目的:早产是全球新生儿和婴儿疾病负担的重要因素。宫腔电描记术(EHG)已成为预测这种情况的一种有前途的技术,由于其高度的灵敏度。尽管在预测早产方面取得了技术进步,它在临床实践中的使用仍然有限,主要障碍之一是缺乏没有专家监督的自动信号处理工具,即自动筛查EHG记录中的运动和呼吸伪影。因此,我们的主要目标是设计和验证一个自动系统,用于在EHG记录中分割和筛选子宫起源的生理部分,以实现对子宫肌电活动的强大表征。预测早产,并有助于促进EHG技术向临床实践的可转移性。
方法:为此,我们合并了TPEHGDS数据库中的300份EHG记录和我们自己的数据库(Ci2B-LaFe)中的69份单胎妊娠女性EHG记录.该数据集用于训练和评估U-Net,U-Net++,和U-Net3+用于EHG信号的生理和伪像段的语义分割。然后通过后处理对模型的预测进行微调。
结果:U-Net3+优于其他型号,ROC曲线下面积为91.4%,生理活动检测平均精密度为96.4%。从0.6到0.8的阈值实现了从93.7%到97.4%的精度和从81.7%到94.5%的特异性,检测高质量的生理段,同时保持召回和特异性之间的权衡。后处理通过微调生理段和损坏段,提高了模型的适应性,确保准确的伪影检测,同时保持EHG信号的生理段完整性。
结论:由于自动分割在预测早产方面与双盲手动分割一样有效,该自动分割工具通过消除专家对双盲分割的需求,填补了现有早产预测系统工作流程中的关键空白,并促进了EHG的实际临床使用.这项工作可能有助于早期发现真正的早产妇女,并将使临床医生能够为孕产妇健康监测系统设计个体患者策略并预测不良妊娠结局。
OBJECTIVE: Preterm delivery is an important factor in the disease burden of the newborn and infants worldwide. Electrohysterography (EHG) has become a promising technique for predicting this condition, thanks to its high degree of sensitivity. Despite the technological progress made in predicting preterm labor, its use in clinical practice is still limited, one of the main barriers being the lack of tools for automatic signal processing without expert supervision, i.e. automatic screening of motion and respiratory artifacts in EHG records. Our main objective was thus to design and validate an automatic system of segmenting and screening the physiological segments of uterine origin in EHG records for robust characterization of uterine myoelectric activity, predicting preterm labor and help to promote the transferability of the EHG technique to clinical practice.
METHODS: For this, we combined 300 EHG recordings from the TPEHG DS database and 69 EHG recordings from our own database (Ci2B-La Fe) of women with singleton gestations. This dataset was used to train and evaluate U-Net, U-Net++, and U-Net 3+ for semantic segmentation of the physiological and artifacted segments of EHG signals. The model\'s predictions were then fine-tuned by post-processing.
RESULTS: U-Net 3+ outperformed the other models, achieving an area under the ROC curve of 91.4 % and an average precision of 96.4 % in detecting physiological activity. Thresholds from 0.6 to 0.8 achieved precision from 93.7 % to 97.4 % and specificity from 81.7 % to 94.5 %, detecting high-quality physiological segments while maintaining a trade-off between recall and specificity. Post-processing improved the model\'s adaptability by fine-tuning both the physiological and corrupted segments, ensuring accurate artifact detection while maintaining physiological segment integrity in EHG signals.
CONCLUSIONS: As automatic segmentation proved to be as effective as double-blind manual segmentation in predicting preterm labor, this automatic segmentation tool fills a crucial gap in the existing preterm delivery prediction system workflow by eliminating the need for double-blind segmentation by experts and facilitates the practical clinical use of EHG. This work potentially contributes to the early detection of authentic preterm labor women and will allow clinicians to design individual patient strategies for maternal health surveillance systems and predict adverse pregnancy outcomes.