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

An automated algorithm for online detection of fragmented QRS and identification of its various morphologies
An automated algorithm for online detection of fragmented QRS and identification of its various morphologies
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 complex is extracted and discrete wavelet transform (DWT) with one level of decomposition, using the ‘Haar’ wavelet, is applied on it to detect the presence of fragmentation. Detailed DWT coefficients were observed to hypothesize the postulates of detection of all types of morphologies as reported in the literature. To model and verify the algorithm, PhysioNet's PTB database was used. Forty patients were randomly selected from the database and their ECG were examined by two experienced cardiologists and the results were compared with those obtained from the algorithm. Out of 40 patients, 31 were considered appropriate for comparison by two cardiologists, and it is shown that 334 out of 372 (89.8%) leads from the chosen 31 patients complied favourably with our proposed algorithm. The sensitivity and specificity values obtained for the detection of f-QRS were 0.897 and 0.899, respectively. Automation will speed up the detection of fragmentation, reducing the human error involved and will allow it to be implemented for hospital-based remote monitoring and ICD devices.
electrocardiography, fragmented qrs, wavelet transform
1742-5689
20130761-[16pp]
Maheshwari, Sidharth
f2178955-359d-4531-8005-3c907bb8ddf2
Acharyya, Amit
f7c95a87-04ac-4d13-a74c-0c4d89b1c79c
Puddu, Paolo Emilio
4daadd4b-cc61-49e6-954c-0107300b7025
Mazomenos, Evangelos B.
23983827-c7e7-4ee1-bfc8-986aa3594279
Leekha, Gourav
3a8b3ec6-88fe-4397-965f-f09f06a16881
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Schiariti, Michele
9c870d3b-8a07-4d64-91f1-67e5ab33686f
Maheshwari, Sidharth
f2178955-359d-4531-8005-3c907bb8ddf2
Acharyya, Amit
f7c95a87-04ac-4d13-a74c-0c4d89b1c79c
Puddu, Paolo Emilio
4daadd4b-cc61-49e6-954c-0107300b7025
Mazomenos, Evangelos B.
23983827-c7e7-4ee1-bfc8-986aa3594279
Leekha, Gourav
3a8b3ec6-88fe-4397-965f-f09f06a16881
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Schiariti, Michele
9c870d3b-8a07-4d64-91f1-67e5ab33686f

Maheshwari, Sidharth, Acharyya, Amit, Puddu, Paolo Emilio, Mazomenos, Evangelos B., Leekha, Gourav, Maharatna, Koushik and Schiariti, Michele (2013) An automated algorithm for online detection of fragmented QRS and identification of its various morphologies. Journal of the Royal Society Interface, 10 (89), 20130761-[16pp]. (doi:10.1098/rsif.2013.0761).

Record type: Article

Abstract

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 complex is extracted and discrete wavelet transform (DWT) with one level of decomposition, using the ‘Haar’ wavelet, is applied on it to detect the presence of fragmentation. Detailed DWT coefficients were observed to hypothesize the postulates of detection of all types of morphologies as reported in the literature. To model and verify the algorithm, PhysioNet's PTB database was used. Forty patients were randomly selected from the database and their ECG were examined by two experienced cardiologists and the results were compared with those obtained from the algorithm. Out of 40 patients, 31 were considered appropriate for comparison by two cardiologists, and it is shown that 334 out of 372 (89.8%) leads from the chosen 31 patients complied favourably with our proposed algorithm. The sensitivity and specificity values obtained for the detection of f-QRS were 0.897 and 0.899, respectively. Automation will speed up the detection of fragmentation, reducing the human error involved and will allow it to be implemented for hospital-based remote monitoring and ICD devices.

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e-pub ahead of print date: 16 October 2013
Keywords: electrocardiography, fragmented qrs, wavelet transform
Organisations: Electronic & Software Systems

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Local EPrints ID: 358997
URI: https://eprints.soton.ac.uk/id/eprint/358997
ISSN: 1742-5689
PURE UUID: c1ceceba-81d7-4e1a-8287-3984510693eb

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Date deposited: 18 Oct 2013 09:13
Last modified: 19 Jul 2019 21:24

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Contributors

Author: Sidharth Maheshwari
Author: Amit Acharyya
Author: Paolo Emilio Puddu
Author: Evangelos B. Mazomenos
Author: Gourav Leekha
Author: Michele Schiariti

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