Introduction: The analysis of electrocardiogram (ECG) signals allows the experts to diagnosis several cardiac disorders. However, the accuracy of such diagnostic depends on the signals quality. In this paper it is proposed a simple method for power-line interference (PLI) removal based on the wavelet decomposition, without the use of thresholding techniques. Methods: This method consists in identifying the ECG and noise frequency range for further zeroing wavelet detail coefficients in the subbands with no ECG coefficients in the frequency content. Afterward, the enhanced ECG signal is obtained by the inverse discrete wavelet transform (IDWT
Algorithms for blind source separation have been extensively studied in the last years. This paper proposes the use of multiresolution analysis in three decomposition levels of the wavelet transform, such as a preprocessing step, and the AMUSE algorithm to separate the source signals in distinct levels of resolution. Results show that there is an improvement in the estimation of the signals and in the mixing matrix even in noisy environment if compared to the use of AMUSE only.
Premature Ventricular Contraction is an arrhythmia that can be associated with several cardiac disorders that affect from 40% to 75% of the general population. Premature Ventricular Contraction occurrence is diagnosed from the Electrocardiogram. If in an Electrocardiogram one (or two) Premature Ventricular Contraction occurs between two Normal heartbeats, then there is a Ventricular Bigeminy (or Trigeminy). The prevalence of Ventricular Bigeminy/Trigeminy rhythms is associated with angina, hypertension, congestive heart failure and myocardial infarction. In this work it is proposed a new approach for these rhythms early diagnosis using Decision Tree models. The proposed approach uses the information before occurrence of Ventricular Bigeminy/Trigeminy, i.e., the number of normal and abnormal heartbeats and the heart rhythm. In order to rhythm prediction, the models obtained from Random Forest algorithm, induced by cross-validation approach, are used. Proposed approach predicted Ventricular Bigeminy/Trigeminy occurrence with accuracy, sensitivity and specificity of 98.94%, 96.28% and 99.83, respectively. Furthermore, the results showed that the Ventricular Bigeminy/Trigeminy is preceded for Normal, Atrioventricular Junctional and Paced heart rhythms in most of the examples. Besides that, it is presented a simple algorithm for decision about the occurrence of Ventricular Bigeminy/Trigeminy rhythms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.