Electrocardiogram Fiducial Points Detection and Estimation Methodology for Automatic Diagnose
René Yáñez de la Rivera1, Moisés Soto-Bajo2, *, Andrés Fraguela-Collar1
Identifiers and Pagination:Year: 2018
First Page: 208
Last Page: 230
Publisher Id: TOBIOIJ-11-208
Article History:Received Date: 18/6/2018
Revision Received Date: 6/8/2018
Acceptance Date: 10/8/2018
Electronic publication date: 28/09/2018
Collection year: 2018
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The estimation of fiducial points is specially important in the analysis and automatic diagnose of Electrocardiographic (ECG) signals.
A new algorithm which could be easily implemented is presented to accomplish this task.
Its methodology is rather simple, and starts from some ideas available in the literature combined with new approachs provided by the authors. First, a QRS complex detection algorithm is presented based on the computation of energy maxima in ECG signals which allow the measurement of cardiac frequency (in beats per minute) and the estimation of R peaks temporal positions (in number of samples). From these ones, an estimation of fiducial points Q, S, J, P and T waves onset and offset points are worked out, supported in a simple modified slope method with constraints.
The location process of fiducial points is assisted with the help of the so called curvature filters, which allow to improve the accuracy in this task.
The procedure is simulated in Matlab and GNU Octave by using test signals from the MIT medical database, Cardiosim II equipment patterns and synthetic signals developed by the authors.
One of the novelties of this work is the global strategy. Also, another significant innovation is the introduction of the curvature filters. We think this concept will prove to be a useful tool in signal processing, not only in ECG analysis.