Electrocardiogram stream level correlated patterns as features to classify heartbeats for arrhythmia prediction
Data Technologies and Applications
ISSN: 2514-9288
Article publication date: 27 October 2020
Issue publication date: 2 November 2020
Abstract
Purpose
Digital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are focusing on developing comprehensive models for such detection. Categorically in the proposed diagnosis for arrhythmia, which is a critical diagnosis to prevent cardiac-related deaths, any constructive models can be a value proposition. In this study, the focus is on developing a holistic system that predicts the scope of arrhythmia from the given electrocardiogram report. The proposed method is using the sequential patterns of the electrocardiogram elements as features.
Design/methodology/approach
Considering the decision accuracy of the contemporary classification methods, which is not adequate to use in clinical practices, this manuscript coined a new dimension of features to perform supervised learning and classification using the AdaBoost classifier. The proposed method has titled “Electrocardiogram stream level correlated patterns as features (ESCPFs),” which takes electrocardiograms (ECGs) signal streams as input records to perform supervised learning-based classification to detect the arrhythmia scope in given ECG record.
Findings
From the results and comparative reports generated for the study, it is evident that the model is performing with higher accuracy compared to some of the earlier models. However, focusing on the emerging solutions and technologies, if the accuracy factors for the model can be improved, it can lead to compelling predictions and accurate outcome from the process.
Originality/value
The authors represent complete automatic and rapid arrhythmia as classifier, which could be applied online and examine long ECG records sequence efficiently. By releasing the needs for extraction of features, the authors project an application based on raw signals, one result to heart rates date, whose objective is to lessen computation time when attaining minimum classification error outcomes.
Keywords
Acknowledgements
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the General Research Project under grant number (R.G.P1/155/40).
Citation
Al-Yarimi, F.A.M., Munassar, N.M.A. and Al-Wesabi, F.N. (2020), "Electrocardiogram stream level correlated patterns as features to classify heartbeats for arrhythmia prediction", Data Technologies and Applications, Vol. 54 No. 5, pp. 685-701. https://doi.org/10.1108/DTA-03-2020-0076
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited