论文标题
深度序列学习从$ \ $ \ $$ 25多普勒设备进行准确的妊娠年龄估计
Deep Sequence Learning for Accurate Gestational Age Estimation from a $\$$25 Doppler Device
论文作者
论文摘要
评估胎儿发育通常是通过超声成像等技术进行的，超声成像通常由于有效操作设备所需的高成本，维护，技能和培训，因此在农村地区通常无法使用。在这项工作中，我们提出了一种基于低成本的一维多普勒方法来估计胎龄（GA）。使用智能手机从5至9个月的GA之间收集了401次怀孕的多普勒时间序列。 GA估计的建议模型基于序列学习，通过使用卷积长期记忆网络形成时间依赖的模型。从多普勒信号中提取时频功能，并在馈送到网络之前正规化。相对于最后一个月经期的总体平均绝对GA误差为0.71个月，其表现优于所有先前的作品。
Assessing fetal development is usually carried out by techniques such as ultrasound imaging, which is generally unavailable in rural areas due to the high cost, maintenance, skills and training needed to operate the devices effectively. In this work, we propose a low-cost one-dimensional Doppler-based method for estimating gestational age (GA). Doppler time series were collected from 401 pregnancies between 5 and 9 months GA using a smartphone. The proposed model for GA estimation is based on sequence learning by forming a temporally dependent model using a convolutional long-short-term memory network. Time-frequency features are extracted from Doppler signals and regularized before feeding to the network. The overall mean absolute GA error with respect to the last menstrual period was found to be 0.71 month, which outperforms all previous works.
