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Nowadays there exist different approaches to cancel out noise effect and baseline drift in biomedical signals. However, none of them can be considered as completely satisfactory. In this work an artificial neural network (ANN) based approach to cancel out baseline drift in electrocardiogram signals is presented. The system is based on a grown ANN allowing to optimize both the hidden layer number of nodes and the coefficient matrixes. These matrixes are optimized following the simultaneous perturbation algorithm, offering much lower computational cost that the traditional back propagation algorithm. The proposed methodology has been compared with traditional baseline reduction methods (FIR, Wavelet-based and Adaptive LMS filtering) making use of cross correlation, signal to interference ratio and signal to noise ratio indexes. Obtained results show that the ANN-based approach performs better, with respect to baseline drift reduction and signal distortion at filter output, than traditional methods. [ABSTRACT FROM AUTHOR]

In a substantial number of patients atrial fibrillation (AF) recurs after successful electrical cardioversion, but at present there are no reliable clinical markers for confidently identifying the patients in which recurrence will occur within a short period of time. This study evaluates the predictive classification performance of Sample Entropy (SampEn) in the discrimination between recurrent and non-recurrent AF episodes. A validated database of 35 ECG recordings acquired from AF subjects undergoing cardioversion was used throughout the study, together with their known recurrence status at one month. SampEn was applied to these QRST-reduced electrocardiograms, to atrial activity (AA), and also to heart rate (R-R intervals). The sample entropy of R-R intervals was significantly reduced (p=0.043) in the recurrent AF episodes compared with maintenance sinus rhythm episodes. SampEn applied to the AA signal showed a opposite results, it was reduced with a significant increasing trend in the maintenance sinus rhythm episodes (p=0.017). There is a need for welldefined studies with larger patient groups in order to assess the entropy changes further and to look for possible changes, which might predict AF recurrence. [ABSTRACT FROM AUTHOR]