Balkan Journal of Electrical and Computer Engineering
Scalogram-Based Binary Fetal Arrhythmia Classification Using ML and DL
By Parisa Ebrahimpour Moghaddam Tasouj, Gökhan Soysal and Osman Eroğul.
Abstract
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
FECG signals were converted to time-frequency images using the Continuous Wave Transform (CWT), and these images were used as input to DL-based CNN architectures such as AlexNet, GoogleNet, and ResNet50. The effect of data length was investigated to achieve high accuracy with transfer learning. In addition, these DL models were compared with traditional machine learning methods.