A multimodal deep learning-based algorithm for specific fetal heart rate events detection

Nov 1, 2024Biomedizinische Technik. Biomedical engineering

A deep learning method to detect specific events in fetal heart rate

AI simplified

Abstract

The algorithm achieved a classification accuracy of 96.2% for fetal heart rate acceleration events.

Classification accuracies for various fetal heart rate events were as follows: 94.4% for deceleration, 90.9% for tachycardia, and 85.8% for bradycardia.
The algorithm classified four distinct deceleration patterns with an overall accuracy of 67.0%.
It demonstrated 80.9% accuracy specifically for late deceleration and 98.9% for prolonged deceleration.
The approach combined multiple feature extraction techniques with deep learning algorithms to enhance monitoring.
The algorithm is designed to support clinicians in detecting and assessing fetal heart rate events.

AI simplified

OBJECTIVES: This study aims to develop a multimodal deep learning-based algorithm for detecting specific fetal heart rate (FHR) events, to enhance automatic monitoring and intelligent assessment of fetal well-being.

METHODS: We analyzed FHR and uterine contraction signals by combining various feature extraction techniques, including morphological features, heart rate variability features, and nonlinear domain features, with deep learning algorithms. This approach enabled us to classify four specific FHR events (bradycardia, tachycardia, acceleration, and deceleration) as well as four distinct deceleration patterns (early, late, variable, and prolonged deceleration). We proposed a multi-model deep neural network and a pre-fusion deep learning model to accurately classify the multimodal parameters derived from Cardiotocography signals.

RESULTS: These accuracy metrics were calculated based on expert-labeled data. The algorithm achieved a classification accuracy of 96.2 % for acceleration, 94.4 % for deceleration, 90.9 % for tachycardia, and 85.8 % for bradycardia. Additionally, it achieved 67.0 % accuracy in classifying the four distinct deceleration patterns, with 80.9 % accuracy for late deceleration and 98.9 % for prolonged deceleration.

CONCLUSIONS: The proposed multimodal deep learning algorithm serves as a reliable decision support tool for clinicians, significantly improving the detection and assessment of specific FHR events, which are crucial for fetal health monitoring.

Full Text

Full text is available at the source.

View full text (XML) ↗View full text ↗

Abstract

Full Text

Related papers

what lands in your inbox each week: