Bono, V, Mazomenos, EB orcid.org/0000-0003-0357-5996, Chen, T et al. (5 more authors) (2014) Development of an Automated Updated Selvester QRS Scoring System Using SWT-Based QRS Fractionation Detection and Classification. IEEE Journal of Biomedical and Health Informatics, 18 (1). pp. 193-204. ISSN 2168-2194
Abstract
The Selvester score is an effective means for estimating the extent of myocardial scar in a patient from low-cost ECG recordings. Automation of such a system is deemed to help implementing low-cost high-volume screening mechanisms of scar in the primary care. This paper describes, for the first time to the best of our knowledge, an automated implementation of the updated Selvester scoring system for that purpose, where fractionated QRS morphologies and patterns are identified and classified using a novel stationary wavelet transform (SWT)-based fractionation detection algorithm. This stage informs the two principal steps of the updated Selvester scoring scheme-the confounder classification and the point awarding rules. The complete system is validated on 51 ECG records of patients detected with ischemic heart disease. Validation has been carried out using manually detected confounder classes and computation of the actual score by expert cardiologists as the ground truth. Our results show that as a stand-alone system it is able to classify different confounders with 94.1% accuracy whereas it exhibits 94% accuracy in computing the actual score. When coupled with our previously proposed automated ECG delineation algorithm, that provides the input ECG parameters, the overall system shows 90% accuracy in confounder classification and 92% accuracy in computing the actual score and thereby showing comparable performance to the stand-alone system proposed here, with the added advantage of complete automated analysis without any human intervention.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Automated ECG processing, electrocardiography, myocardial scar, Selvester QRS score, stationary wavelet transform (SWT) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Nov 2022 12:10 |
Last Modified: | 23 Nov 2022 12:36 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/jbhi.2013.2263311 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160486 |