Optimized Fetal ECG Feature Extraction with Genetic Algorithm Based Heart Rate Detection

Congenital heart problems are the most common primary cause of death that can be attributed to birth defects. It is possible to identify cardiac defects by analyzing the geomorphology of cardiac electric indications. Additionally, the examination of fetal cardiac signals, which does not involve any intrusive procedures, can be an efficient method for monitoring the health of the fetal heart and identifying cardiac abnormalities at an early stage. One method that can be utilized to evaluate the health and condition of the fetus is the ECG signal, which is a graphical copy of the electric potential that is generated in conjunction with the action of the heart. However, there are several technical issues that arise during the process of non-invasive FECG extraction. These issues include interference from maternal electrocardiograms and maternal electromyograms, interference from power lines operating at 50 Hz, and baseline drift. Extraction and analysis of the fetal electrocardiogram signal are the key goals of electronic fetal monitoring; they are the primary objectives. In the process of extracting features from the fetal ECG indication, which is a nonlinear mix of the gentle ECG, the fetal ECG indication, and other interfering signals, digital signal processing techniques constitute a key part of the process. It is possible to find solutions to problems involving abnormality detection by employing data mining techniques and soft computing approaches for categorization.