automated tool for robust physiological data extraction. PhysioMerge allows users to implement customizable checks to validate signals against predefined criteria, flag anomalies, and preserve valid segments, reducing the need for subjective manual assessment. The software also supports advanced waveform morphology analysis, extracting features such as peak prominence, curvature, and slopes that complement conventional analyses. Flexible data segmentation by time, intervention, or study phase, combined with batch processing capabilities, allows researchers to efficiently handle large datasets and perform consistent, high-precision analyses.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/aeafa8","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T22:53:29Z","timestamp":1790981609000},"page":"105015","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["PhysioMerge: a tool for physiological data extraction and analysis"],"prefix":"10.1088","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6050-4492","authenticated-orcid":false,"given":"Taha","family":"Abbasi-Hashemi","sequence":"first","affiliation":[{"name":"McMaster University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1075-4183","authenticated-orcid":true,"given":"Baraa K","family":"Al-Khazraji","sequence":"additional","affiliation":[{"name":"McMaster University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"266","published-online":{"date-parts":[[2026,10,27]]},"reference":[{"key":"pmeaaeafa8bib1","doi-asserted-by":"publisher","first-page":"1048","DOI":"10.1002\/lt.21499","type":"journal-article","article-title":"Noninvasive monitoring of cerebral perfusion pressure in patients with acute liver failure using transcranial doppler ultrasonography","volume":"14","author":"Aggarwal","year":"2008","journal-title":"Liver Transplant."},{"key":"pmeaaeafa8bib2","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1152\/japplphysiol.00328.2024","type":"journal-article","article-title":"Effects of single-and bilateral limb immersion on systemic and cerebral hemodynamic responses to the cold pressor test","volume":"137","author":"Allison","year":"2024","journal-title":"J. Appl. Physiol."},{"key":"pmeaaeafa8bib3","first-page":"B231","type":"journal-article","article-title":"Eeg correlates of task engagement and mental workload in vigilance, learning and memory tasks","volume":"78","author":"Berka","year":"2007","journal-title":"Aviat. Space Environ. Med."},{"key":"pmeaaeafa8bib4","author":"BioSPPY","year":"2026","type":"other"},{"key":"pmeaaeafa8bib5","author":"Elucimed","year":"2018","type":"other"},{"key":"pmeaaeafa8bib6","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1109\/TBME.2008.2008636","type":"journal-article","article-title":"Morphological clustering and analysis of continuous intracranial pressure","volume":"56","author":"Hu","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"pmeaaeafa8bib7","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/ab6360","type":"journal-article","article-title":"Forecasting intracranial hypertension using multi-scale waveform metrics","volume":"41","author":"H\u00fcser","year":"2020","journal-title":"Physiol. Meas."},{"key":"pmeaaeafa8bib8","author":"LabChart\u2014Life Science Data Acquisition Software | Data Analysis","type":"other"},{"key":"pmeaaeafa8bib9","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1152\/japplphysiol.00653.2021","type":"journal-article","article-title":"Reproducibility and diurnal variation of the directional sensitivity of the cerebral pressure-flow relationship in men and women","volume":"132","author":"Labrecque","year":"2022","journal-title":"J. Appl. Physiol."},{"key":"pmeaaeafa8bib10","doi-asserted-by":"publisher","first-page":"1689","DOI":"10.3758\/s13428-020-01516-y","type":"journal-article","article-title":"NeuroKit2: a python toolbox for neurophysiological signal processing","volume":"53","author":"Makowski","year":"2021","journal-title":"Behav. Res. Methods"},{"key":"pmeaaeafa8bib11","article-title":"Molecularneurobiology\/breathe_easy","author":"MolecularNeurobiology","year":"2025","type":"other"},{"key":"pmeaaeafa8bib12","doi-asserted-by":"publisher","first-page":"2414","DOI":"10.1177\/0271678X16667524","type":"journal-article","article-title":"Cerebral autoregulation and symptoms of orthostatic hypotension in familial dysautonomia","volume":"37","author":"Mora","year":"2017","journal-title":"J. Cereb. Blood Flow Metab."},{"key":"pmeaaeafa8bib13","doi-asserted-by":"publisher","first-page":"2491","DOI":"10.1109\/ICPR.2016.7900010","type":"conference-proceedings","article-title":"Detection of intracranial hypertension using deep learning","author":"Quachtran","year":"2016"},{"key":"pmeaaeafa8bib14","article-title":"RespirAct","year":"2024","type":"other"},{"key":"pmeaaeafa8bib15","doi-asserted-by":"publisher","first-page":"1462","DOI":"10.1364\/BOE.386612","type":"journal-article","article-title":"Estimating intracranial pressure using pulsatile cerebral blood flow measured with diffuse correlation spectroscopy","volume":"11","author":"Ruesch","year":"2020","journal-title":"Biomed. Opt. Express"},{"key":"pmeaaeafa8bib16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/23279095.2025.2469261","type":"journal-article","article-title":"Alterations in cerebral artery flow velocity acceleration pattern correlate with cognitive impairment in diabetes mellitus","author":"Tas","year":"2025","journal-title":"Appl. Neuropsychol.: Adult"},{"key":"pmeaaeafa8bib17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0228642","type":"journal-article","article-title":"Toward automated classification of pathological transcranial doppler waveform morphology via spectral clustering","volume":"15","author":"Thorpe","year":"2020","journal-title":"PLoS One"},{"key":"pmeaaeafa8bib18","doi-asserted-by":"publisher","first-page":"847","DOI":"10.3389\/fneur.2018.00847","type":"journal-article","article-title":"Decision criteria for large vessel occlusion using transcranial doppler waveform morphology","volume":"9","author":"Thorpe","year":"2018","journal-title":"Frontiers Neurol."},{"key":"pmeaaeafa8bib19","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1152\/jappl.2001.91.2.929","type":"journal-article","article-title":"Improved analysis of brachial artery ultrasound using a novel edge-detection software system","volume":"91","author":"Woodman","year":"2001","journal-title":"J. Appl. Physiol."}],"container-title":["Physiological Measurement"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/aeafa8","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/aeafa8\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/aeafa8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/aeafa8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/aeafa8\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/aeafa8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/aeafa8\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/aeafa8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T11:00:16Z","timestamp":1793098816000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/aeafa8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,27]]},"references-count":19,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,10,27]]},"published-print":{"date-parts":[[2026,10,30]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6579\/aeafa8","relation":{},"ISSN":["0967-3334","1361-6579"],"issn-type":[{"value":"0967-3334","type":"print"},{"value":"1361-6579","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,27]]},"assertion":[{"value":"PhysioMerge: a tool for physiological data extraction and analysis","name":"article_title","label":"Article Title"},{"value":"Physiological Measurement","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2026 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2026-02-26","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-10-02","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-10-27","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}