Sample Point Classification of Abdominal ECG Through CNN-Transformer Model Enables Efficient Fetal Heart Rate Detection

Monitoring fetal heart rate (FHR) is essential for the early detection of fetal distress and ensuring safe delivery. Direct invasive fetal electrocardiography (FECG) provides reliable FHR signals but is limited to use during labor. Noninvasive fetal heart monitoring can be achieved through abdominal electrocardiography (AECG), where fetal heartbeat waveforms are captured from electrodes placed on maternal abdomen. However, accurately locating fetal R-peaks and obtaining FHRs remain challenging due to interference from uterine contractions and maternal heartbeats. To address this challenge, we proposed an end-to-end fetal R-peak detection method based on sample point classification. Inspired by semantic segmentation in image analysis, we utilized an encoder-decoder model to classify each point in the 1-D AECG signal. The model captures the global contextual information of fetal heartbeat waveforms by combining convolutional neural networks (CNNs) and transformer, resulting in a high-precision classification of each sample point in the AECG signal. Moreover, to mitigate the impact of misclassified points on fetal R-peak detection, we proposed a postprocessing method based on the periodicity of heartbeats. The effectiveness of the proposed method is validated on two AECG databases, achieving satisfactory results with an average F1-score of 98.91%. These findings demonstrate the potential application of long-term fetal monitoring using portable devices. The code is publicly available at https://github.com/ZJUT-CBS/FR-Net.

I. Introduction

Stillbirth refers to the death of a fetus after 24 weeks of gestation, affecting around 2.5 million infants worldwide each year [1]. Fetal heart rate (FHR) is a crucial indicator used to assess fetal well-being and monitor fetal condition during pregnancy. Accurate assessment of FHR is of paramount importance in the timely detection of hypoxia, intrauterine distress, and other fetal conditions [2]. Traditionally, ultrasonic cardiotocography (CTG) has been the primary technique employed in clinical practice to obtain FHR. However, CTG provides only time-averaged FHR and lacks detailed information on heart rate variability [3]. Moreover, the absolute safety of the fetus cannot be guaranteed in ultrasound radio frequency exposure [4].