{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T11:07:05Z","timestamp":1779102425881,"version":"3.51.4"},"reference-count":45,"publisher":"IOP Publishing","issue":"5","license":[{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2026,5,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . Cardiovascular diseases remain the leading cause of death worldwide, highlighting the need for non-invasive and cost-effective risk assessment tools. Biological systems, including the heart, exhibit complex nonlinear dynamics arising from interactions between their subsystems. Information-theoretic measures, particularly entropy-based methods, provide a framework to quantify these interactions. Using ECG recordings, we investigate information flow between heart rhythm and ventricular repolarization to identify potential markers of pathological alterations in cardiac electrical activity.\n                    <jats:italic>Approach<\/jats:italic>\n                    . Entropy-based measures of information transfer were derived from beat-to-beat ECG time series using a window-based approach and subsequently averaged at the subject level. These features were used as inputs to supervised machine learning models to discriminate patients with congenital long QT syndrome (LQTS) from healthy controls. Model performance was evaluated using repeated stratified train-test splits, and classification robustness was assessed across multiple runs using standard performance metrics, including the area under the receiver operating characteristic curve. The explainable artificial intelligence techniques were applied. SHapley Additive exPlanations were used to quantify the contribution of entropy-based features to the model predictions. This post-hoc explainability analysis enabled systematic assessment of feature importance while preserving the predictive performance of the models.\n                    <jats:italic>Results<\/jats:italic>\n                    . The proposed approach achieved high and stable classification performance across repeated validation runs. Both random forest (RF) and support vector machine (SVM) classifiers demonstrated high discrimination between LQTS patients and healthy controls, with consistently high area under the curve. For RF a mean accuracy of 95.9%, mean sensitivity of 95.9%, and mean specificity of 92.9% were achieved across repeated runs. For SVM the corresponding mean values were 93.1%, 93.1%, and 92.0%, respectively.\n                    <jats:italic>Conclusions.<\/jats:italic>\n                    Explainability analysis revealed a dominant contribution of multivariate and conditional information flow features compared with single-source entropy measures, highlighting the relevance of joint and conditional interactions in the classification patterns.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ae6969","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T22:53:21Z","timestamp":1778108001000},"page":"055015","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based information flow analysis of ECG signals for long QT syndrome"],"prefix":"10.1088","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9315-0942","authenticated-orcid":true,"given":"Mateusz","family":"Ozimek","sequence":"first","affiliation":[{"name":"Warsaw University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2322-6833","authenticated-orcid":false,"given":"Ma\u0142gorzata","family":"Andrzejewska-Ozimek","sequence":"additional","affiliation":[{"name":"Warsaw University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8051-0577","authenticated-orcid":true,"given":"Monika","family":"Petelczyc","sequence":"additional","affiliation":[{"name":"Warsaw University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"pmeaae6969bib1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103584","type":"journal-article","article-title":"Explainable AI decision model for ECG data of cardiac disorders","volume":"75","author":"Anand","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"pmeaae6969bib2","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1186\/s12916-022-02350-z","type":"journal-article","article-title":"A deep learning approach identifies new ECG features in congenital long QT syndrome","volume":"20","author":"Aufiero","year":"2022","journal-title":"BMC Med."