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 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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2014-01, Vol.18 (1), p.193-204
Main Authors: Bono, Valentina, Mazomenos, Evangelos B., Taihai Chen, Rosengarten, James A., Acharyya, Amit, Maharatna, Koushik, Morgan, John M., Curzen, Nick
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Automated ECG processing
Automation
Databases, Factual
electrocardiography
Electrocardiography - classification
Electrocardiography - methods
Heart - physiopathology
Humans
myocardial scar
Reproducibility of Results
Selvester QRS score
Severity of Illness Index
stationary wavelet transform (SWT)
Wavelet Analysis
Wavelet transforms
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2013.2263311