Detecting anomalies in fetal electrocardiogram records using deep learning models

Abstract

In order to assess the fetus health and make timely decisions throughout pregnancy, Fetal Electrocardiography (FECG) monitoring is essential. Huge datasets for electrocardiograms are freely accessible from Physionet ATM Dataset1- Abdominal and Direct Fetal ECG Database (adfecgdb), Dataset2- Fetal ECG Synthetic Database (fecgsyndb), Dataset3- Non-Invasive Fetal ECG Database(nifecgdb). In this study, categorization is done based on normal and abnormal (Atrial fibrillation) FECG from three online dataset which contains FECG recordings as major details. Deep learning models like Transfer Learning (TL) and Convolutional Neural Networks (CNN) are being investigated. The composite abdominal signal and the FECG are separated using a wavelet transform approach. The best model for categorizing the parameters of the FECG is determined through a comparative analysis and performance is improved using Continuous Wavelet Transform (CWT). The accuracy of the CNN-based technique is found to be 98.59%, whereas the accuracy of the transfer learning model is 99.01% for FECG classification. The computation of metric parameters for all the datasets is done. The classification of normal and abnormal (Atrial fibrillation) is best performed in TL model compared to CNN. Real-time data analysis is done for PQRST plotting and comparative study is done using Net Reclassification Improvement (NRI) and obtained NRI = 13%, z static 0f 3.7641, p-Value of 0.00016721. Acute Myocardial Infraction (AMI) identification is done based on ST segment of Maternal ECG (MECG) images to analyze the heart attack risk. The proposed work can be utilized to track FECG waveforms in real-time for wearable technology because of its end-to-end properties and expandable intrinsic for diagnosing multi-lead heart disorders.