},{"key":"pmeaae6969bib3","doi-asserted-by":"publisher","first-page":"155285","DOI":"10.1109\/ACCESS.2025.3603848","type":"journal-article","article-title":"Complexity measures in biomedical signal analysis: a clinically-grounded survey across EEG, ECG, intracranial pressure, and photoplethysmogram modalities","volume":"13","author":"Balasubramanian","year":"2025","journal-title":"IEEE Access"},{"key":"pmeaae6969bib4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0093808","type":"journal-article","article-title":"Multiscale complexity analysis of the cardiac control identifies asymptomatic and symptomatic patients in long QT syndrome type 1","volume":"9","author":"Bari","year":"2014","journal-title":"PLoS One"},{"key":"pmeaae6969bib5","doi-asserted-by":"publisher","first-page":"1312","DOI":"10.1016\/j.amjcard.2004.07.123","type":"journal-article","article-title":"Temporal repolarization lability differences among genotyped patients with the long QT syndrome","volume":"94","author":"Bilchick","year":"2004","journal-title":"Am. J. Cardiol."},{"key":"pmeaae6969bib6","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1001\/jamacardio.2020.7422","type":"journal-article","article-title":"Use of artificial intelligence and deep neural networks in evaluation of patients with electrocardiographically concealed long QT syndrome from the surface 12-lead electrocardiogram","volume":"6","author":"Bos","year":"2021","journal-title":"JAMA Cardiol."},{"key":"pmeaae6969bib7","first-page":"536","type":"journal-article","article-title":"On the theory of filter amplifiers","volume":"7","author":"Butterworth","year":"1930","journal-title":"Exp. Wirel. Wirel. Eng."},{"key":"pmeaae6969bib8","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1016\/j.jelectrocard.2012.08.001","type":"journal-article","article-title":"The telemetric and Holter ECG warehouse (THEW): the first three years of development and research","volume":"45","author":"Couderc","year":"2012","journal-title":"J. Electrocardiol."},{"key":"pmeaae6969bib9","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1080\/01621459.1979.10482531","type":"journal-article","article-title":"Distribution of the estimators for autoregressive time series with a unit root","volume":"74","author":"Dickey","year":"1979","journal-title":"J. Am. Stat. Assoc."},{"key":"pmeaae6969bib10","type":"web-resource","article-title":"Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root","volume":"49","author":"Dickey","year":"1981"},{"key":"pmeaae6969bib11","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.3390\/jpm12071135","type":"journal-article","article-title":"Detection of patients with congenital and often concealed long-QT syndrome by novel deep learning models","volume":"12","author":"Doldi","year":"2022","journal-title":"J. Pers. Med."},{"key":"pmeaae6969bib12","doi-asserted-by":"publisher","DOI":"10.1063\/1.5115506","type":"journal-article","article-title":"Comparison of methods for the assessment of nonlinearity in short-term heart rate variability under different physiopathological states","volume":"29","author":"Faes","year":"2019","journal-title":"Chaos"},{"key":"pmeaae6969bib13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fphys.2011.00080","type":"journal-article","article-title":"Information domain approach to the investigation of cardio-vascular, cardio-pulmonary, and vasculo-pulmonary causal couplings","volume":"2","author":"Faes","year":"2011","journal-title":"Front. Physiol."},{"key":"pmeaae6969bib14","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.compbiomed.2011.02.007","type":"journal-article","article-title":"Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series","volume":"42","author":"Faes","year":"2012","journal-title":"Comput. Biol. Med."},{"key":"pmeaae6969bib15","doi-asserted-by":"publisher","first-page":"277","DOI":"10.3390\/e17010277","type":"journal-article","article-title":"Information decomposition in bivariate systems: theory and application to cardiorespiratory dynamics","volume":"17","author":"Faes","year":"2015","journal-title":"Entropy"},{"key":"pmeaae6969bib16","doi-asserted-by":"publisher","first-page":"5","DOI":"10.3390\/e19010005","type":"journal-article","article-title":"Information decomposition in multivariate systems: definitions, implementation and application to cardiovascular networks","volume":"19","author":"Faes","year":"2017","journal-title":"Entropy"},{"key":"pmeaae6969bib17","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1093\/eurjpc\/zwac081","type":"journal-article","article-title":"The influence of exercise and postural changes on ventricular repolarization in the long QT syndrome: a systematic scoping review","volume":"29","author":"Harvey","year":"2022","journal-title":"Eur. J. Prev. Cardiol."