Saeed Shakibfar1, Claus Graff2, Jørgen K Kanters3,4,5, Jimmi Nielsen6, Samuel Schmidt2, Johannes J Struijk2. 1. Center for Sensory Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark. 2. Medical Informatics Group (MI), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark. 3. Laboratory of Experimental Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark. 4. Department of Cardiology, Herlev & Gentofte University Hospitals, Copenhagen, Denmark. 5. Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark. 6. Center for Schizophrenia, Aalborg Psychiatric Hospital, Aalborg University Hospital, Aalborg, Denmark.
Abstract
BACKGROUND: Recently, numerous models and techniques have been developed for analyzing and extracting features from the T wave which could be used as biomarkers for drug-induced abnormalities. The majority of these techniques and algorithms use features that determine readily apparent characteristics of the T wave, such as duration, area, amplitude, and slopes. METHODS: In the present work the T wave was down-sampled to a minimal rate, such that a good reconstruction was still possible. The entire T wave was then used as a feature vector to assess drug-induced repolarization effects. The ability of the samples or combinations of samples obtained from the minimal T-wave representation to correctly classify a group of subjects before and after receiving d,l-sotalol 160 mg and 320 mg was evaluated using a linear discriminant analysis (LDA). RESULTS: The results showed that a combination of eight samples from the minimal T-wave representation can be used to identify normal from abnormal repolarization significantly better compared to the heart rate-corrected QT interval (QTc). It was further indicated that the interval from the peak of the T wave to the end of the T wave (Tpe) becomes relatively shorter after IKr inhibition by d,l-sotalol and that the most pronounced repolarization changes were present in the ascending segment of the minimal T-wave representation. CONCLUSIONS: The minimal T-wave representation can potentially be used as a new tool to identify normal from abnormal repolarization in drug safety studies.
BACKGROUND: Recently, numerous models and techniques have been developed for analyzing and extracting features from the T wave which could be used as biomarkers for drug-induced abnormalities. The majority of these techniques and algorithms use features that determine readily apparent characteristics of the T wave, such as duration, area, amplitude, and slopes. METHODS: In the present work the T wave was down-sampled to a minimal rate, such that a good reconstruction was still possible. The entire T wave was then used as a feature vector to assess drug-induced repolarization effects. The ability of the samples or combinations of samples obtained from the minimal T-wave representation to correctly classify a group of subjects before and after receiving d,l-sotalol 160 mg and 320 mg was evaluated using a linear discriminant analysis (LDA). RESULTS: The results showed that a combination of eight samples from the minimal T-wave representation can be used to identify normal from abnormal repolarization significantly better compared to the heart rate-corrected QT interval (QTc). It was further indicated that the interval from the peak of the T wave to the end of the T wave (Tpe) becomes relatively shorter after IKr inhibition by d,l-sotalol and that the most pronounced repolarization changes were present in the ascending segment of the minimal T-wave representation. CONCLUSIONS: The minimal T-wave representation can potentially be used as a new tool to identify normal from abnormal repolarization in drug safety studies.
Authors: Mads P Andersen; Joel Q Xue; Claus Graff; Jørgen K Kanters; Egon Toft; Johannes J Struijk Journal: J Electrocardiol Date: 2008-09-19 Impact factor: 1.438
Authors: Christina Abrahamsson; Corina Dota; Bo Skallefell; Leif Carlsson; Dunia Halawani; Lars Frison; Anders Berggren; Nils Edvardsson; Göran Duker Journal: J Electrocardiol Date: 2011 Jul-Aug Impact factor: 1.438
Authors: J J Struijk; J K Kanters; M P Andersen; T Hardahl; C Graff; M Christiansen; E Toft Journal: Med Biol Eng Comput Date: 2006-06-03 Impact factor: 2.602
Authors: L Johannesen; J Vicente; R A Gray; L Galeotti; Z Loring; C E Garnett; J Florian; M Ugander; N Stockbridge; D G Strauss Journal: Clin Pharmacol Ther Date: 2013-12-12 Impact factor: 6.875
Authors: F E Cruz Filho; I G Maia; M L Fagundes; R C Barbosa; P A Alves; R M Sá; S H Boghossian; J C Ribeiro Journal: J Am Coll Cardiol Date: 2000-07 Impact factor: 24.094
Authors: J Nielsen; M P Andersen; C Graff; J K Kanters; T Hardahl; J Dybbro; J J Struijk; J M Meyer; E Toft Journal: Acta Psychiatr Scand Date: 2010-01-19 Impact factor: 6.392
Authors: L Johannesen; J Vicente; J W Mason; C Erato; C Sanabria; K Waite-Labott; M Hong; J Lin; P Guo; A Mutlib; J Wang; W J Crumb; K Blinova; D Chan; J Stohlman; J Florian; M Ugander; N Stockbridge; D G Strauss Journal: Clin Pharmacol Ther Date: 2015-11-28 Impact factor: 6.875
Authors: Nenad Sarapa; Joel Morganroth; Jean-Philippe Couderc; Steven F Francom; Borje Darpo; Joseph C Fleishaker; Janet D McEnroe; William T Chen; Wojciech Zareba; Arthur J Moss Journal: Ann Noninvasive Electrocardiol Date: 2004-01 Impact factor: 1.468
Authors: Claus Graff; Mads P Andersen; Joel Q Xue; Thomas B Hardahl; Jørgen K Kanters; Egon Toft; Michael Christiansen; Henrik K Jensen; Johannes J Struijk Journal: Drug Saf Date: 2009 Impact factor: 5.606