Fetal R-peak detection using a swin transformer network with dynamic encoding and parallel decoding

S R Breesha ¹, S S Vinsley ², M Nisha ¹

⁰Department of Electrical and Electronics Engineering, Arunachala Hitech Engineering College, Kanyakumari, Tamil Nadu 629165, India.

Journal of Electrocardiology +

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January 1, 2026

View abstract on PubMed

Summary

A novel deep learning network, DPSTFR-Net, accurately detects fetal heart rate (FHR) from abdominal ECG signals. This method enhances maternal and fetal monitoring systems with high precision and recall.

Area Of Science

Biomedical Engineering
Cardiovascular Technology
Artificial Intelligence in Healthcare

Background

Fetal heart rate (FHR) monitoring is crucial for assessing fetal well-being.
Accurate FHR detection from abdominal electrocardiogram (fECG) signals is challenging due to noise and interference.
Existing methods often struggle with signal quality and require complex preprocessing.

Purpose Of The Study

To develop an advanced deep learning model for robust FHR detection from abdominal ECG data.
To improve the accuracy and reliability of FHR estimation compared to existing techniques.
To create a foundation for enhanced maternal and fetal monitoring systems.

Main Methods

A dynamic encoding and parallel decoding Swin transformer-based network (DPSTFR-Net) was designed for FHR detection.
A cascaded Sparse Low-Rank and Kernel Recursive Least Squares (CSKL) filter was employed for signal preprocessing to remove noise.
Adaptive position encoding and parallel decoder contextual information were utilized to enhance classification accuracy.

Main Results

DPSTFR-Net achieved high performance on benchmark datasets (PCDB and ADFECGDB).
Accuracy reached up to 97.52%, precision up to 98.25%, and recall up to 97.35%.
The method demonstrated effective noise reduction and reliable FHR estimation from abdominal ECG.

Conclusions

The proposed DPSTFR-Net effectively estimates FHR from abdominal ECG signals, outperforming conventional methods.
The network's ability to handle noisy signals makes it suitable for real-world applications.
This technology holds potential for commercialization in long-term maternal and fetal monitoring systems.

Keywords:

Fetal heart rate estimation Kernel recursive least square filter Noise removal R-peak detection Swin transformer