Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/24237
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Mathunjwa, BM | - |
dc.contributor.author | Lin, YT | - |
dc.contributor.author | Lin, CH | - |
dc.contributor.author | Abbod, MF | - |
dc.contributor.author | Sadrawi, M | - |
dc.contributor.author | Shieh, JS | - |
dc.date.accessioned | 2022-03-13T12:21:13Z | - |
dc.date.available | 2022-03-13T12:21:13Z | - |
dc.date.issued | 2022-02-20 | - |
dc.identifier | 1660 | - |
dc.identifier.citation | Mathunjwa, B.M., Lin, Y.T., Lin, C.H., Abbod, M.F., Sadrawi, M. and Shieh, J.S. (2022) ‘ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features’, Sensors, 22 (4), 1660, p. 1-26. doi:10.3390/s22041660. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/24237 | - |
dc.description | Data Availability Statement: This study utilizes the publicly available dataset, from https:// physionet.org, accessed on 22 June 2020. | - |
dc.description.abstract | Copyright: © 2022 by the authors. In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were de-tected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhyth-mia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance. | en_US |
dc.description.sponsorship | Ministry of Science and Technology, Taiwan (grant number: MOST 110-2221-E-155-004-MY2). | - |
dc.format.extent | 1 - 26 | - |
dc.format.medium | Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | electrocardiogram | en_US |
dc.subject | arrhythmia | en_US |
dc.subject | recurrence plot | en_US |
dc.subject | deep residual convolutional neural network | en_US |
dc.title | ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/s22041660 | - |
dc.relation.isPartOf | Sensors | - |
pubs.issue | 4 | - |
pubs.publication-status | Published | - |
pubs.volume | 22 | - |
dc.identifier.eissn | 1424-8220 | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | 3.06 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License