Standard ECG Lead I Prospective Estimation Study from Far-field Bipolar Leads on the Left Upper Arm: A Neural Network Approach
- Submitting institution
-
University of Ulster
- Unit of assessment
- 12 - Engineering
- Output identifier
- 76489936
- Type
- D - Journal article
- DOI
-
10.1016/j.bspc.2019.01.020
- Title of journal
- Biomedical Signal Processing and Control
- Article number
- BSPC 1471
- First page
- 171
- Volume
- 51
- Issue
- -
- ISSN
- 1746-8094
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
A - Healthcare Sensor Systems
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In this international-led collaborative research study within the EU-WASTCArD project, a clinical multichannel arm-ECG mapping database was created (N=150). Using artificial-neural-network based estimation information analysis techniques, the feasibility for reconstructing the standard ECG Lead I from upper-left-arm bipolar ECG lead recordings was evidenced (similarity-ρ >80%). Also, the implemented information theory analytics revealed that there is an estimated information of 1.6 bits/beat between armband bipolar leads and limb Lead I. Thus, information metrics demonstrated the feasibility to estimate Lead I ECG waveforms from left-upper-arm bipolar leads (correlation >80%); hence the concept of heart rhythm monitoring from wireless-wearable armband ECG recording devices.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -