Electrocardiogram Fiducial Points Detection and Estimation Methodology for Automatic Diagnose

The Open Bioinformatics Journal 28 Sept 2018 RESEARCH ARTICLE DOI: 10.2174/1875036201811010208



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.

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