RESEARCH ARTICLE


Electrocardiogram Fiducial Points Detection and Estimation Methodology for Automatic Diagnose



René Yáñez de la Rivera1, Moisés Soto-Bajo2, *, Andrés Fraguela-Collar1
1 Facultad de Ciencias Físico-Matemáticas, Benemérita Universidad Autónoma de Puebla, Avenida San Claudio y 18 Sur, Colonia San Manuel, 72570 Puebla, México
2 Cátedras CONACYT - Benemérita Universidad Autónoma de Puebla. Facultad de Ciencias Físico-Matemáticas, Benemérita Universidad Autónoma de Puebla, Avenida San Claudio y 18 Sur, Colonia San Manuel, 72570 Puebla, México


© 2018 Yáñez de la Rivera et al.

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.

* Address correspondence to the author at the Cátedras CONACYT - Benemérita Universidad Autónoma de Puebla. Facultad de Ciencias Físico-Matemáticas, Benemérita Universidad Autónoma de Puebla, Avenida San Claudio y 18 Sur, Colonia San Manuel, 72570 Puebla, México; E-mail: moises.soto@fcfm.buap.mx


Abstract

Background:

The estimation of fiducial points is specially important in the analysis and automatic diagnose of Electrocardiographic (ECG) signals.

Objective:

A new algorithm which could be easily implemented is presented to accomplish this task.

Methods:

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.

Results:

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.

Conclusion:

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.

Keywords: Automatic diagnose, Curvature filters, Electrocardiographic signal, Fiducial points, P wave, QRS complex, T wave, Signal processing.