The analysis of the surface Electrocardiogram (ECG) is the most extended noninvasive technique in cardi-ological diagnosis. In order to properly use the ECG, we need to cancel out ectopic beats. These beats may occur in both normal subjects and patients with heart disease, and their presence represents an important source of error which must be handled before any other analysis. This paper presents a method for electrocardiogram ectopic beat cancellation based on Radial Basis Function Neural Network (RBFNN). A train-able neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care is presented. Six types of beats including: Normal Beats (NB); Premature Ventricular Contractions (PVC); Left Bundle Branch Blocks (LBBB); Right Bundle Branch Blocks (RBBB); Paced Beats (PB) and Ectopic Beats (EB) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. Average Results for the RBFNN based method provided an ectopic beat reduction (EBR) of (mean ± std) EBR = 7, 23 ± 2.18 in contrast to traditional compared methods that, for the best case, yielded EBR = 4.05 ± 2.13. The results prove that RBFNN based methods are able to obtain a very accurate reduction of ectopic beats together with low distortion of the QRST complex.
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