Inter-Patient ECG Classification with Convolutional and Recurrent Neural Networks
- Submitting institution
-
University of Central Lancashire
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 28379
- Type
- D - Journal article
- DOI
-
10.1016/j.bbe.2019.06.001
- Title of journal
- Journal of Biocybernetics and Biomedical Engineering
- Article number
- -
- First page
- 868
- Volume
- 39
- Issue
- 3
- ISSN
- 0208-5216
- Open access status
- Compliant
- Month of publication
- July
- 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
- Yes
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In this work, we use a deep learning approach for ECG classification. The results obtained show that without applying any complicated feature engineering methods, our models have considerably outperformed the state-of-the-art performance for supraventricular (SVEB) and ventricular (VEB) arrhythmia classifications datasets. This work can be considered as a more generic approach for dealing with scenarios in which varieties of ECG signals are collected from different patients using different types of sensor devices. It has generated many citations since it has been published in 2019 and has attracted interest from researchers from all over the world.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -