Output details
15 - General Engineering
University of Oxford
Article title
Automated QT Analysis That Learns from Cardiologist Annotations
Type
D - Journal article
Title of journal
ANNALS OF NONINVASIVE ELECTROCARDIOLOGY
Article number
-
Volume number
14
Issue number
-
First page of article
S9
ISSN of journal
1082-720X
Year of publication
2009
Number of additional authors
4
Additional information
The Food and Drug Administration (FDA) and European regulatory agencies require all new drugs to be evaluated in cardiac safety (Phase 1) studies. This paper describes a semi-supervised machine learning algorithm to quantify timing and shape changes in the electrocardiogram (ECG) waveform. The intellectual property (IP) described in the paper was protected by patent applications which have resulted in three granted US patents (US7941209, US8290575 and US8332017). It represents the core of the BioQT commercial system, which is now regularly used by pharmaceutical companies in their submissions to the FDA
(see http://www.obsmedical.com/products/bioqt).
Interdisciplinary
-
Cross-referral requested
-
Research group
E - Biomedical Engineering
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
