Multimodel fuzzy rule based pattern mining technique for fetal heart rate detection using wavelet analysis and genetic algorithm

Abstract

Congenital heart defects are the most common disabilities and the newlineleading cause of birth defect-related deaths. The morphology of cardiac newlineelectrical signals can be used to manifest cardiac defects. The non-invasive newlinestudy of fetal cardiac signals can provide an effective means of monitoring the newlinewell-being of the fetal heart, and this may be used for the early detection of newlinecardiac abnormalities. The electrocardiogram (ECG) signal is the graphical newlinerecording of the electrical potential generated in association with heart newlineactivity. It is one of the physiological signals commonly used in clinical newlineaspects. As in adults, the well-being and the status of the fetus can be assessed newlinefrom a fetal electrocardiogram (FECG) signal. newlineNon-invasive extraction of FECG has many technical problems. The newlineFECG signal is corrupted by different sources of interferences such as newlinematernal electrocardiogram (MECG), maternal electromyogram (MEMG), newline50 Hz power line interference, and baseline wander. The low amplitude of the newlinesignals, the different types of noise, and overlapping frequencies of mother newline