An automated algorithm for online detection of fragmented qrs and identification of its various morphologies

Maheshwari, S and Acharyya, Amit and Puddu, P E and Mazomenos, E B and Leekha, G and Maharatna, K and Schiariti, M (2013) An automated algorithm for online detection of fragmented qrs and identification of its various morphologies. Interface, 10 (89). pp. 1-18. ISSN 1742-5689

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Fragmented QRS (f-QRS) has been proven to be an efficient biomarker for several diseases, including remote and acute myocardial infarction, cardiac sarcoidosis, non-ischaemic cardiomyopathy, etc. It has also been shown to have higher sensitivity and/or specificity values than the conventional markers (e.g. Q-wave, ST-elevation, etc.) which may even regress or disappear with time. Patients with such diseases have to undergo expensive and sometimes invasive tests for diagnosis. Automated detection of f-QRS followed by identification of its various morphologies in addition to the conventional ECG feature (e.g. P, QRS, T amplitude and duration, etc.) extraction will lead to a more reliable diagnosis, therapy and disease prognosis than the state-of-the-art approaches and thereby will be of significant clinical importance for both hospital-based and emerging remote health monitoring environments as well as for implanted ICD devices. An automated algorithm for detection of f-QRS from the ECG and identification of its various morphologies is proposed in this work which, to the best of our knowledge, is the first work of its kind. Using our recently proposed time-domain morphology and gradient-based ECG feature extraction algorithm, the QRS. © 2013 The Author(s) Published by the Royal Society. All rights reserved.

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IITH Creators:
IITH CreatorsORCiD
Acharyya, Amit
Item Type: Article
Uncontrolled Keywords: Electrocardiography; Fragmented qrs; Wavelet transform
Subjects: Physics > Electricity and electronics
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 11 Nov 2014 10:40
Last Modified: 29 Aug 2017 10:56
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