The University of Southampton
University of Southampton Institutional Repository

A Time-domain morphology and gradient based algorithm for ECG feature extraction

A Time-domain morphology and gradient based algorithm for ECG feature extraction
A Time-domain morphology and gradient based algorithm for ECG feature extraction
A Time Domain Morphology and Gradient (TDMG) based algorithm is presented in this paper for the extraction of all the fiducial time instances from a single PQRST complex. By estimating these characteristic points, all clinically important temporal ECG parameters can be calculated. The proposed algorithm is based on a combination of extrema detection and slope information, with the use of adaptive thresholding to achieve the extraction of 11 time instances. A pre-processing step removes any noise and artefacts from the captured ECG signal. Initially, the position of the R-wave and the QRS-complex boundaries are localized in time. Following, by focusing on the part of the signal that precedes and succeeds the QRS-complex, the remaining fiducial points from the P and T waves are estimated. The initial localisation of the wave boundaries is complimented by amendment steps which are introduced to cater for atypical wave morphologies, indicative of particular heart conditions. The proposed algorithm is evaluated on the QT and PTB databases against medically annotated ECG samples. The results demonstrate the ability of the proposed scheme, to estimate the ECG fiducial points with acceptable accuracy from a single-lead ECG signal. In addition, this investigation reveals the ability of the TDMG algorithm to perform accurately irrespective of the lead chosen, the different disease categories and the sampling frequency of the captured ECG signal.
117-122
Mazomenos, Evangelos
23983827-c7e7-4ee1-bfc8-986aa3594279
Chen, Taihai
62b1db38-757b-4250-8b48-de4e47f09d9e
Acharyya, Amit
f7c95a87-04ac-4d13-a74c-0c4d89b1c79c
Bhattacharya, Arnab
264c8746-e81f-4c4e-8c46-c84cd170c288
Rosengarten, James
3ccf8397-ca9e-4b04-864f-5c2515db8965
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Mazomenos, Evangelos
23983827-c7e7-4ee1-bfc8-986aa3594279
Chen, Taihai
62b1db38-757b-4250-8b48-de4e47f09d9e
Acharyya, Amit
f7c95a87-04ac-4d13-a74c-0c4d89b1c79c
Bhattacharya, Arnab
264c8746-e81f-4c4e-8c46-c84cd170c288
Rosengarten, James
3ccf8397-ca9e-4b04-864f-5c2515db8965
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Mazomenos, Evangelos, Chen, Taihai, Acharyya, Amit, Bhattacharya, Arnab, Rosengarten, James and Maharatna, Koushik (2012) A Time-domain morphology and gradient based algorithm for ECG feature extraction. IEEE International Conference on Industrial Technology, Greece. pp. 117-122 . (doi:10.1109/ICIT.2012.6209924).

Record type: Conference or Workshop Item (Paper)

Abstract

A Time Domain Morphology and Gradient (TDMG) based algorithm is presented in this paper for the extraction of all the fiducial time instances from a single PQRST complex. By estimating these characteristic points, all clinically important temporal ECG parameters can be calculated. The proposed algorithm is based on a combination of extrema detection and slope information, with the use of adaptive thresholding to achieve the extraction of 11 time instances. A pre-processing step removes any noise and artefacts from the captured ECG signal. Initially, the position of the R-wave and the QRS-complex boundaries are localized in time. Following, by focusing on the part of the signal that precedes and succeeds the QRS-complex, the remaining fiducial points from the P and T waves are estimated. The initial localisation of the wave boundaries is complimented by amendment steps which are introduced to cater for atypical wave morphologies, indicative of particular heart conditions. The proposed algorithm is evaluated on the QT and PTB databases against medically annotated ECG samples. The results demonstrate the ability of the proposed scheme, to estimate the ECG fiducial points with acceptable accuracy from a single-lead ECG signal. In addition, this investigation reveals the ability of the TDMG algorithm to perform accurately irrespective of the lead chosen, the different disease categories and the sampling frequency of the captured ECG signal.

Text
paperKC-003107_mazomenos.pdf - Other
Download (382kB)

More information

Published date: March 2012
Venue - Dates: IEEE International Conference on Industrial Technology, Greece, 2012-03-01
Organisations: Faculty of Health Sciences, Electronic & Software Systems

Identifiers

Local EPrints ID: 340407
URI: https://eprints.soton.ac.uk/id/eprint/340407
PURE UUID: 3764f358-c258-458d-b1f2-832026b475dd

Catalogue record

Date deposited: 21 Jun 2012 11:02
Last modified: 06 Aug 2019 18:55

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×