Vican Ivan, Kreković Gordan, Jambrošić Kristian
University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, Croatia.
Visage Technologies, Ivana Lučića 2A, 10000 Zagreb, Croatia.
Comput Methods Programs Biomed. 2021 May;203:106038. doi: 10.1016/j.cmpb.2021.106038. Epub 2021 Mar 10.
A fetal phonocardiography signal can be hard to interpret and classify due to various sources of additive noise in the womb, spanning from fetal movement to maternal heart sounds. Nevertheless, the non-invasive nature of the method makes it potentially suitable for long-term monitoring of fetal health, especially since it can be implemented on ubiquitous devices such as smartphones. We have employed empirical mode decomposition for the extraction of intrinsic mode functions that would enable the utilization of additional characteristics from the signal.
Fetal heart recordings from 7 pregnant women in the 3rd trimester or pregnancy were taken in parallel with a measurement microphone and a portable Doppler device. Signal peaks positions from the Doppler were taken as the locations of S1 heart sounds and subsequently used as classification labels for the microphone signal. After employing a moving window approach for segmentation, more than 7600 observations were stored in the final dataset. The 135 extracted features consisted of typical audio temporal and spectral characteristics, each taken from separate sets of audio signals and intrinsic mode functions. We have used a number of metrics and methods to validate the usability of features, including univariate analysis of feature ranking and importance. Furthermore, we have used machine learning to train a number of classifiers to validate the usability of features based on intrinsic mode functions, taking prediction accuracy as the comparison metric.
Features extracted from intrinsic mode functions combined with audio features significantly improve accuracy in comparison to using only audio features. The improvements of detection accuracy obtained with a selected set of combined features spanned from 3.8% to even 10.3% based on the employed classifier.
We have utilized empirical mode decomposition as a method of extracting features relevant for fetal heartbeat classification. The results show consistent improvements in detection accuracy when these characteristics are added to a set of conventional audio features. This implies substantial benefits of applying empirical mode decomposition and lays the groundwork for future research on fetal heartbeat detection.
由于子宫内存在多种加性噪声源，从胎儿运动到母体心音，胎儿心音图信号可能难以解释和分类。然而，该方法的非侵入性使其有可能适用于胎儿健康的长期监测，特别是因为它可以在智能手机等普及设备上实现。我们采用经验模态分解来提取本征模态函数，以便能够利用信号的其他特征。
在妊娠晚期对7名孕妇进行胎儿心脏记录，同时使用测量麦克风和便携式多普勒设备。将多普勒信号的峰值位置作为S1心音的位置，随后用作麦克风信号的分类标签。采用移动窗口方法进行分割后，最终数据集中存储了7600多个观测值。提取的135个特征包括典型的音频时域和频域特征，每个特征分别取自不同的音频信号集和本征模态函数。我们使用了多种指标和方法来验证特征的可用性，包括特征排名和重要性的单变量分析。此外，我们使用机器学习训练了多个分类器，以基于本征模态函数验证特征的可用性，以预测准确率作为比较指标。
与仅使用音频特征相比，从本征模态函数中提取的特征与音频特征相结合可显著提高准确率。基于所采用的分类器，一组选定的组合特征所获得的检测准确率提高了3.8%至10.3%。
我们利用经验模态分解作为提取与胎儿心跳分类相关特征的方法。结果表明，当将这些特征添加到一组传统音频特征中时，检测准确率持续提高。这意味着应用经验模态分解有很大益处，并为未来胎儿心跳检测的研究奠定了基础。
