Scalogram-Based Binary Fetal Arrhythmia Classification Using ML and DL

Objectives

Fetal electrocardiography (FECG) is a valuable tool for assessing fetal health and identifying cardiac rhythm abnormalities. This study aimed to determine the convolutional neural network (CNN)-based deep learning (DL) model that provides the highest performance for binary classification of fetal arrhythmia and normal rhythm (ARR vs NR) in FECG signals.

Methods