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Development of an automated updated selvester QRS scoring system using SWT-based QRS fractionation detection and classification

Development of an automated updated selvester QRS scoring system using SWT-based QRS fractionation detection and classification
Development of an automated updated selvester QRS scoring system using SWT-based QRS fractionation detection and classification
The Selvester score is an effective means for estimating the extent of myocardial scar in a patient from lowcost 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 article 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.
2168-2194
193-204
Bono, Valentina
1cb487d9-7af0-421b-8207-a0e785e0c9dd
Mazomenos, Evangelos B.
23983827-c7e7-4ee1-bfc8-986aa3594279
Chen, Taihai
62b1db38-757b-4250-8b48-de4e47f09d9e
Rosengarten, James
3ccf8397-ca9e-4b04-864f-5c2515db8965
Acharyya, Amit
f7c95a87-04ac-4d13-a74c-0c4d89b1c79c
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Morgan, John M.
ac98099e-241d-4551-bc98-709f6dfc8680
Curzen, Nick
70f3ea49-51b1-418f-8e56-8210aef1abf4
Bono, Valentina
1cb487d9-7af0-421b-8207-a0e785e0c9dd
Mazomenos, Evangelos B.
23983827-c7e7-4ee1-bfc8-986aa3594279
Chen, Taihai
62b1db38-757b-4250-8b48-de4e47f09d9e
Rosengarten, James
3ccf8397-ca9e-4b04-864f-5c2515db8965
Acharyya, Amit
f7c95a87-04ac-4d13-a74c-0c4d89b1c79c
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Morgan, John M.
ac98099e-241d-4551-bc98-709f6dfc8680
Curzen, Nick
70f3ea49-51b1-418f-8e56-8210aef1abf4

Bono, Valentina, Mazomenos, Evangelos B., Chen, Taihai, Rosengarten, James, Acharyya, Amit, Maharatna, Koushik, Morgan, John M. and Curzen, Nick (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), 193-204. (doi:10.1109/JBHI.2013.2263311).

Record type: Article

Abstract

The Selvester score is an effective means for estimating the extent of myocardial scar in a patient from lowcost 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 article 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.

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e-pub ahead of print date: 15 May 2013
Published date: January 2014
Organisations: Electronics & Computer Science

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Local EPrints ID: 353070
URI: https://eprints.soton.ac.uk/id/eprint/353070
ISSN: 2168-2194
PURE UUID: b54eddb7-934a-473f-b84d-730e4a862a34

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Date deposited: 29 May 2013 10:42
Last modified: 18 Nov 2019 20:38

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Contributors

Author: Valentina Bono
Author: Evangelos B. Mazomenos
Author: Taihai Chen
Author: James Rosengarten
Author: Amit Acharyya
Author: Koushik Maharatna
Author: John M. Morgan
Author: Nick Curzen

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