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Daniel Guldenring

Showing results (21-30 of 33) with videos related to

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Heart Rhythm|November 9, 2025
Accounting for inconclusive results and repeated testing: A framework for evaluating wearable electrocardiogram diagnostic performance with application to Apple Watch and artificial intelligence-enhanced interpretationPeter Doggart, Caitlin Fisher, Pardis Biglarbeigi, et al.
Journal of Electrocardiology|September 19, 2015
Data analysis of diagnostic accuracies in 12-lead electrocardiogram interpretation by junior medical fellowsTomas Novotny, Raymond Robert Bond, Irena Andrsova, et al.
Journal of Electrocardiology|September 15, 2017
A decision support system and rule-based algorithm to augment the human interpretation of the 12-lead electrocardiogramAndrew W Cairns, Raymond R Bond, Dewar D Finlay, et al.
Journal of Electrocardiology|September 25, 2016
Human factors analysis of the CardioQuick Patch®: A novel engineering solution to the problem of electrode misplacement during 12-lead electrocardiogram acquisitionRaymond R Bond, Dewar D Finlay, James McLaughlin, et al.
Journal of Electrocardiology|September 22, 2021
Overview of featurization techniques used in traditional versus emerging deep learning-based algorithms for automated interpretation of the 12-lead ECGDewar Finlay, Raymond Bond, Michael Jennings, et al.
European Heart Journal. Acute Cardiovascular Care|September 28, 2016
Epicardial potentials computed from the body surface potential map using inverse electrocardiography and an individualised torso model improve sensitivity for acute myocardial infarction diagnosisMichael J Daly, Dewar D Finlay, Daniel Guldenring, et al.
Journal of Electrocardiology|September 1, 2020
Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysisKhaled Rjoob, Raymond Bond, Dewar Finlay, et al.
Journal of Biomedical Informatics|October 1, 2016
A computer-human interaction model to improve the diagnostic accuracy and clinical decision-making during 12-lead electrocardiogram interpretationAndrew W Cairns, Raymond R Bond, Dewar D Finlay, et al.
Journal of Electrocardiology|September 3, 2019
Data driven feature selection and machine learning to detect misplaced V1 and V2 chest electrodes when recording the 12‑lead electrocardiogramKhaled Rjoob, Raymond Bond, Dewar Finlay, et al.
JMIR Medical Informatics|April 16, 2021
Reliable Deep Learning-Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and ValidationKhaled Rjoob, Raymond Bond, Dewar Finlay, et al.
Pageof 4

Showing results (21-30 of 33) with videos related to

Sort By:
Pageof 4
Heart Rhythm|November 9, 2025
Accounting for inconclusive results and repeated testing: A framework for evaluating wearable electrocardiogram diagnostic performance with application to Apple Watch and artificial intelligence-enhanced interpretationPeter Doggart, Caitlin Fisher, Pardis Biglarbeigi, et al.
Journal of Electrocardiology|September 19, 2015
Data analysis of diagnostic accuracies in 12-lead electrocardiogram interpretation by junior medical fellowsTomas Novotny, Raymond Robert Bond, Irena Andrsova, et al.
Journal of Electrocardiology|September 15, 2017
A decision support system and rule-based algorithm to augment the human interpretation of the 12-lead electrocardiogramAndrew W Cairns, Raymond R Bond, Dewar D Finlay, et al.
Journal of Electrocardiology|September 25, 2016
Human factors analysis of the CardioQuick Patch®: A novel engineering solution to the problem of electrode misplacement during 12-lead electrocardiogram acquisitionRaymond R Bond, Dewar D Finlay, James McLaughlin, et al.
Journal of Electrocardiology|September 22, 2021
Overview of featurization techniques used in traditional versus emerging deep learning-based algorithms for automated interpretation of the 12-lead ECGDewar Finlay, Raymond Bond, Michael Jennings, et al.
European Heart Journal. Acute Cardiovascular Care|September 28, 2016
Epicardial potentials computed from the body surface potential map using inverse electrocardiography and an individualised torso model improve sensitivity for acute myocardial infarction diagnosisMichael J Daly, Dewar D Finlay, Daniel Guldenring, et al.
Journal of Electrocardiology|September 1, 2020
Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysisKhaled Rjoob, Raymond Bond, Dewar Finlay, et al.
Journal of Biomedical Informatics|October 1, 2016
A computer-human interaction model to improve the diagnostic accuracy and clinical decision-making during 12-lead electrocardiogram interpretationAndrew W Cairns, Raymond R Bond, Dewar D Finlay, et al.
Journal of Electrocardiology|September 3, 2019
Data driven feature selection and machine learning to detect misplaced V1 and V2 chest electrodes when recording the 12‑lead electrocardiogramKhaled Rjoob, Raymond Bond, Dewar Finlay, et al.
JMIR Medical Informatics|April 16, 2021
Reliable Deep Learning-Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and ValidationKhaled Rjoob, Raymond Bond, Dewar Finlay, et al.
Pageof 4