Cooperative Feature Classification Technique through Ensemble Factor Matching using Deep Learning

Fetal Arrhythmia is an abnormality of cardiac function that causes the heartbeat to be slow, fast, or both. This abnormality results in premature atrial or ventricular contractions that demand transplacental medications such as flecainide at high dosages. To detect this syndrome/disease, this article introduces a Cooperative Feature Classification Technique (CFCT) based on Deep Ensemble Learning (DEL). The process begins with nonlinear cardiogram feature extraction, followed by classification of the extracted features using predefined time intervals. Both steps are performed in sequence, with ensemble factor detection occurring between them. The ensemble factor, in this context, refers to a statistical measure that summarizes the consensus output or agreement among multiple models in the ensemble learning process. If the ensemble factor detected in both steps matches, the dysfunction is classified as an abnormality and verified using existing clinical data.If the ensemble factors differ between the feature extraction and classification steps, the classification of extracted features proceeds without interrupting the extraction process. This approach ensures that the nonlinearity of the cardiogram signals is preserved. The entire process is then repeatedly checked for uniqueness or similarity using the second hidden layer of the deep learning paradigm, continuing until a maximum value is reached. This organized sequence enhances classification precision by reducing errors.