Department of Electronic Engineering, Guangxi Normal University, Guilin, China.
Department of Artificial Intelligence and Manufacturing, Hechi University, Hechi, China.
J Healthc Eng. 2021 Aug 25;2021:1745292. doi: 10.1155/2021/1745292. eCollection 2021.
Fetal movement (FM) is an essential physiological parameter to determine the health status of the fetus. To address the problems of harrowing FM signal extraction and the low recognition rate of traditional machine learning classifiers in FM signal detection, this paper develops a passive FM signal detection system based on intelligent sensing technology. FM signals are obtained from the abdomen of the pregnant woman by using accelerometers. The FM signals are extracted and identified according to the clinical nature of the features hidden in the amplitude and waveform of the FM signals that fluctuate in duration. The system consists of four main stages: (i) FM signal preprocessing, (ii) maternal artifact signal preidentification, (iii) FM signal identification, and (iv) FM classification. Firstly, Kalman filtering is used to reconstruct the FM signal in a continuous low-amplitude noise background. Secondly, the maternal artifact signal is identified using an amplitude threshold algorithm. Then, an innovative dictionary learning algorithm is used to construct a dictionary of FM features, and orthogonal matching pursuit and adaptive filtering algorithms are used to identify the FM signals, respectively. Finally, mask fusion classification is performed based on the multiaxis recognition results. Experiments are conducted to evaluate the performance of the proposed FM detection system using publicly available and self-built accelerated FM datasets. The classification results showed that the orthogonal matching pursuit algorithm was more effective than the adaptive filtering algorithm in identifying FM signals, with a positive prediction value of 89.74%. The proposed FM detection system has great potential and promise for wearable FM health monitoring.
胎儿运动（FM）是确定胎儿健康状况的重要生理参数。为了解决 FM 信号提取困难和传统机器学习分类器在 FM 信号检测中识别率低的问题，本文开发了一种基于智能传感技术的被动 FM 信号检测系统。FM 信号通过加速度计从孕妇腹部获取。FM 信号根据 FM 信号幅度和波形中隐藏的特征的临床性质进行提取和识别，这些特征随时间波动。该系统由四个主要阶段组成：（i）FM 信号预处理，（ii）母体伪迹信号预识别，（iii）FM 信号识别，（iv）FM 分类。首先，使用卡尔曼滤波在连续的低幅度噪声背景下重建 FM 信号。其次，使用幅度阈值算法识别母体伪迹信号。然后，使用创新的字典学习算法构建 FM 特征字典，并使用正交匹配追踪和自适应滤波算法分别识别 FM 信号。最后，基于多轴识别结果进行掩模融合分类。使用公开可用的和自建的加速 FM 数据集评估所提出的 FM 检测系统的性能。实验结果表明，在识别 FM 信号方面，正交匹配追踪算法比自适应滤波算法更有效，正预测值为 89.74%。所提出的 FM 检测系统在可穿戴 FM 健康监测方面具有很大的潜力和应用前景。
