- Abstract:
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We set out to use machine learning techniques to analyse ECG data to improve risk evaluation of cardiovascular disease in a very large cohort study of the Chinese population. We performed this investigation by (i) detecting “abnormality” using 3 one-class classification methods, and (ii) predicting probabilities of “normality”, arrhythmia, ischemia, and hypertrophy using a multiclass approach. For one-class classification, we considered 5 possible definitions for “normality” and used 10 autom...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted manuscript
- Files:
-
-
(pdf, 737.9kb)
-
- Publisher copy:
- 10.1109/EMBC.2016.7591218
-
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- Grant:
- Digital Economy Programme grant EP/G036861/1
- Publisher:
- Institute of Electrical and Electronics Engineers Publisher's website
- Publication date:
- 2016-10-05
- Acceptance date:
- 2016-05-07
- DOI:
- URN:
-
uuid:2f6e4f59-9b0f-4758-b917-a000c7e04869
- Source identifiers:
-
624287
- Local pid:
- pubs:624287
- Copyright holder:
- Institute of Electrical and Electronics Engineers
- Copyright date:
- 2016
- Notes:
- ©2016 IEEE.
Conference
Risk prediction for cardiovascular disease using ECG data in the China Kadoorie Biobank
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+ Engineering and Physical Sciences Research Council
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China Scholarship Council
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Royal Academy of Engineering
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