Multi-modal detection of fetal movements using a wearable monitor
Multi-modal detection of fetal movements using a wearable monitor
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DOI：
10.1016/j.inffus.2023.102124
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发表时间：
2023-11-11
影响因子：
18.6
通讯作者：
Vaidyanathan，Ravi
中科院分区：
文献类型：
--
作者：
Ghosh，Abhishek K.;Catelli，Danilo S.;Vaidyanathan，Ravi
胎动（FM）模式作为胎儿健康的生物标志物的重要性已在产科得到广泛的争论。然而，目前的FM监测方法，如超声检查，无法在临床环境之外使用，这使得了解FM的性质和演变具有挑战性。一小部分工作已经引入了基于传感器的可穿戴FM监视器来解决这一差距。尽管在受控环境中有希望，但用于监测临床外FM的可靠仪器仍然没有解决，特别是由于将FM与母体活动产生的干扰伪影分离的挑战。迄今为止，努力几乎完全集中在同质（单一）传感和信息融合模式，如去耦的声学或加速度计传感器。然而，FM和相关信号伪影具有均匀传感器阵列可能无法有效捕获或分离的变化的功率和频率带宽。在这项调查中，我们介绍了一种新型的可穿戴FM监视器与嵌入式异构传感器套件相结合的加速度计，声学传感器和压电膜片，旨在捕捉广泛的FM和干扰伪影信号功能，使两者更有效的隔离。我们进一步概述了一种新的数据融合架构，结合数据相关阈值和机器学习，自动检测FM，并将其与现实世界（家庭）环境中的信号伪影分离。通过同时记录母体对FM的感知，使用33小时的在家使用来验证设备和数据融合架构的性能。FM监视器检测到了令人印象深刻的82%的母亲感测FM，在识别FM和非FM事件方面的总体准确度为90%。从妊娠32周开始，检测的一致性最强，这与预防死产的关键FM监测窗口重叠。我们相信，在这项研究中提出的多模态传感器融合方法将是一个重要的里程碑，在低成本的可穿戴FM监视器的发展，使无处不在的监测FM在无人监督的环境。
The importance of Fetal Movement (FM) patterns as a biomarker for fetal health has been extensively argued in obstetrics. However, the inability of current FM monitoring methods, such as ultrasonography, to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. A small body of work has introduced wearable sensor-based FM monitors to address this gap. Despite promises in controlled environments, reliable instrumentation to monitor FM out-of-clinic remains unresolved, particularly due to the challenges of separating FMs from interfering artifacts arising from maternal activities. To date, efforts have been focused almost exclusively on homogenous (single) sensing and information fusion modalities, such as decoupled acoustic or accelerometer sensors. However, FM and related signal artifacts have varying power and frequency bandwidths that homogeneous sensor arrays may not capture or separate efficiently. In this investigation, we introduce a novel wearable FM monitor with an embedded heterogeneous sensor suite combining accelerometers, acoustic sensors, and piezoelectric diaphragms designed to capture a broad range of FM and interfering artifact signal features enabling more efficient isolation of both. We further outline a novel data fusion architecture combining data-dependent thresholding and machine learning to automatically detect FM and separate it from signal artifacts in real-world (home) environments. The performance of the device and the data fusion architecture are validated using 33 h of at-home use through concurrent recording of maternal perception of FM. The FM monitor detected an impressive 82 % of maternally sensed FMs with an overall accuracy of 90 % in recognizing FM and non-FM events. Consistency of detection was strongest from 32 gestational weeks onwards, which overlaps with the critical FM monitoring window for stillbirth prevention. We believe the multi-modal sensor fusion approach presented in this research will be a major milestone in the development of low-cost wearable FM monitors enabling pervasive monitoring of FM in unsupervised environments.