Electrocardiogram Complexity Recognition and Modeling by Multilayer Perceptron
Ricardo Rodriguez, Adriana Mexicano, Jiri Bila, Rafael Ponce, Salvador Cervantes
Progress in Signals and Telecommunication Engineering, Vol. 2, No. 2, 2013.
DOI: 
Link: http://www.dline.info/pste/fulltext/v2n2/2.pdf
Abstract:
This paper presents a method for modeling and identification of electrocardiogram signals; the proposed method consists of two phases; the first one is focused on obtaining the period of an ECG signal using a procedure of autocorrelation. The second phase obtains R-peaks using the Hilbert transform. Finally, Multilayer Perceptron Neural Network using a retraining technique is applied for the prediction stage; this has been validated using twenty three different records from the MIT-BIH arrhythmia database. Results confirm that the presented approach for detection of the ECG complex obtains 99.9% accuracy. The performance of the prediction method is promising due to the root mean squared errors of the prediction are of 0.029, 0.04, and 0.059 of the ECG amplitude, for 1, 2, and 3 steps ahead, respectively, for the 100 record. Promising results for twenty three different records are also presented.