dc.contributor.author |
Peng, Tommy |
|
dc.contributor.author |
Malik, Avinash |
|
dc.contributor.author |
Trew, Mark L |
|
dc.date.accessioned |
2021-08-06T02:56:54Z |
|
dc.date.available |
2021-08-06T02:56:54Z |
|
dc.date.issued |
2021-7-1 |
|
dc.identifier.citation |
Biomedical Signal Processing and Control 68:102712 01 Jul 2021 |
|
dc.identifier.issn |
1746-8094 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/55881 |
|
dc.description.abstract |
Altered electrocardiogram (ECG) morphology is important for assessing cardiac pro-arrhythmic risk of drugs. We propose a basis function method to predict morphological and QT, JT, Tpeak—Tend timing interval changes in ECGs due to drug effects. The method systematically decomposes ECGs for a study population recorded at varying pharmacokinetic states into Gaussian Mesa Functions (GMFs). The GMF parameters are then fit to linear mixed effects drug sensitivity models. For a new subject, post-drug GMF parameter values and ECG morphology at varying pharmacokinetic states are predicted from the pre-drug GMF parameter values using the drug sensitivity models. The proposed methodology is validated with clinical ECGs of human subjects administered Dofetilide, Quinidine, Ranolazine, and Verapamil. The datasets are obtained from the ECGRVDQ database. The proposed method predicts post-drug timing intervals not significantly different to expert annotated intervals (pair-wise t-test p>0.05) for 153 out of 180 scenarios (drug type, hours post-dose, and ECG timing interval combinations). A comparative method based on expert annotations predicts 105 out of 180 scenarios. Importantly, realistic predictions of post-dose ECG morphology are reconstructed from predicted GMF parameters. Our study suggests that the GMF parameter space can provide important new biomarkers for assessing and visualizing drug-induced changes in ECGs. |
|
dc.language |
en |
|
dc.publisher |
Elsevier BV |
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dc.relation.ispartofseries |
Biomedical Signal Processing and Control |
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dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
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dc.subject |
Science & Technology |
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dc.subject |
Technology |
|
dc.subject |
Engineering, Biomedical |
|
dc.subject |
Engineering |
|
dc.subject |
Electrocardiogram decomposition |
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dc.subject |
Pro-arrhythmic risk |
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dc.subject |
Gaussian Mesa Functions |
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dc.subject |
Electrocardiogram prediction |
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dc.subject |
INDUCED QT PROLONGATION |
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dc.subject |
T-WAVE MORPHOLOGY |
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dc.subject |
DYNAMICAL MODEL |
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dc.subject |
ARRHYTHMIAS |
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dc.subject |
RANOLAZINE |
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dc.subject |
QUINIDINE |
|
dc.subject |
SIGNALS |
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dc.subject |
0903 Biomedical Engineering |
|
dc.subject |
0906 Electrical and Electronic Engineering |
|
dc.subject |
1004 Medical Biotechnology |
|
dc.title |
Predicting drug-mediated pro-arrhythmic effects using pre-drug electrocardiograms |
|
dc.type |
Journal Article |
|
dc.identifier.doi |
10.1016/j.bspc.2021.102712 |
|
pubs.begin-page |
102712 |
|
pubs.volume |
68 |
|
dc.date.updated |
2021-07-27T02:56:20Z |
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dc.rights.holder |
Copyright: Elsevier Ltd. All rights reserved. |
en |
pubs.author-url |
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000670368300007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e41486220adb198d0efde5a3b153e7d |
|
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Article |
|
pubs.subtype |
Journal |
|
pubs.elements-id |
853163 |
|
dc.identifier.eissn |
1746-8108 |
|
pubs.number |
102712 |
|