PDF(Pubmed)
Abstract：
UNASSIGNED: Fetal heart rate monitoring during labor can aid healthcare professionals in identifying alterations in the heart rate pattern. However, discrepancies in guidelines and obstetrician expertise present challenges in interpreting fetal heart rate, including failure to acknowledge findings or misinterpretation. Artificial intelligence has the potential to support obstetricians in diagnosing abnormal fetal heart rates.
UNASSIGNED: Employ preprocessing techniques to mitigate the effects of missing signals and artifacts on the model, utilize data augmentation methods to address data imbalance. Introduce a multi-scale long short-term memory neural network trained with a variety of time-scale data for automatically classifying fetal heart rate. Carried out experimental on both single and multi-scale models.
UNASSIGNED: The results indicate that multi-scale LSTM models outperform regular LSTM models in various performance metrics. Specifically, in the single models tested, the model with a sampling rate of 10 exhibited the highest classification accuracy. The model achieves an accuracy of 85.73%, a specificity of 85.32%, and a precision of 85.53% on CTU-UHB dataset. Furthermore, the area under the receiver operating curve of 0.918 suggests that our model demonstrates a high level of credibility.
UNASSIGNED: Compared to previous research, our methodology exhibits superior performance across various evaluation metrics. By incorporating alternative sampling rates into the model, we observed improvements in all performance indicators, including ACC (85.73% vs. 83.28%), SP (85.32% vs. 82.47%), PR (85.53% vs. 82.84%), recall (86.13% vs. 84.09%), F1-score (85.79% vs. 83.42%), and AUC(0.9180 vs. 0.8667). The limitations of this research include the limited consideration of pregnant women\'s clinical characteristics and disregard the potential impact of varying gestational weeks.
UNASSIGNED: Employ preprocessing techniques to mitigate the effects of missing signals and artifacts on the model, utilize data augmentation methods to address data imbalance. Introduce a multi-scale long short-term memory neural network trained with a variety of time-scale data for automatically classifying fetal heart rate. Carried out experimental on both single and multi-scale models.
UNASSIGNED: The results indicate that multi-scale LSTM models outperform regular LSTM models in various performance metrics. Specifically, in the single models tested, the model with a sampling rate of 10 exhibited the highest classification accuracy. The model achieves an accuracy of 85.73%, a specificity of 85.32%, and a precision of 85.53% on CTU-UHB dataset. Furthermore, the area under the receiver operating curve of 0.918 suggests that our model demonstrates a high level of credibility.
UNASSIGNED: Compared to previous research, our methodology exhibits superior performance across various evaluation metrics. By incorporating alternative sampling rates into the model, we observed improvements in all performance indicators, including ACC (85.73% vs. 83.28%), SP (85.32% vs. 82.47%), PR (85.53% vs. 82.84%), recall (86.13% vs. 84.09%), F1-score (85.79% vs. 83.42%), and AUC(0.9180 vs. 0.8667). The limitations of this research include the limited consideration of pregnant women\'s clinical characteristics and disregard the potential impact of varying gestational weeks.
摘要：
分娩期间的胎儿心率监测可以帮助医疗保健专业人员识别心率模式的变化。然而,指南和产科医生专业知识的差异在解释胎儿心率方面提出了挑战，包括未能承认调查结果或误解。人工智能有可能支持产科医生诊断胎儿心率异常。
■采用预处理技术来减轻丢失信号和伪影对模型的影响，利用数据增强方法来解决数据不平衡问题。介绍一种用各种时间尺度数据训练的多尺度长短期记忆神经网络，用于自动对胎儿心率进行分类。在单尺度和多尺度模型上进行了实验。
■结果表明，多尺度LSTM模型在各种性能度量方面优于常规LSTM模型。具体来说,在测试的单个模型中，采样率为10的模型显示出最高的分类精度。该模型的准确率达到85.73%，特异性为85.32%，CTU-UHB数据集上的精度为85.53%。此外，0.918的接受者工作曲线下面积表明我们的模型具有较高的可信度.
■与以前的研究相比，我们的方法在各种评估指标中表现出卓越的性能。通过将替代采样率纳入模型，我们观察到所有绩效指标的改善，包括ACC（85.73%与83.28%），SP（85.32%与82.47%），PR（85.53%与82.84%），召回（86.13%与84.09%)，F1得分（85.79％vs.83.42%)，和AUC（0.9180vs.0.8667）。这项研究的局限性包括对孕妇临床特征的考虑有限，以及忽略不同孕周的潜在影响。
■采用预处理技术来减轻丢失信号和伪影对模型的影响，利用数据增强方法来解决数据不平衡问题。介绍一种用各种时间尺度数据训练的多尺度长短期记忆神经网络，用于自动对胎儿心率进行分类。在单尺度和多尺度模型上进行了实验。
■结果表明，多尺度LSTM模型在各种性能度量方面优于常规LSTM模型。具体来说,在测试的单个模型中，采样率为10的模型显示出最高的分类精度。该模型的准确率达到85.73%，特异性为85.32%，CTU-UHB数据集上的精度为85.53%。此外，0.918的接受者工作曲线下面积表明我们的模型具有较高的可信度.
■与以前的研究相比，我们的方法在各种评估指标中表现出卓越的性能。通过将替代采样率纳入模型，我们观察到所有绩效指标的改善，包括ACC（85.73%与83.28%），SP（85.32%与82.47%），PR（85.53%与82.84%），召回（86.13%与84.09%)，F1得分（85.79％vs.83.42%)，和AUC（0.9180vs.0.8667）。这项研究的局限性包括对孕妇临床特征的考虑有限，以及忽略不同孕周的潜在影响。
