Accurate Fetal QRS-Complex Classification from Abdominal Electrocardiogram Using Deep Learning
Annisa Darmawahyuni, Bambang Tutuko, Siti Nurmaini, Muhammad Naufal Rachmatullah, Muhammad Ardiansyah, Firdaus Firdaus, Ade Iriani Sapitri, Anggun Islami
Abstract
Abstract Fetal heart monitoring during pregnancy plays a critical role in diagnosing congenital heart disease (CHD). A noninvasive fetal electrocardiogram (fECG) provides additional clinical information for fetal heart monitoring. To date, the analysis of noninvasive fECG is challenging due to the cancellation of maternal QRS-complexes, despite significant advances in electrocardiography. Fetal QRS-complex is highly considered to measure fetal heart rate to detect some fetal abnormalities such as arrhythmia. In this study, we proposed a deep learning (DL) framework that stacked a convolutional layer and bidirectional long short-term memory for fetal QRS-complexes classification. The fECG signals are first preprocessed using discrete wavelet transform (DWT) to remove the noise or inferences. The following step beats and QRS-complex segmentation. The last step is fetal QRS-complex classification based on DL. In the experiment of Physionet/Computing in Cardiology Challenge 2013, this study achieved 100% accuracy, sensitivity, specificity, precision, and F1-score. A stacked DL model demonstrates an effective tool for fetal QRS-complex classification and contributes to clinical applications for long-term maternal and fetal monitoring.