Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy; Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy.
Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy.
Comput Methods Programs Biomed. 2023 Oct;240:107736. doi: 10.1016/j.cmpb.2023.107736. Epub 2023 Jul 30.
Computerized Cardiotocography (cCTG) allows to analyze the Fetal Heart Rate (FHR) objectively and thoroughly, providing valuable insights on fetal condition. A challenging but crucial task in this context is the automatic identification of fetal activity and quiet periods within the tracings. Different neural mechanisms are involved in the regulation of the fetal heart, depending on the behavioral states. Thereby, their correct identification has the potential to increase the interpretability and diagnostic capabilities of FHR quantitative analysis. Moreover, the most common pathologies in pregnancy have been associated with variations in the alternation between quiet and activity states.
We address the problem of fetal states clustering by means of an unsupervised approach, resorting to the use of a multivariate Hidden Markov Models (HMM) with discrete emissions. A fixed length sliding window is shifted on the CTG traces and a small set of features is extracted at each slide. After an encoding procedure, these features become the emissions of a multivariate HMM in which quiet and activity are the hidden states. After an unsupervised training procedure, the model is used to automatically segment signals.
The achieved results indicate that our developed model exhibits a high degree of reliability in identifying quiet and activity states within FHR signals. A set of 35 CTG signals belonging to different pregnancies were independently annotated by an expert gynecologist and segmented using the proposed HMM. To avoid any bias, the physician was blinded to the results provided by the algorithm. The overall agreement between the HMM's predictions and the clinician's interpretations was 90%.
The proposed method reliably identified fetal behavioral states, the alternance of which is an important factor in the fetal development. One key strength of our approach lies in the ease of interpreting the obtained results. By utilizing a small set of parameters that are already used in cCTG and possess clear intrinsic meanings, our method provides a high level of explainability. Another significant advantage of our approach is its fully unsupervised learning process. The states identified by our model using the Baum-Welch algorithm are associated with the "Active" and "Quiet" states only after the clustering process, removing the reliance on expert annotations. By autonomously identifying the clusters based solely on the intrinsic characteristics of the signal, our method achieves a more objective evaluation that overcomes the limitations of subjective interpretations. Indeed, we believe it could be integrated in cCTG systems to obtain a more complete signal analysis.
计算机化胎心监护（cCTG）允许客观、全面地分析胎儿心率（FHR），为胎儿状况提供有价值的信息。在这种情况下，一项具有挑战性但至关重要的任务是自动识别描记中的胎儿活动和安静期。不同的神经机制参与胎儿心脏的调节，这取决于行为状态。因此，正确识别它们有可能提高 FHR 定量分析的可解释性和诊断能力。此外，妊娠中最常见的病理与安静和活动状态之间的交替变化有关。
我们通过使用具有离散发射的多变量隐马尔可夫模型（HMM）的无监督方法来解决胎儿状态聚类的问题。固定长度的滑动窗口在 CTG 轨迹上移动，并在每个幻灯片上提取一小组特征。经过编码过程后，这些特征成为多变量 HMM 的发射，其中安静和活动是隐藏状态。在无监督训练过程之后，该模型用于自动分段信号。
所获得的结果表明，我们开发的模型在识别 FHR 信号中的安静和活动状态方面表现出高度的可靠性。一组属于不同妊娠的 35 个 CTG 信号由一位专家妇科医生独立注释，并使用提出的 HMM 进行分段。为了避免任何偏见，医生对算法提供的结果是盲目的。HMM 的预测和临床医生解释之间的总体一致性为 90%。
所提出的方法可靠地识别了胎儿行为状态，而这些状态的交替是胎儿发育的一个重要因素。我们方法的一个关键优势在于解释所获得结果的容易程度。通过利用已经在 cCTG 中使用并且具有明确内在意义的一小组参数，我们的方法提供了高度的可解释性。我们方法的另一个重要优势是其完全无监督的学习过程。使用 Baum-Welch 算法的模型识别的状态仅在聚类过程之后才与“活动”和“安静”状态相关联，从而消除了对专家注释的依赖。通过仅基于信号的内在特征自主识别聚类，我们的方法实现了更客观的评估，克服了主观解释的局限性。事实上，我们相信它可以集成到 cCTG 系统中，以获得更完整的信号分析。
