RETRACTED: Machine learning based pervasive analytics for ECG signal analysis
International Journal of Pervasive Computing and Communications
ISSN: 1742-7371
Article publication date: 29 July 2021
Issue publication date: 4 January 2024
Retraction statement
The publishers of International Journal of Pervasive Computing and Communications wish to retract the article S., A. and S., V. (2024), “Machine learning based pervasive analytics for ECG signal analysis”, International Journal of Pervasive Computing and Communications, Vol. 20 No. 1, pp. 1–18. https://doi.org/10.1108/IJPCC-03-2021-0080
An internal investigation into a series of submissions has uncovered evidence that the peer review process was compromised. As a result of these concerns, the findings of the article cannot be relied upon. This decision has been taken in accordance with Emerald’s publishing ethics and the COPE guidelines on retractions. The authors of this paper would like to note that they do not agree with the content of this notice.
The publishers of the journal sincerely apologize to the readers.
The retracted article is available at: https://doi.org/10.1108/IJPCC-03-2021-0080
Abstract
Purpose
Pervasive analytics act as a prominent role in computer-aided prediction of non-communicating diseases. In the early stage, arrhythmia diagnosis detection helps prevent the cause of death suddenly owing to heart failure or heart stroke. The arrhythmia scope can be identified by electrocardiogram (ECG) report.
Design/methodology/approach
The ECG report has been used extensively by several clinical experts. However, diagnosis accuracy has been dependent on clinical experience. For the prediction methods of computer-aided heart disease, both accuracy and sensitivity metrics play a remarkable part. Hence, the existing research contributions have optimized the machine-learning approaches to have a great significance in computer-aided methods, which perform predictive analysis of arrhythmia detection.
Findings
In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.
Originality/value
In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.
Keywords
Citation
S., A. and S., V. (2024), "RETRACTED: Machine learning based pervasive analytics for ECG signal analysis", International Journal of Pervasive Computing and Communications, Vol. 20 No. 1, pp. 1-18. https://doi.org/10.1108/IJPCC-03-2021-0080
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited