Reconstruction of Missing Samples in Antepartum and Intrapartum FHR Measurements Via Mini-Batch-Based Minimized Sparse Dictionary Learning

Zhang, Yefei; Zhao, Zhidong; Deng, Yanjun; Zhang, Xiaohong

doi:10.1109/jbhi.2021.3093647

Cited by 8 publications

(2 citation statements)

References 26 publications

(46 reference statements)

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“…Additionally, the imbalance between positive and negative samples poses a challenge, requiring data augmentation to increase the number of pathological samples. To overcome the challenges mentioned above, we adopted the preprocessing and data augmentation methods previously proposed by our group [ 44 , 45 ] to enhance the original signal, and the denoised signal is shown in Fig. 6 .…”

Section: Resultsmentioning

confidence: 99%

Hybrid-FHR: a multi-modal AI approach for automated fetal acidosis diagnosis

Zhao,

Zhu,

Jiao

et al. 2024

BMC Med Inform Decis Mak

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Background In clinical medicine, fetal heart rate (FHR) monitoring using cardiotocography (CTG) is one of the most commonly used methods for assessing fetal acidosis. However, as the visual interpretation of CTG depends on the subjective judgment of the clinician, this has led to high inter-observer and intra-observer variability, making it necessary to introduce automated diagnostic techniques. Methods In this study, we propose a computer-aided diagnostic algorithm (Hybrid-FHR) for fetal acidosis to assist physicians in making objective decisions and taking timely interventions. Hybrid-FHR uses multi-modal features, including one-dimensional FHR signals and three types of expert features designed based on prior knowledge (morphological time domain, frequency domain, and nonlinear). To extract the spatiotemporal feature representation of one-dimensional FHR signals, we designed a multi-scale squeeze and excitation temporal convolutional network (SE-TCN) backbone model based on dilated causal convolution, which can effectively capture the long-term dependence of FHR signals by expanding the receptive field of each layer’s convolution kernel while maintaining a relatively small parameter size. In addition, we proposed a cross-modal feature fusion (CMFF) method that uses multi-head attention mechanisms to explore the relationships between different modalities, obtaining more informative feature representations and improving diagnostic accuracy. Results Our ablation experiments show that the Hybrid-FHR outperforms traditional previous methods, with average accuracy, specificity, sensitivity, precision, and F1 score of 96.8, 97.5, 96, 97.5, and 96.7%, respectively. Conclusions Our algorithm enables automated CTG analysis, assisting healthcare professionals in the early identification of fetal acidosis and the prompt implementation of interventions.

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Section: Resultsmentioning

confidence: 99%

Hybrid-FHR: a multi-modal AI approach for automated fetal acidosis diagnosis

Zhao,

Zhu,

Jiao

et al. 2024

BMC Med Inform Decis Mak

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“…The selected FHR signals were filled in using the mini-batch-based minimized sparse dictionary learning method ( Zhang Y. et al, 2022 ). Subsequently, 40 pathological samples were augmented using the category constraint-based Wasserstein GAN model with gradient penalty to generate 40 simulated pathological signals, totaling 80 pathological samples.…”

Section: Methodsmentioning

confidence: 99%

Multimodal learning for fetal distress diagnosis using a multimodal medical information fusion framework

Zhang

Deng²,

Zhou³

et al. 2022

Front. Physiol.

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Cardiotocography (CTG) monitoring is an important medical diagnostic tool for fetal well-being evaluation in late pregnancy. In this regard, intelligent CTG classification based on Fetal Heart Rate (FHR) signals is a challenging research area that can assist obstetricians in making clinical decisions, thereby improving the efficiency and accuracy of pregnancy management. Most existing methods focus on one specific modality, that is, they only detect one type of modality and inevitably have limitations such as incomplete or redundant source domain feature extraction, and poor repeatability. This study focuses on modeling multimodal learning for Fetal Distress Diagnosis (FDD); however, exists three major challenges: unaligned multimodalities; failure to learn and fuse the causality and inclusion between multimodal biomedical data; modality sensitivity, that is, difficulty in implementing a task in the absence of modalities. To address these three issues, we propose a Multimodal Medical Information Fusion framework named MMIF, where the Category Constrained-Parallel ViT model (CCPViT) was first proposed to explore multimodal learning tasks and address the misalignment between multimodalities. Based on CCPViT, a cross-attention-based image-text joint component is introduced to establish a Multimodal Representation Alignment Network model (MRAN), explore the deep-level interactive representation between cross-modal data, and assist multimodal learning. Furthermore, we designed a simple-structured FDD test model based on the highly modal alignment MMIF, realizing task delegation from multimodal model training (image and text) to unimodal pathological diagnosis (image). Extensive experiments, including model parameter sensitivity analysis, cross-modal alignment assessment, and pathological diagnostic accuracy evaluation, were conducted to show our models’ superior performance and effectiveness.

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