},{"key":"pmeaae6969bib18","doi-asserted-by":"publisher","first-page":"1296","DOI":"10.1093\/europace\/euq184","type":"journal-article","article-title":"Abnormal repolarization dynamics revealed in exercise test in long QT syndrome mutation carriers with normal resting QT interval","volume":"12","author":"Hekkala","year":"2010","journal-title":"Europace"},{"key":"pmeaae6969bib19","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1016\/j.hrthm.2019.12.020","type":"journal-article","article-title":"Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization","volume":"17","author":"Hermans","year":"2020","journal-title":"Heart Rhythm"},{"key":"pmeaae6969bib20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0184352","type":"journal-article","article-title":"The development and validation of an easy to use automatic QT-interval algorithm","volume":"12","author":"Hermans","year":"2017","journal-title":"PLoS One"},{"key":"pmeaae6969bib21","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1093\/eurheartj\/ehl355","type":"journal-article","article-title":"Diagnostic criteria for congenital long QT syndrome in the era of molecular genetics: do we need a scoring system?","volume":"28","author":"Hofman","year":"2006","journal-title":"Eur. Heart J."},{"key":"pmeaae6969bib22","doi-asserted-by":"publisher","DOI":"10.3389\/fnetp.2021.711778","type":"journal-article","article-title":"The new field of network physiology: building the human physiolome","volume":"1","author":"Ivanov","year":"2021","journal-title":"Front. Netw. Physiol."},{"key":"pmeaae6969bib23","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1080\/03091902.2019.1640306","type":"journal-article","article-title":"A robust algorithm for heart rate variability time series artefact correction using novel beat classification","volume":"43","author":"Lipponen","year":"2019","journal-title":"J. Med. Eng. Technol."},{"key":"pmeaae6969bib24","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":"pmeaae6969bib25","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1136\/heart.87.3.220","type":"journal-article","article-title":"Relation between QT and RR intervals is highly individual among healthy subjects: implications for heart rate correction of the QT interval","volume":"87","author":"Malik","year":"2002","journal-title":"Heart"},{"key":"pmeaae6969bib26","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1111\/j.1540-8159.2009.02179.x","type":"journal-article","article-title":"QT interval variability and adaptation to heart rate changes in patients with long QT syndrome","volume":"32","author":"N\u011bmec","year":"2009","journal-title":"Pacing Clin. Electrophysiol."},{"key":"pmeaae6969bib27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fphys.2021.611731","type":"journal-article","article-title":"Information flow between heart rhythm, repolarization, and the diastolic interval series for healthy individuals and LQTS1 patients","volume":"12","author":"Ozimek","year":"2021","journal-title":"Front. Physiol."},{"key":"pmeaae6969bib28","doi-asserted-by":"publisher","first-page":"148","DOI":"10.3389\/fphys.2012.00148","type":"journal-article","article-title":"Role of editing of R-R intervals in the analysis of heart rate variability","volume":"3","author":"Peltola","year":"2012","journal-title":"Front. Physiol."},{"key":"pmeaae6969bib29","doi-asserted-by":"crossref","first-page":"287","DOI":"10.2174\/1573403X10666140514103612","type":"journal-article","article-title":"The measurement of the QT interval","volume":"10","author":"Postema","year":"2014","journal-title":"Curr. Cardiol. Rev."},{"key":"pmeaae6969bib30","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.mcpdig.2023.11.003","type":"journal-article","article-title":"Artificial intelligence in diagnosis of long QT syndrome: a review of current state, challenges, and future perspectives","volume":"2","author":"Raissi Dehkordi","year":"2024","journal-title":"Mayo Clin. Proc."},{"key":"pmeaae6969bib31","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2023.1097468","type":"journal-article","article-title":"Individualized QT interval (QTi) is a powerful diagnostic tool in long QT syndrome: results from a large validation study","volume":"10","author":"Robyns","year":"2023","journal-title":"Front. Cardiovasc. Med."},{"key":"pmeaae6969bib32","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1161\/01.CIR.88.2.782","type":"journal-article","article-title":"Diagnostic criteria for the long QT syndrome. An update","volume":"88","author":"Schwartz","year":"1993","journal-title":"Circulation"},{"key":"pmeaae6969bib33","doi-asserted-by":"publisher","DOI":"10.1063\/1.3675622","type":"journal-article","article-title":"Fractal variability: an emergent property of complex dissipative systems","volume":"22","author":"Seely","year":"2012","journal-title":"Chaos"},{"key":"pmeaae6969bib34","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1536\/ihj.21-094","type":"journal-article","article-title":"Explainable artificial intelligence model for diagnosis of atrial fibrillation using holter electrocardiogram waveforms","volume":"62","author":"Taniguchi","year":"2021","journal-title":"Int. Heart J."},{"key":"pmeaae6969bib35","article-title":"Congential long QT syndrome\u2014E-HOL-03-0480-013","author":"University of Rochester Medical Center","year":"2012a","type":"web-resource"},{"key":"pmeaae6969bib36","article-title":"Healthy Individuals\u2014E-HOL-03-0202-003","author":"University of Rochester Medical Center","year":"2012b","type":"web-resource"},{"key":"pmeaae6969bib37","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/S0002-9149(01)02339-6","type":"journal-article","article-title":"Differentiation between LQT1 and LQT2 patients and unaffected subjects using 24-hour electrocardiographic recordings","volume":"89","author":"Viitasalo","year":"2002","journal-title":"Am. J. Cardiol."},{"key":"pmeaae6969bib38","article-title":"Explaining deep learning for ECG analysis: building blocks for auditing and knowledge discovery","author":"Wagner","year":"2023","type":"preprint"},{"key":"pmeaae6969bib39","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1136\/heartjnl-2020-318259","type":"journal-article","article-title":"Diagnosis, management and therapeutic strategies for congenital long QT syndrome","volume":"108","author":"Wilde","year":"2022","journal-title":"Heart"},{"key":"pmeaae6969bib40","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.yjmcc.2018.02.001","type":"journal-article","article-title":"Autonomic conflict exacerbates long QT associated ventricular arrhythmias","volume":"116","author":"Winter","year":"2018","journal-title":"J. Mol. Cell. Cardiol."},{"key":"pmeaae6969bib41","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1161\/CIRCEP.109.907865","type":"journal-article","article-title":"Utility of treadmill testing in identification and genotype prediction in long-QT syndrome","volume":"3","author":"Wong","year":"2010","journal-title":"Circ.: Arrhythmia Electrophysiol."},{"key":"pmeaae6969bib42","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2023.1172451","type":"journal-article","article-title":"The diagnostic value of electrocardiogram-based machine learning in long QT syndrome: a systematic review and meta-analysis","volume":"10","author":"Wu","year":"2023","journal-title":"Front. Cardiovasc. Med."},{"key":"pmeaae6969bib43","doi-asserted-by":"publisher","DOI":"10.1111\/anec.12878","type":"journal-article","article-title":"Differential diagnosis between LQT1 and LQT2 by QT\/RR relationships using 24\u2010hour Holter monitoring: a multicenter cross\u2010sectional study","volume":"26","author":"Yodogawa","year":"2021","journal-title":"Ann. Noninvasive Electrocardiol."},{"key":"pmeaae6969bib44","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1111\/j.1542-474X.2005.10205.x","type":"journal-article","article-title":"QT dynamics and variability","volume":"10","author":"Zareba","year":"2005","journal-title":"Ann. Noninvasive Electrocardiol."},{"key":"pmeaae6969bib45","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1016\/j.hroo.2024.07.006","type":"journal-article","article-title":"From genes to clinical management: a comprehensive review of long QT syndrome pathogenesis and treatment","volume":"5","author":"Zhu","year":"2024","journal-title":"Heart Rhythm O2"}],"container-title":["Physiological Measurement"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae6969","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae6969\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae6969","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae6969\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae6969\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae6969\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae6969\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae6969\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T10:08:47Z","timestamp":1779098927000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae6969"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,18]]},"references-count":45,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5,18]]},"published-print":{"date-parts":[[2026,5,29]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6579\/ae6969","relation":{},"ISSN":["0967-3334","1361-6579"],"issn-type":[{"value":"0967-3334","type":"print"},{"value":"1361-6579","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,18]]},"assertion":[{"value":"Machine learning-based information flow analysis of ECG signals for long QT syndrome","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":"2026-01-19","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-05-06","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-05-18","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}