PDF(Pubmed)
Abstract：
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.
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.
摘要：
背景：在临床医学中，使用心电图(CTG)监测胎儿心率(FHR)是评估胎儿酸中毒最常用的方法之一.然而,由于CTG的视觉解释取决于临床医生的主观判断，这导致了观察者间和观察者内的高度可变性，这使得有必要引入自动诊断技术。
方法：在本研究中，我们提出了一种针对胎儿酸中毒的计算机辅助诊断算法(Hybrid-FHR),以帮助医师做出客观决策并及时采取干预措施.混合动力FHR使用多模态特征，包括一维FHR信号和基于先验知识设计的三种类型的专家特征(形态学时域，频域,和非线性)。为了提取一维FHR信号的时空特征表示,设计了一种基于扩张因果卷积的多尺度挤压激励时间卷积网络(SE-TCN)骨干模型,通过扩展每层卷积核的感受场，同时保持相对较小的参数大小，可以有效地捕获FHR信号的长期依赖性。此外,我们提出了一种跨模态特征融合（CMFF）方法，该方法使用多头注意机制来探索不同模态之间的关系，获得更多的信息特征表示和提高诊断的准确性。
结果：我们的消融实验表明，混合FHR优于传统的先前方法，平均精度，特异性，灵敏度,精度,F1得分为96.8、97.5、96、97.5和96.7%，分别。
结论：我们的算法实现了自动CTG分析，协助医疗保健专业人员早期发现胎儿酸中毒并及时实施干预措施。
方法：在本研究中，我们提出了一种针对胎儿酸中毒的计算机辅助诊断算法(Hybrid-FHR),以帮助医师做出客观决策并及时采取干预措施.混合动力FHR使用多模态特征，包括一维FHR信号和基于先验知识设计的三种类型的专家特征(形态学时域，频域,和非线性)。为了提取一维FHR信号的时空特征表示,设计了一种基于扩张因果卷积的多尺度挤压激励时间卷积网络(SE-TCN)骨干模型,通过扩展每层卷积核的感受场，同时保持相对较小的参数大小，可以有效地捕获FHR信号的长期依赖性。此外,我们提出了一种跨模态特征融合（CMFF）方法，该方法使用多头注意机制来探索不同模态之间的关系，获得更多的信息特征表示和提高诊断的准确性。
结果：我们的消融实验表明，混合FHR优于传统的先前方法，平均精度，特异性，灵敏度,精度,F1得分为96.8、97.5、96、97.5和96.7%，分别。
结论：我们的算法实现了自动CTG分析，协助医疗保健专业人员早期发现胎儿酸中毒并及时实施干预措施。
