A Fourier attention network for deceleration classification

Zhijiang Zeng; Huijin Wang; Wei Jiang; Yanyan Luo

doi:10.1117/12.3042032

11 September 2024 A Fourier attention network for deceleration classification

Zhijiang Zeng, Huijin Wang, Wei Jiang, Yanyan Luo

Proceedings Volume 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024); 132530O (2024) https://doi.org/10.1117/12.3042032
Event: 4th International Conference on Signal Image Processing and Communication, 2024, Xi'an, China

Abstract

The objective of this study is to enhance the precision of automatically categorizing fetal heart rate (FHR) deceleration using a novel approach. The proposed Fourier transform-based network integrates SGE (Spatial Group-wise Enhance) and Fca (Fully Connected Attention) modules. This combination leads to a substantial enhancement in the accuracy of deceleration event classification. This method is designed to assist clinicians in evaluating the oxygenation status of the fetus. The model's performance evaluation indicates an accuracy of 88.52%, a Matthews correlation coefficient of 83.85%, a recall rate of 84.03%, a precision of 78.74%, and an F1 score of 80.84%. These findings confirm the efficacy of this approach in automated deceleration classification, particularly in addressing the intricate correlation between FHR decelerations and uterine contractions. This approach also demonstrates its capacity to diminish the subjective errors and burden of clinicians.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhijiang Zeng, Huijin Wang, Wei Jiang, and Yanyan Luo "A Fourier attention network for deceleration classification", Proc. SPIE 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024), 132530O (11 September 2024); https://doi.org/10.1117/12.3042032

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